Galen T. Trail

Introduction to Sport Consumer Behavior

Galen T. Trail Copyright 2018 Galen Trail

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Section I Chapter 1 Introduction to Sport Consumer Behavior Pervasiveness/significance of sport consumer behavior In 2016, Rich Luker, the founder of ESPN’s sports poll, estimated that approximately 88% of Americans have at least some interest in sport. That means that there are at least 200 million sport fans in the United States today. Others however, have considerably lower estimates, and the differences probably lie in how “sport fans” are defined. Luker defined sport

fans as anyone who had at least “some interest” in sport. However, a Gallup poll from 2016 indicates that only about 60% of U.S. adults self-identify as a sports fan. Realistically, if there are similar levels of interest in other countries as well, that would translate to there being billions of sport fans throughout the world. However, is that a realistic supposition? Are there are similar numbers of sport fans throughout the world? If we look at the information from Nielsen Sports for football (soccer) across countries, we can see that on average, 46% of the global population said that they are interested in football and 20% play the sport (see figure below). Interest levels vary dramatically from country to country though, with 83% of those in Nigeria indicating interest, but only 27% in the U.S. That said, if 46% of the world population likes football, then when all of the other sports are added into the mix of sport fandom, 60+% would be easy to believe.

Copyright 2018 Galen Trail

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Interest in sport can be shown by a variety of behaviors. People attend sport events, watch sports through some mediated form, listen to sport on the radio, read about sport through print media or on the internet, purchase sport merchandise, and consume concessions, among many other sport related consumption behaviors. All of these consumption behaviors are part of the sport industry, which also includes much more, including the value and cost of running sport organizations. In 2016, Plunkett Research, Ltd. estimated that the U.S. sports industry was worth US$496 billion, and the global sports industry was valued at US$1.3 trillion. This is a substantial increase since 2010, when the SportsBusiness Journal noted that the sports Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior business industry is one of the largest and fastest growing industries in the United States and estimated it was approximately $213 billion in 2009, which was twice the size of the U.S. auto industry and seven times the size of the movie industry at that time. Back then, approximately 15% of that $213 billion, or around $32 billion, was spent by spectators on teams in the U.S. However, there may be indications that the market is slowing. In 2017, Statista estimated that the total revenue in the global sports market would be around US$91 billion (see figure). Note that revenue is substantially different from the estimate of sport industry market value. Revenue is how much money the market is bringing in, whereas market value is the amount that all of the entities in the industry would be worth if they were sold. However, the accuracy of Statista’s 2017 estimate might be questioned especially since they show that there was an actual decrease in revenue generated between 2012 and 2013. Additional information that might cause people in the sport industry a bit of concern is that according to ATKearney, although the global sports-market revenue will continue to grow, the percentage that it is growing by, is slowing. From the chart to the right, it is evident that growth is slowing worldwide, in U.S. sports overall, and in football (soccer) and tennis especially. Specifically, revenue growth was 7% on average from 2009 to 2013, however, ATKearney estimates that the growth will only Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior be 5% from 2013 to 2017. We will have to wait until the end of 2018 to see if they are correct, but this is additional information that growth is slowing. Furthermore, Rich Luker, in 2016, suggested that fan support in the U.S. has softened. He noted that there was a 6% decline from 2014 to 2015 in the number of Americans who spent money on sports at least once a month. He also noted that the number of “avid” sport fans declined by 2% and “those who placed a high priority on time and investment in sports interests” dropped 4 percent. Similarly, In 2015, 4 percent more Americans said they were less interested in sports than they were compared with the 2011-14 average (Luker, 2016). Furthermore, these declines all came despite people indicating that they had more discretionary income (which they certainly could choose to spend on sport). In addition, the stock market had been increasing dramatically and the consumer sentiment index had been climbing substantially over the same period (the consumer sentiment index measures how confident people feel about spending money on consumer goods). Both of these economic indicators typically bode well for people spending money on sport as entertainment. So, despite the pervasiveness of spectator sport and its consumption, there is evidence that the cycle of growth may be slowing. Recently though, Luker has suggested that this slowing is because younger cohorts are starting off less interested in sports than previous cohorts. In the graph below, Luker shows that about 43% of the people in the 2000-2004 cohort (those that are 13-17-years old in 2017) are interested in sport, which is down from the slightly more than 45% of the 1995-1999 cohort. There is an even greater decrease from the 1980-1984 cohort, in which approximately 49% started off as sport fans. Luker also noted that fandom typically decreases throughout the life-span, however, the decrease seems to be getting steeper (i.e., people are losing their interest in sport faster than previous cohorts). In addition, compared to the early cohorts of Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior 1970’s and 1960’s there doesn’t seem to be a plateauing at the older ages. Those in the late 1980’s and early 1990’s cohorts are substantially less interested in sport than the early cohorts when you look at 2017.

Luker argues that some of this lack of interest in sport is because 40% of all 12-17 yearolds spend most of their time online, thus they do not spend time playing sports, going to games with their families, watching games with their friends, etc. If the youth of today is not socialized into sport fandom at a young age (we’ll talk about socialization in a later chapter), then they are considerably less likely to be become sport fans at a later stage of their life. Although some argue that today’s kids are connecting to sport online (e.g., streaming games, watching sport highlights, or playing sport games online, which we’ll discuss more in depth later in the chapter), Luker suggests that this is not the case because the primary reason they are online is not sport at all, it is primarily to connect with friends. Sport is far down the list of what kids and others are doing on their mobile devices. So, it isn’t solely that fans are spending more time online that is causing the problem, it is more likely that because there are so many more things that an individual can do online than ever before, the sport fan may be pulled in a wide variety of different ways and thus is not spending as much time on sport. Although parents may take kids to a game or two, apparently that is not sufficient to develop an interest in sport. Unfortunately, the aforementioned data does not show us one way or the other, but it does show us that spending is down. Or at least based on how these studies have measured spending, it is down. Then the question is whether or not other evidence also suggests that sport consumption that generates revenues is down. To determine this, it is necessary to examine attendance behavior more closely. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Attendance As noted above, attendance at sporting events is one of the ways that fandom has typically been measured. That is, most people expect that if attendance is up, then obviously more sport fans are going to games. That isn’t necessarily so because non-fans can attend games, but in general it certainly is a good measure of fan interest. The NFL (National Football League) in the U.S. has led per game average attendance records for quite some time when looking at professional sports across the globe. Two professional football (soccer) leagues in Europe, Bundesliga in Germany, and the EPL in the U.K., are in second and third place, followed by the AFL (an Australian rules football league). However, when total season attendance is compared across the 2014-15 season, the rankings change dramatically with the MLB (a North American baseball league) leading the way with more than triple the number of attendees of any other league. Primarily this is due to the large number of games in the MLB season (162). Although these numbers give some perspective by comparing leagues globally, these graphs don’t show trends. Based on information from ESPN.com, attendance across the professional leagues in North America has fluctuated over the last decade, with the largest fluctuations taking place in Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior strike or lockout years. Several leagues have set attendance records in the last decade, but in general, attendance seems to have plateaued or even decreased slightly since those records were set for the “Big 4” leagues: National Basketball Association (NBA), National Hockey League (NHL), National Football League (NFL) and Major League Baseball (MLB).

In the 2017-2018 season, the NBA was slightly off their record attendance mark (22.0M) set the prior year with 21.93 million attendees at approximately 92% of arena capacity across the league. However, this was only a 3.3% increase from the 2004-2005 season, or less than a .3% per year increase. Attendance numbers are similar for the NHL. In the 2017-2018 season, the NHL set a record with 22.17 million attendees, slightly over 90% of arena capacity. Attendance for the NHL over the last 10 years has only increased a total of 4.1%, or on average about 41 hundredths of a percent each year. For the NFL, attendance is down from the highs of the mid 1990’s, with slightly over 17 million attendees during the 2017 season. This is down 1.4% from the 2015 season, but up slightly from the 2016 season. However, the league as a whole is still averaging above 98% capacity in its stadiums (in 2017). The drop in attendance from about two decades ago can partially be attributed to a reduction in the size of some of stadiums, which translates to a reduction in the number of available seats. The recent decrease in attendance though has been attributed to a variety of things (e.g., politics, improved media experiences at home, increased costs, etc.), none of which have been empirically established yet. Major League Baseball (MLB) has a lot of room for growth in terms of capacity (the league average was 67% of capacity in 2017). The league did set a record for overall attendance Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior with 79.49 million fans in 2007, but has decreased in attendance since then, losing more than 8.6% over the last decade. As is evident from the MLB line in the attendance graph, attendance took an uptick in 2012, but seems to have resumed its downward trend since. In general, indicators of attendance across the leagues showed that up until 2007 the “Big 4” in North America were doing fairly well. However, the economic downturn in 2008 had an effect that seemingly is lingering. League commissioners for both the NBA and NHL have indicated in the past that league goals are to be at 95% capacity, which would need to see an increase in attendance by about 2-3 million in each league. The NFL needs to recover their lost fans. MLB has substantial room for growth in stadium attendance and is losing fans year over year as well. Obviously, something is wrong and seems to be getting worse. The WNBA (Women’s National Basketball Association) in North America has been in existence since 1997 and attendance WNBA Attendance peaked in 1998 at Avg. per Game 10,896 per game. In 13,750 seemed to have bottomed out in 11,000 2012, as attendance improved slightly 8,250 WNBA over the next two years, but in 2015, 5,500 the WNBA set a new low for average 2,750 attendance. 0 Attendance did 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 improve in 2016 and then again in 2017, showing the best average attendance since 2011. However, this is still a 29% decrease over the heyday in 1998. Many questions remain as to whether the WNBA will be a viable product in the near future. Unfortunately, though, there are only 12 teams presently in the WNBA, down from 16. Three of them claim to make a profit, but we haven’t seen any numbers to back this up. Even back in 2001, Van Chancellor, who was the coach of the Houston Comets and had led them to four consecutive WNBA Finals, noted that "The number one thing we're going to have to do is put a product out there that people want to watch." Regrettably, the WNBA may be facing dire times in the near future unless they find a way to understand their consumer better and in doing so put out a product that people want to watch, which will hopefully increase the number of fans at the games. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Major League Soccer (MLS) has been in existence one year longer than the WNBA. The MLS started out attracting a respectable number of fans, but unlike the WNBA, immediately

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had a major decline as the novelty effect wore off and the average attendance dropped almost 20%. Although attendance has fluctuated a fair amount since 1997, the general trend has been upwards with a new average attendance record being set in 2017 with over 22,106 per match, led by the expansion Atlanta United FC with 48,200, which terminated the Seattle Sounders run of six years in a row for leading attendance average. Unfortunately, many of the other teams do not draw well and have not historically (e.g., Dallas Burn with 15,122 per match). Back in 2012, Jed Hughes, writing for the bleacher report, suggested that the MLS is the third highest spectator sport on average in the U.S. because average per game attendance exceeded (slightly) average per game attendance of the NBA and NHL. However, his use of these stats is misleading because average per game attendance is only one measure of popularity of the sport from a spectating point of view. Other measures such as TV ratings, media contracts, and sponsorship revenue show that the MLS still has a long way to go before becoming as popular as the NHL, NBA, MLB, or NFL. Second, if the Seattle Sounders and Atlanta United FC, only two teams out of 22, are removed from the equation, average league attendance drops more than 2,000 people per game, placing the league substantially behind the NHL and NBA. All of this is not to say that the MLS is not increasing in popularity, it is, but it has not yet approached the other male leagues in the U.S. in popularity. Internationally, the MLS is seventh among football leagues in attendance at about half of the Bundesliga. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior In the United States, NCAA (National Collegiate Athletic Association) football attendance set a record in the 2013 season for the fourth time in five years with 50.3 million people. This was a 16.7% increase over the past decade, but since then attendance has dropped three out of the last four years.

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In 2017, four teams exceeded 100,000 attendees per game, with the University of Michigan leading the way at over 111,000 per game. The Ohio State University averaged over 107,000 per game and generated between US$4.5 and $5 million in ticket revenue per game (B. A. Turner, personal communication) and over $30 million for the season. Not too bad. However, even in the top 20 teams there is a substantial difference in the number of total attendees as #2 OSU had 300,000 more attendees than #20 Florida St. did, partially because they played 1 more home game than FSU did. This could equate to a difference in US$10 million or more in revenue. Unfortunately, there are also 30 FBS teams that average under 20,000 attendees per game and Ball St. averaged the least with only 9,899 people per game: evidence that there is considerable disparity among FBS schools and that this is something that the NCAA needs to address. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Records were set for bowl game attendance following the 2010 season, as 1,822,387 people attended the 35 bowl games. This broke the record set the previous year for total attendance. However, those numbers were down by 6% for bowls following the 2016 season, to 1.7 million. Bowl game Bowl/Attendance 2016-2017 2015-2016 2014-2015 2013-2014 2012-2013 attendance is largely determined by who the Rose 95,128 94,268 91,322 95,173 93,359 participating schools are Peach/Chick-fil-A 75,996 71,007 65,706 67,946 68,027 and how well their fans BCS Championship 74,512 75,765 85,689 94,208 80,120 travel. To some extent Fiesta 71,279 71,123 66,896 65,172 70,242 Music City 68,496 50,488 60,149 52,125 55,801 attendance is also Texas 68,412 66,517 71,115 32,327 50,386 determined by Orange 67,432 67,615 58,211 72,080 72,023 geographic location of the Alamo 59,815 64,569 60,517 65,918 65,277 Cotton 59,615 82,812 71,464 72,690 87,025 bowl game and the Sugar 54,077 72,117 74,682 70,473 54,178 weather. If two popular Outback 51,119 53,202 44,023 51,296 54,527 northerly schools get to Liberty 51,087 61,136 51,282 57,846 53,687 attend a bowl game in a Holiday 48,704 48,328 55,789 52,930 55,507 Russell Athletic 48,625 40,418 40,071 51,098 48,127 sunny location over the Belk 46,902 46,423 45,671 45,211 48,128 holidays, those bowl Citrus/Capital One 46,043 63,459 48,624 56,629 59,712 games will draw well. Tax Slayer/Gator 43,102 58,212 56,310 60,712 48,612 Smaller bowls may try to Sun 42,166 41,182 47,809 47,912 47,922 get the best match-up Armed Forces 40,542 42,169 37,888 39,246 40,754 Heart of Dallas 39,117 20,229 31,297 38,380 48,313 they can with at least one Pinstripe 37,918 37,218 49,012 47,122 39,098 of the schools being New Orleans 35,061 37,275 34,014 54,728 48,828 somewhat close Arizona 33,868 20,425 Cactus/Buffalo Wild Wings 33,328 39,321 35,409 53,284 44,617 geographically. Dollar General/Go Daddy 32,377 28,656 36,811 36,119 37,913 In general, though, Birmingham/BBVA Compass 31,229 59,430 30,083 42,717 59,135 the most popular bowls 29,688 30,289 28,725 27,104 24,610 continue to do fairly well New Mexico Las Vegas 29,286 42,213 33,067 42,178 33,217 with 7 bowls drawing Independence/V100 AdvoCare 28,995 28,995 38,242 36,917 41,853 over 60,000 per game Poinsettia 28,114 21,501 33,077 23,408 35,442 with the 2016-17 Rose Foster Farms 27,608 33,527 34,780 34,136 34,172 Cure 27,213 18,536 Bowl pairing USC and Military 26,656 36,352 34,277 30,163 17,835 Penn State drawing over Famous Idaho Potato 24,975 18,876 18,223 21,951 29,243 95,000. However, there Boca Raton 24,726 25,908 29,419 were a number of smaller Hawaii 23,175 23,557 25,365 29,106 30,024 Camelia 20,257 21,395 20,256 bowls that not only did Quick Lane/Little Caesars 19,117 34,217 23,876 26,259 23,310 not draw well (less than St. Petersburg/Bit Coin/Beef 'O' 15,717 14,652 26,675 20,053 21,759 25,000) but had Brady's attendance decreases of Miami Beach 15,262 21,712 20,761 10,000-18,000. This Bahamas 13,422 13,123 13,667 Total Attendance 1,710,161 1,798,217 1,730,254 1,714,617 1,722,783 certainly does not bode Average Attendance 41,711 43,859 44,365 48,989 49,222 well for those bowls. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior NCAA men’s basketball attendance grew dramatically from 2001 to 2008, setting new attendance records in 2008, with a dramatic jump from 2006 to 2007 as the University of

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Florida won back-to-back titles with the same set of starters. No team had ever accomplished this feat before in the history of the NCAA. However, since then, regular season attendance has been falling fairly consistently. By the 2016-2017 season, total attendance was down more than 1.5 million from the record in 2007-2008. Once again there have been many suggestions as to why the consistent decrease in fan attendance behavior, but very few solid explanations. However, the NCAA tournament has not seen the same decreases. During the 2007 NCAA tournament, almost 700,000 fans attended the 35 tournament games, over 19,900 people per game. Since then attendance has varied, but during the 2017 NCAA tournament, over 720,000 fans attended for an average of 20,103 per tournament game. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior NCAA women’s basketball has also seen growth over the last decade as total attendance reached a new record of 11.34 million fans in 2015-16. This is about a 19% increase since the 2001-2002 season. There was a slight drop in 2016-2017, but overall a small increase since 2007-2008. In 2016-17, the University of South Carolina led in average attendance, with 12,277

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fans per game. Tennessee (9,184) and Iowa State (9,106) were second and third respectively. Interestingly, at some of the top women’s attendance schools, the women have drawn similar attendance numbers as the men. In 2016-2017, at UConn (University of Connecticut) the women drew 8,888 a game while the men only drew 8,505 and at Notre Dame women’s attendance was 8,104, while men’s was 8,254. That said, nationally, the men outdraw the women by 20 million across the NCAA. This indicates that although the women are making great strides, they still need to continue to grow their fan base considerably.

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Introduction to Sport Consumer Behavior Suites and club seats Included in the attendance figures discussed to this point are the people that enjoy some of the “best seats in the house,” the suite and club-level seats. The suites and club seats, however, are more than just fancy places from which to watch a game. Professional sports teams have realized that a very valuable source of revenue can be gained through suite rentals and the sale of club seating. For instance, Forbes estimates that the Dallas Cowboys (NFL team)

generate over $100 million per year in suite revenues. Although in most of the professional leagues teams must share revenue from ticket sales and national media contracts, revenues generated from suite rentals are typically not shared with the league and other teams. This has made suites a very valuable stream of revenue for most owners and why many owners have clamored for new stadiums or new arenas that have many new suites. For those owners who cannot get (or do not want) a new stadium, remodeling the old will have to suffice. A while back, the Boston Red Sox redid some of their suites so that they could increase the price by $65,000 to $283,000 per year. “Cherrywood floors replaced tiles, new kitchens with granite countertops, food warmers, and stainless-steel appliances were installed, $12,000 audio-visual systems with three high-definition plasma TV sets and surround sound were added, along with computers with high-speed Internet access.” Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Some teams though are finding out they either built too many suites or the market has changed, and people do not want to have to rent a suite for the entire season, especially in baseball where there are so many games. For example, the Seattle Mariners tore out eight suites and converted the area into club seating which allowed them to seat more people and allowed fans to experience all the amenities of having a suite, without having to lease one for the entire year. Apparently the Safeco Field scenario is part of a national trend of a decline in demand for luxury suites. Stadiums and arenas throughout the nation are having to change their suite configuration or sales packages in response to the change in demand. As markets change and as fan preferences change, teams need to be able to understand their customers better than they typically do. Club seating and suites are certainly not limited to the professional ranks. When both The Ohio State University (Ohio Stadium) and the University of Tennessee (Neyland Stadium) remodeled their stadiums to expand regular seating, they also added suites, 81 and 78, respectively. These suites could generate more than $5 million per year for each athletic department. The University of Texas is another example; when the university remodeled Darrell K. Royal-Texas Memorial Stadium they added premium and club seating which will allow them to generate approximately $12 million a year (Schwartz, 2007). For programs like the University of Michigan, with a stadium capacity of 109,901, the largest in the country, luxury suites are a much more feasible way to increase stadium revenue than building another crowded upper deck. Even Missouri State University got into the action. Not exactly a hotbed of college basketball, they built a new $67 million arena (JQH Arena) which seats 11,000 fans. They included 24 suites, which sold out, and generated approximately $840,000 per year for the athletic department. In addition, they have 114 club seats and 50 courtside seats that sold for $2,000 -$2,500 per seat per year. Suites and club seats are a very valuable source of revenue, but both professional teams and college athletic departments need to know how to market these pricey seats, which requires a very good understanding of the potential consumer base. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Media consumption In their TVSM Global Report 2017, SportBusiness International estimates that the value of global sports media rights was just under US$47 billion in 2017, with potential growth to over US$54 billion in 2021. The U.S. led all countries with around US$21 billion, more than

quadrupling the next closest country (the UK at US$4.8B). Football (soccer) led all sports with global media rights value of US$18.8B, followed by American football with US$7.3B. Although this information shows how much media rights are valued, it does not tell us how sport fans are consuming the media.


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Introduction to Sport Consumer Behavior According to a Deloite survey in 2016, 74% of U.S. consumers continue to pay for TV subscriptions, which has held pretty steady since 2012. However, the percentage of consumers that are paying for a streaming service has jumped from 10% in 2009 to 49% in 2016. Neely (2016) noted that sports leagues and teams are the primary reason for this growth. Similarly, the decline in the amount of time spent watching live TV by American consumers has seemed to level off at about 4:06 hours per day according to Nielsen. Radio is not dead though as consumption is holding steady at about 1:52 hours per day, showing that radio still is a valid source of media content. The amount of time spent watching content on a smartphone though has increased from 58 minutes per day in 2014 to 2:10 hours in 2016. However, as is apparent by the graph to the right, consumption of sport by smartphone varies substantially by country, with 93% of sport fans in China consuming by smart phone, but only 40% in France. Furthermore, in the U.S., 30% of consumers use their smartphone while watching TV. We will discuss the ramifications of these issues later in the chapter, but for now we are going focus TV viewing. Television viewership NFL. The average TV audience size for NFL regular season games in 2016 was 16.5 million viewers, down from the 2015 numbers of 17.9 million per game. For the 2017 regular season, TV audiences are down another 10% on average. These recent decreases changed a slight reversal of the upward trend seen over the last five years. Now it seems that these declines are just continuing the downward trend over the last two decades. On the other hand, Super Bowl ratings and viewership continue to be strong as the Super Bowl is typically the most watched TV program every year. In February of 2015, Super Bowl XLIX between the Seattle Seahawks and the New England Patriots was watched by 114.4 million people, the mostwatched Super Bowl ever. It was also the most watched show ever on TV; exceeding the final episode of M.A.S.H. back in 1983, which had 105.97 million viewers. However, the last three Super Bowls in 2016-2018 saw TV viewership declines. However, for the 2018 Super Bowl, and estimated 10.3 million people streamed it, when added to the 103.4 million who watched it on TV, gave a total number close to the record in 2015. Regardless, the last three Super Bowls were still in the top 10 all time of shows watched on TV. Copyright 2018 Galen Trail

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The 2015 Super Bowl was the highest rated Super Bowl since 1986 with a 47.5 rating and a 71% rating share. The rating is the percentage of the nation's TV sets that were tuned in to the game and the rating share is the percent of the TV sets which were turned on and were tuned to the game.

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Introduction to Sport Consumer Behavior When the teams generate such good ratings, the media companies can charge enormous advertising rates as well. For a typical regular season NFL game, a 30 second ad may cost $700,000, almost double the amount charged for a 30-second spot during the highest rated drama series. Although this rate is substantially higher than any non-sport ad spot, it certainly pales in comparison to the Super Bowl ad rates of over $5.0 million for a 30-second spot in 2017. In 2011, Chevrolet might have actually got some bang for their $3.5Mbuck as Nielsen identified the Wild Ride ad in the 2011 Super Bowl where two guys describe a woman’s wild ride in a silver Chevrolet Camaro as the mostwatched ad of all time. An estimated 116.6 million viewers saw the advertisement. Even though attendance is down at regular season NFL games and TV viewing behavior is decreasing, the Super Bowl viewing base for the NFL seems to be doing well. MLB. As noted earlier in the chapter, attendance for Major League Baseball continues to decrease, but TV viewing is taking up the slack over the last two years, with national audiences increasing. However, MLB teams typically care more about their local TV ratings because they control those contracts and can generate billions of dollars in revenue from them (e.g., the Los Angeles Dodgers or New York Yankees). Local ratings for both 2016 and 2017 have varied dramatically by team and from year to year. For example, the Kansas City Royals average rating in 2017 was down over 30% from 2016, whereas the Cleveland Indians ratings were up almost 30% over the previous year. The Dodgers had one of the MLB World Series Ratings and worst local ratings in MLB Rating Share because no cable company 40 will carry their channel, but they had one of the largest 30 increases in viewership. However, the Rating ratings and ratings-share 20 Share for the World Series was definitely on a downward 10 trend since the year after the lockout in 1994, until the last couple of World 0 Series. The 2012 World 1995 1998 2001 2004 2007 2010 2013 2016


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Introduction to Sport Consumer Behavior Series was the lowest rated in history, but the 2016 Series between the Chicago Cubs and Cleveland Indians was the highest rated since 2004 and the Series in 2017 between the Los Angeles Dodgers and Houston Astros was the second highest rated. Up until the last couple of years, the decreasing attendance and decreasing World Series viewing behavior seemed to indicate a definite regression down the escalator model of consumption behavior. This also seems to indicate is that although the population in the United States continues to increase, as do the number of television sets, the number of people watching baseball on TV certainly is not increasing. NBA. The NBA seems to be losing viewers as well. According to Holloway (2017), TNT, ABC, and ESPN all had declines in viewership for the 2016-2017 campaign. Holloway noted that those three channels and NBA TV averaged 1.19M viewers, down approximately 6% from the prior season but on par with the 2014-2015 season. She hypothesized that the decrease was due to increases in streaming viewership. ESPN, for example, had an increase of 18% from 2015-2016, averaging 45,000 viewers per minute and she also attributed the decrease in TV viewing to teams resting star players. Both of these are possible, but neither have been proven. For the playoffs, from 1998 to 2006, the ratings for the NBA finals dropped almost 67% to set a historically low 6.2 rating for the 2007 finals between LeBron James’

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Introduction to Sport Consumer Behavior Cleveland Cavaliers and Tim Duncan’s San Antonio Spurs. One factor that likely contributed to the decline ratings for that period was the shift from network broadcasts to the majority of games being broadcast through cable station providers including ESPN and TNT. Since the lows of 2007 however, ratings and overall viewership for the NBA Finals has climbed: ratings to 11.6 in 2015 and viewership to slightly over 20 million for the last three years. Smaller market teams typically generate lower ratings, but not always. However, there is

a long way to go before the NBA can regain the previous highs during the Michael Jordan era (1997 & 1998) for NBA Final’s ratings and viewership. The NBA needs to have a better understanding of consumer (fan/spectator) beliefs and motives to figure out why the dramatic ebb and flow of the ratings exist. NHL. The NHL has never had good regular season TV ratings, mainly because it is a game that is very difficult to follow on TV, especially for people who are not hockey aficionados. Viewership varies dramatically from year to year for some reason and also it varies between Canada and the U.S. quite a bit. When one is up the other is down and vice versa. In the U.S., viewership declined by about 20% from the 2015-16 season to the 2016-17 season. In Canada though, viewership was up, especially for the Edmonton Oiler games (40%) and the Toronto Maple Leaf games (20%). The 2015-16 season was the NHL’s best on average for viewership in the U.S., averaging almost 500,000 viewers per game. However, in Canada it was lower for


Page 22

Introduction to Sport Consumer Behavior some reason. One might assume that the variances were due to success (or lack thereof) of teams based in the two countries, but no one has examined that possibility. Although the NHL’s Final’s ratings have always been better than the regular season, not surprisingly, they still NHL Finals Ratings aren’t very good 5 compared to the other leagues. Since their peak in 1997 at a 4.0 rating, 4 they dropped 60% to the lowest rated final on 3 record in 2007. Rating Apparently, the 2 Anaheim Ducks versus the Ottawa Senators was not riveting hockey. 1 In 2008 they did a remarkable rebound and the 2010 Finals were the 0 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 highest since 2002. However, the ratings dropped in 2011 and then dropped another 33% in 2012, to the second lowest ratings ever. They recovered to some extent in 2013-2015, but dropped again in 2016 and 2017. The 2013 Finals between the Boston Bruins and Chicago Blackhawks averaged 5.8 million viewers, which was the highest number of viewers on record, and Game Six was watched by 8.2 million. All of this is rather strange considering that once again the players were locked out for part of the 2012-2013 season. Hockey fans are resilient to say the least. Once more it seems that the league would definitely benefit from a more in-depth understanding of consumers, both fans and spectators.


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Introduction to Sport Consumer Behavior WNBA. The WNBA has never had good TV ratings, and like their male counterparts, their ratings have been dropping steadily (at least for those that we were able to find). However, the WNBA regular season ratings have been dropping since the leagues inception in 1997, but have now bottomed out around .1. Over the last several years for the finals, the ratings seem to have leveled off between .3 and .4, although the WNBA did achieve a .5 for ratings in 2017. Unfortunately, the number of viewers did not exceed that of the 2014 finals between the Phoenix Mercury and the Chicago Sky, where the reported average viewership for the three finals’ games was around 634K. Even so, these numbers pale in comparison to any male professional league and NCAA women’s basketball (see below) viewership totals. The WNBA and its fans need figure out how to make this product work in the near future or women’s professional basketball in the U.S. may not exist much longer. In 2016, Johnson and Cwieka

WNBA FINALS RATINGS/VIEWERSHIP Since ESPN acquired exclusive rights

Year 2017 2016 2015 2014 2013 2012 2011 2010

Net ABC, ESPN, ESPN2 ABC, ESPN, ESPN2 ABC, ESPN, ESPN2 ABC, ESPN, ESPN2 ESPN, ESPN2 ESPN, ESPN2 ESPN, ESPN2 ABC, ESPN2

2009

ESPN2

2008

ESPN2

Series MIN 3 LA 2 LA 3 MIN 2 MIN 3 IND 2 PHX 3 CHI 0 MIN 3 ATL 0 IND 3 MIN 1 MIN 3 ATL 0 SEA 3 ATL 0 PHX 3 IND 2 DET 3 SA 0 PHX 3 DET 2 DET 3 SAC 2 SAC 3 CONN 1 SEA 2 CONN 1 DET 2 LA 1

Rtg.

Vwrs.

0.5

559K

0.3

487K

0.3

502K

0.4

634K

0.2

345K

0.3

477K

0.3

515K

0.4

495K

0.4

551K

0.2

315K

suggested that the WNBA had turned the ESPN, 0.4 545K 2007 corner based on increased attendance after ESPN2 the 2016 season and a fairly successful 0.4 484K 2006 ESPN2 marketing campaign. That said, the regular season ratings for the 2016 season were still ABC, .07 and that was after a 20% increase from 0.3 437K 2005 ESPN2 the season before. The WNBA has thought that they had found the key to success 0.2 345K 2004 ESPN2 before, but it hasn’t been true. They and their fans need to remember that the NBA ABC, 849K 0.6 2003 wasn’t exactly that successful 20 years into ESPN their existence either. In fact, the NBA was still struggling until a couple of rookies named Larry Bird and Magic Johnson joined the league in 1979, a mere 33 years after the first game in NBA history. Prior to that season in 1979, very few games were on TV and games were often not broadcast live. So, if the WNBA can hang on for at least a decade more, they might become considerably more successful than they currently are, because based on some measures of success, they are ahead of where the NBA was at the same stage. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior RATINGS/VIEWERSHIP FOR COLLEGE FOOTBALL POSTSEASON NCAA Football. Up * Peach and Fiesta bowls are compared to last year’s semifinals until the 2010-11 the NCAA ^ Orange and Cotton bowls are compared to 2014-15 season Viewership Bowl Season, college football +/This Yr. Net Date/Time Rtg. Bowl Game viewership did not seem to Mon 1/9 ESPN Clemson have the problems 8 PM Megacast -3% 14.2 25.266M Alabama CFP National experienced by the Lowest rated and least-watched title game since 2012 Championship preceding professional Sat 12/31 ESPN Alabama CFP Semifinal (Peach 3 PM ESPN2 Washington 10.7 19.344M +23% Bowl)* leagues. Perhaps this Sat 12/31 ESPN Clemson CFP Semifinal (Fiesta indicates that the United 7 PM ESPN2 9.8 19.237M +3% Ohio State Bowl)* States is predominantly an Mon 1/2 USC 5 PM ESPN Penn State 8.6 15.740M +16% American football (not Second-lowest rating in Rose Bowl history Rose Bowl soccer) mad country. Fri 12/30 FSU However, as Paulsen (2017) 8 PM ESPN Michigan 6.2 11.461M +28% noted, 25 of the 39 bowl Most-watched Orange Bowl, excluding playoffs, since 2008 Orange Bowl^ games posted a decline in Mon 1/2 Oklahoma 8 PM ESPN +6% 5.6 9.515M Auburn viewership. I’ve only Second-lowest Sugar Bowl rating since 1994 Sugar Bowl included the top 9 watched Sat 12/31 LSU bowls in the figure to the 11 AM ABC -27% Louisville 3.9 6.390M Citrus Bowl right, but go to Paulsen’s Mon 1/2 Florida 1 PM ABC 3.6 6.106M +116% Iowa Outback Bowl webpage for all of the Mon 1/2 Wisconsin ratings. In addition, 23 of the 1 PM ESPN -40% W. Michigan 3.1 5.442M 25 that posted declines, set Lowest rated and least-watched Cotton Bowl since 2005 Cotton Bowl^ multi-year lows. This doesn’t bode well for the NCAA and bowl committees. Average regular season viewership for NCAA football on CBS has shown declines every year since 2013 NCAA Football Regular Season Viewership with a plateauing the last two years. 8,000,000 Viewership on ABC 6,000,000 has varied quite a bit, but combined, 4,000,000 the viewership 2,000,000 numbers are down from 2013. This 0 even includes one 2013 2014 2015 2016 of the most SEC on CBS ABC Saturday Night Football watched games with over 16.8 million watching Michigan at Ohio State on Nov. 26th, 2016. Approximate 50% more people watched that game than the SEC Championship game between Alabama and Florida that same year. In addition, more people watched it than all but 4 bowl games that season.


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Introduction to Sport Consumer Behavior The playoff system for the NCAA FBS (matching up the top four teams) seemed to generate a substantial buzz at the end of the 2014 season and thus increased viewership from previous seasons’ bowl games. However, since then ratings and viewership has declined as the novelty effect wore off. In addition, Solomon (2014) thought that there was less interest in lower-profile teams that have no shot in being one of the top four teams. Even though teams from one of the smaller profile conferences (e.g., MAC) might make a decent bowl, it is highly unlikely that any team from one of those conferences would be selected into the final four playoff. This seems to marginalize those schools and teams. There was suspicion that even the Big-12 conference was at a disadvantage compared to the SEC, ACC, Big 10 and Pac 12 because of the smaller number of schools in the conference and no conference championship game, so they added schools and this year (2017) added a conference championship. Overall, I think we can look back and see that this was probably the start of a new era where there will be greater separation between the “Big Five” conferences and the rest. In many ways the power and money is tilting even more toward those schools (in the Big Five conferences). NCAA Men’s Basketball. Season-long television ratings for the NCAA Men’s basketball regular season are difficult to come by; however, ratings for the NCAA tournament show a

downward trend from 1998 to 2003 where they bottomed out at a rating of 5.0. Since that 31.5% drop, ratings improved through 2007 by 46% exceeding 1999 levels. Unfortunately, the ratings dropped again in 2008 and 2009, and have bounced around a bit since then. However, the ratings for the Men’s Championship game which had shown an even more precipitous decline over the 1998-2004 period (38.2%) and then a jump in 2005 to a 15.0 rating, dropped again to a historical low in 2009 at 10.8. The ratings for the 2010 Championship game between Butler and Duke seemed to be another anomaly similar to 2005, when there was another jump. This seems to indicate that the 2005 and 2010 championship games might be Copyright 2018 Galen Trail

Page 26

Introduction to Sport Consumer Behavior incongruities in a downward trend of ratings, as the 2011 game was down substantially. Since then, the ratings have bounced around, but in 2016 set a new low. The total TV audience numbers, as one would expect, reflect the same trends for both the tournament and the championship game as the 2010 numbers (23.9 million viewers) was the highest since 2005 by quite a bit. The 2015 game between Duke and Wisconsin had over 28 million viewers, the most since 1997, but the next year there were only 17.75 million viewers for the game between Villanova and North Carolina. Overall this indicates that marketers and managers need to understand their audience better so that they can determine why fewer people are watching. Unless these trends are reversed, future media contracts will be reduced, thus reducing the amount of money that each athletic department receives. NCAA Women’s Basketball. NCAA Women’s Basketball Championship game TV ratings have fluctuated fairly substantially since 2000 with a high of 4.3 in 2004 and a low of 2.0 in 2013

NCAA Women's Basketball Championship Game TV Rating 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

and 2016, but the fluctuations seem to be more dependent on the teams playing than any trend of fan interest in women’s basketball. However, a number of commentators have hypothesized that the decrease in ratings lately is due to the University of Connecticut dominating women’s basketball with their 111-game win streak that was ended in the semifinal game in 2017. There was a slight uptick in the finals game ratings from the previous years’. It was the first time that UConn wasn’t in the final for quite some time. That said, marketers need to know their consumers better so that they can market to them more efficiently and segment their markets more appropriately. However, there is also another possibility that explains the decrease in ratings and/or number of viewers across all sports, and that is livestreaming. Fans who are livestreaming are not usually counted in ratings and typically not counted in the viewership numbers. So, let’s talk about that a little more. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Livestreaming Sport fans are able to live stream sports through existing TV partners. For example, I can watch the NFL on my laptop or on my smart phone through my Xfinity-Comcast subscription, which carries CBS, NBC, ABC/ESPN, and Fox. Those are the media channels that own the TV broadcast rights for the NFL. However, in 2016, Twitter paid the NFL US$10 million to livestream the Thursday night games. This year (2017), Amazon paid US$50 million to stream those games. So now fans can watch the games on TV, stream them online through cable carriers or through social media. Although some claim that the reason the NFL is losing TV viewership is because people are streaming through a social media company and those individuals are not being counted, the math doesn’t work. For example, the Thursday Night football games brought in an average on 15 million viewers in 2016, but were down 9%, or approximately 1.5 million viewers, from 2015. However, the number of people livestreaming those games on average was around 350,000. So, the NFL was still down 1.15 million in viewership even if those livestreaming were taken into account. The accuracy of those numbers (and others) might be debated because sometimes they are estimates, rather than exact numbers. However, Nichols (March, 2017) reported that in a recent Morning Consult poll, 47% of U.S. adults indicated that they would be more inclined to watch an NFL game if a social media company streamed it. In addition, 19% of those surveyed said that they did watch at least one NFL game streamed by Twitter during the 2016 season. This seems to indicate that the trend to watch streamed events through social media will probably increase. What is interesting though, is that the percentage of Americans willing to watch streamed games varied dramatically across sports as the graph above shows. Regardless, sport marketers need to understand why fan behaviors are changing, how they are changing, and especially how their viewership behaviors are changing. Different ways of consuming the product through media means that there will need to be different ways of generating revenues based on these different media types. Understanding why some fans are will stream sports, whereas other won’t, is very critical. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior A corollary to the idea that viewership is down due to streaming, is that people are still watching, but just aren’t watching as much or as long, and some have blamed younger generations for this decrease. Singer (2017), despite the information in the graph to the right that shows there is a 7% difference between Millennials (38%) and Gen Xers (45%) who consider themselves committed sport fans (see Total at bottom of graph), argues that we shouldn’t blame millennials, but blame short attention spans instead. He argues that millennials are sport fans too, but they just consume different sports and in briefer amounts. In some sports, the millennials actually are as committed as the Gen Xers (NBA) or are bigger fans (International soccer or MLS). The major difference comes from the NFL, where Gen Xers are much more likely to be committed fans.

Millennials do consume sport differently than older generations (not surprisingly), so their consumption may not be measured correctly. In addition, Singer argues that Generation X sport fans are following the millennials by adopting digital technology to consume sport, which also makes it look like Gen Xers are losing interest in sport, when actually they, also, are just changing consumption patterns. As we can see though, watching the games through media is not the only way that fans consume sport through media, they also consume news, highlights and information about their favorites teams through a variety of media. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Sports news consumption The Pew Research Center determined that more and more people in the U.S. are consuming general news online rather than on TV, although TV still has a slight (7%) edge. Radio news consumption is holding fairly steady at 25%, but print newspaper consumption still continues to decline, now at 18% in 2017. In addition, more people are starting to prefer getting their news on their mobile device rather than on a desktop computer, as 45% of U.S. adults get at least some of their news on a mobile device, and of those that get news both on mobile and on desktop, over 67% prefer mobile, showing the value of mobile. Specific to news about sport, preferences about where sport fans get their fix is highly country dependent. For example, in 2016, 38% of sport fans in Japan preferred getting their sport news from TV, whereas only 20% in the U.K. get their sport news from TV. Internet sport news consumption is the highest in Italy, the U.K. and Japan. Radio typically ranked higher than newspapers in most countries. WOM (word of mouth) is typically not a source of sports news for most people. The graph also shows the percentage of people in each country who have no interest in sports news, which also varies a fair amount. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Team Websites & Social Media As the previous graph showed, a number of people get their sport news online, but even more people follow their specific team online either through the team’s website or through social media. In 2016, Forbes.com ranked sport teams by their global reach. Team

Rank

Followers (Millions)

Interactions (Millions)

Media Value (Millions $US)

1

FC Barcelona

145

1,450

$25.30

2

Real Madrid

141

601

$17.20

3

Manchester United

88.4

522

$12.10

4

Arsenal

50.6

239

$9.20

5

Chelsea

59.6

181

$6.10

6

FC Bayern

46.8

204

$2.50

7

Los Angeles Lakers

29.4

106

$11.00

8

Golden State Warriors

13.6

293

$3.30

9

Manchester City

27.5

107

$5.80

10

Cleveland Cavaliers

9.5

102

$12.50

11

Juventus

30.3

156

$2.00

12

Liverpool

37.3

146

$1.60

13

New England Patriots

10.9

88.4

$4.30

14

Borussia Dortmund

18.8

91.4

$1.90

15

Paris Saint-Germain

32.7

67.2

$1.30

16

Denver Broncos

7.3

84.5

$2.30

17

Dallas Cowboys

11.7

61.41

$2.50

18

San Antonio Spurs

9.3

80.3

$1.70

19

Carolina Panthers

5.1

80.3

$1.70


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Introduction to Sport Consumer Behavior 20

Seattle Seahawks

7

66.7

$2.70

FC Barcelona leads all teams in followers with 145 million. This is more than all of the NFL teams combined! Real Madrid closely follows with 141 million. However, as Scott Tilton, the cofounder for Hookit noted, follower numbers don’t necessarily translate into engagement or value of earned media. Based on the Hookit examinations, Barcelona also leads in interactions (likes, shares, comments, and retweets) with 1.45 billion. Forbes estimates that this translates to a little more than US$25 million in earned media value. This is not a linear relationship across teams though because, as you can see, the Los Angeles Lakers only had 106 million interactions, or less than 1/14th the amount as Barcelona, but had US$11 million in earned media value, slightly less than half that of Barcelona. Furthermore, the Cleveland Cavaliers only have 9.5 million followers, but over 100 million interactions, which translates to more earned media value (US$12.5 M) than the Lakers. Forbes created the overall rankings by averaging the rankings for each team across the three categories. So even though the Lakers are 10th in followers and 11th in interactions, because they are 5th in media value, that bumps them up to 7th overall in the ranking.

Looking at the 4 largest leagues in North America, it is also noticeable the differences specific to attention garnererd on Instragram and how it varies (or not) across the season. The NBA seems to generate the most interest overall, and that interest peaks during the finals, but drops off dramatically during the off-season.

The above information shows how digital consumption varies by league, by team, and by country, but Yahoo segmented fans is the U.S. a little differently. They noted that sports fans, can be broken into three segments: Casual Fans, Everyday Fans, and Uber Fans. They estimated that there are approximately 15 million Uber Fans and that they spend an average of 14 hours per week online. This is more than 2.5 times the 5.6 hours that Everyday Fans spend. However, there are 35.5 million Everyday Fans. Casual Fans on the other hand spend only about two hours per week online and there are only about 10 million of them. Granted this data is Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior now a little old, coming from 2011, but it shows back then how much time is being spent online. Yahoo also reported that 97% Uber Fans check stats the day after the game, 93% go online while watching a game, and 75% participate in fantasy sports. These percentages are considerably higher than for Casual Fans of whom only 68% check stats, 40% go online during a game and only 10% participate in fantasy sports. Fantasy sports is not something that we have touched on yet and we need to do so. Fantasy Sports Fantasy sport is defined as any “sports competition with imaginary teams which the participants own, manage, and coach.” The games are based on statistics generated by actual players or teams of a professional sport. Sometimes these are called rotisserie leagues. As you can see from the graph below, the number of fantasy sports players is dramatically increasing in the U.S. and Canada, to 56.8 million in 2015 according to Business Insider. This flattened out

considerably in 2016, only increasing by .6 million people, to 57.4 million. FSTA also estimates that approximately 20% of the population in Canada and the U.S. participate in fantasy sports. Although these numbers pale in comparison to those that spectate and consider themselves fans of ‘real sport’, it is definitely a segment that is garnering interest from the media, advertisers, sponsors, and sport consumer researchers. Researchers have started to Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior investigate the motives behind fantasy league behavior as well, typically following research and theories of fan behavior to try and determine whether this segment consumes sport differently.

As you can see from the graph below, participation in a season-long fantasy sports league varies fairly dramatically by age and a little bit by level of fandom. We will discuss more about that in future chapters. Although fantasy sports seem to be plateauing in interest, another type of “sport” consumption is taking off, and that is eSports. eSports eSports are defined as a “multilayer video game played competitively for spectators, typically by professional gamers.” Moran points out that like fantasy sports, eSports is largely the domain of the young, with more than half of Millennial respondents either strongly or marginally interested (compared with a mere 21% of non-Millennials). Although fantasy and real sports tend to go hand in hand, however, the opposite is true with eSports — which may help explain the strong disinterest among sports traditionalists (only 6% of pro football fans identify as avid eSports followers, for instance). Although there is an audience for eSports and the audience is increasing (see graph below), and yes, the video game being played is a sport video game, I am not ready to include this category Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior in sport consumption. It certainly can be considered entertainment, and theories and research paradigms used in sport consumer research may be applicable, as they are applicable to business and entertainment in general, I still don’t include it within spectator sport because the spectacle is not focused on athletes competing against each other on a field of play. Others will certainly disagree 😊😊. Newzoo estimates that the number of eSports enthusiasts will continue to increase and estimates that it already exceeds 175 million people in 2017. They also estimate that the number of occasional viewers will increase to over 210 million in 2019, while the number of people aware of eSports will exceed 1.5 billion in 2019. About 35% of that awareness comes from North America (NAM in the circle chart) and an additional 28% from China. Newzoo also claims that 22% of American male millennials (age 21-35) watch eSports, which supposedly is more than ice hockey and equivalent to baseball. That said, it is about half that of basketball and American football (see graph below). In addition, the other generations listed, both younger and older have little interest in eSports, and one assumes that women have considerably less interest in eSports than men, considering that females were removed from the sample to show the graph, and that females view eSports at about half the rate of males across all of the sports listed in the Newzoo report. So why the interest in eSports? It does seem that Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior eSports allows access to the coveted male millennial demographic that advertisers and consumer businesses want. This demographic tends to spend their money rather easily and on products that other segments don’t as much. Team licensed merchandise As noted earlier in the chapter, Plunkett Research, Ltd. estimated that entire U.S. Sport Industry was valued at $485 billion in 2014 (and $1.5 trillion for the entire world), but notes that is a very rough estimate because the sports world is so complex it is very difficult to put an exact number on it. Transparency Market Research indicated that a part of the industry is Licensed Sports Merchandise and estimated that in 2016 for North America the market revenue was approximately US$14.66 billion. This includes the combined sales of licensed merchandise among the four major professional leagues and universities in the U.S. The Licensing Letter’s

Retail Sales of Licensed Merchandise 4.00 3.50

$ in Billions

3.00 2.50 2.00 1.50 1.00 0.50 -

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

MLB

3.10

3.20

3.30

3.15

2.82

2.75

3.10

3.18

3.21

3.29

3.52

NFL

3.15

3.30

3.25

3.05

2.74

2.7

3

3.07

3.13

3.29

3.4

NBA

2.35

2.2

2.25

2.05

1.8

1.75

2

2.05

2.2

2.32

2.54

NHL

0.40

0.75

0.80

0.80

0.75

0.63

0.89

0.91

0.93

0.99

1.03

Sports Licensing Report noted retail sales of licensed merchandise based on sports leagues and events has continued to grow in the U.S. and Canada. MLB led the way by selling US$3.52 billion in licensed merchandise in 2015 followed by the US$3.40 billion the NFL sold of its product. The NBA added an estimated US$2.54 billion in gross sales and the NHL pulled in an estimated US$1.03 billion from sales. Only in the last several years have the leagues finally recovered from the highs of 2007. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior According to the newly renamed IMG College Licensing, which handles licensing for approximately 200 colleges and universities, the North American college sports licensing

2017-18 Total Apparel Deals in Millions $20.00 $18.00 $16.00 $14.00 $12.00 $10.00 $8.00 $6.00 $4.00 $2.00 UCLA

Louisville

Texas

Michigan

Wisconsin

Nebraska

Texas A&M

South Carolina

Kansas

Auburn

Ohio State

Indiana

Oklahoma

Cincinnati

Maryland

Oklahoma St.

Connecticut

LSU

FSU

North Carolina

$-

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

market is probably around US$5 billion in 2017. The top 20 apparel deals in the NCAA are listed in the above chart. Under Armour overpaid by quite a bit for UCLA and is probably regretting it now and I’m sure that Adidas is certainly regretting the $16M they paid Louisville considering the fiasco there now with the athletic department under FBI investigation. The University of Texas dropped to 3rd after leading for 10 years. As Lee and Trail (2012) noted, “Not only are ATM [Athletic Team Merchandise] sales an expanding and important source of revenue generation for many sport organizations, but ATM may also cause a synergistic purchasing effect. For instance, it is easy to observe sport fans wearing or possessing several items of ATM such as hats, t-shirts, and jerseys while consuming sport (e.g., attending, participating, or watching sporting games)” (p. 2). So not only is it important to understand why consumers attend games and consume sport through the media, it is also very important to understand why people purchase team-licensed merchandise. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Concession consumption Most teams will not make as much money on concessions as the Yankees do. In 2013, they netted US$53 million and Washington Redskins are making more than $15 million per year on concessions and that is with only 8 regular season home games. Koren and Vincent have estimated that teams generate about US$8.7 million on average for concessions. They probably used the same technique as I do by using the Team Marketing Report’s (TMR) Fan Cost Index (FCI). Unfortunately, TMR and the FCI no longer exist for some reason, so I don’t have the numbers for the most recent years. However, using the data that they had, I estimated how much concession costs have increased since 2000 across the four major leagues (MLB, NBA, NFL, and NHL) by using the league averages for the cost of a hotdog, a beer, and a soda from the information tracked by TMR. For MLB, in 2000, the total

Concession Costs $20.00 $18.00 $16.00 $14.00 $12.00 $10.00 $8.00 $6.00 $4.00 $2.00 $-

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

MLB

$8.

NBA

$9. $10 $10 $11 $11 $11 $11 $12 $14 $14 $14 $15 $16 $16 $17 $16

NFL

$9. $10 $11 $11 $12 $12 $12 $12 $14 $15 $15 $16 $16 $16 $17 $17 $17

NHL

$9.

$9.

$9. $10 $10 $11 $11 $12 $13 $13 $13 $13 $13 $14 $14 $14 $14

$9. $10 $10


$11 $12 $12 $13 $14 $14 $14 $15 $16 $16

Page 38

Introduction to Sport Consumer Behavior cost of a hotdog, a beer and a coke, averaged for the league, was $8.97. By 2016, the cost was $14.60, a 63% increase. Similarly, for the NBA, concessions increased 71%; 77% for the NFL; and 78% for the NHL. However, during that same time, inflation rate only increased 43.8%. There were a couple of years that had a large impact on concession costs though. In 2008 for the NFL and 2009 for the NBA there were huge increases of 14% and 16% respectively. The NHL and MLB also had jumps of 8% around this same time. However, recently concession costs have increased around the rate of inflation. In fact, in the NFL, concession costs have decreased in dollar amounts in 2015 and 2016.

Comparison of Concession Increases to Inflation Rate

Percent

18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% -2.0% -4.0% Inflation Rate

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 3.2% 2.8% 3.8% -0.4% 1.6% 3.2% 2.1% 1.5% 1.7% 2.4% 2.1%

% MLB Concession Increase

3%

5%

8%

-2%

0%

2%

5%

1%

3%

3.5% 3.3%

% NBA Concession Increase

1%

1%

16%

1%

5%

3%

4%

5%

1%

4.2%

% NFL Concession Increase

3%

2%

14%

4%

1%

5%

2%

-1%

5%

0%

% NHL Concession Increase

6%

5%

5%

8%

3%

1%

5%

6%

2%

0%

For most franchises and athletic departments, concession sales are a critical source of revenue, especially for minor league teams and for colleges. Regardless of the level, managers need to understand what the consumer wants from the concession stand, booth, or restaurant. As Muret (2007) discussed in a SportsBusiness Journal article, there are many ways to maximize concession revenues. The Kansas City Royals had performed very poorly on the field for a good number of years, which obviously had a deleterious effect on attendance. In late June of 2007, the Royals decided to try something new by selling all-you-can-eat seats. Mark Tilson, the Royals’ vice president of marketing and sales said that gross ticket revenue from those seats increased by $60,000 over what the Royals had forecast for those seats. The seats, which sold for $22 in 2006, went for $35 in advance or $40 on game day in 2007 (Muret, 2007). The Royals were not the only team trying out this new approach to maximizing per fan revenues. Now, 18 of the 30 MLB teams are doing it and there is a specific website where you can go to get your gastromic fix. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Naming Rights Back in the early 2000’s, naming rights deals seemed to have plateaued at an average of a little less than $3 million per year. However, they recovered for a bit around 2006-2008 when the Citigroup Inc. deal went through with the New York Mets for $20 million a year, but then the economy tanked and other than MetLife Stadium that houses the New York Giants and New York Jets, new stadia had difficulty selling the naming rights (e.g., Dallas Cowboy stadium, which finally sold for $20 million per year in 2013 over 4 years after it opened). However, naming rights have been picking up again as the new Miami Dolphins home (Hard Rock Stadium), Atlanta Falcons home (Mercedes-benz Stadium), and the Minnesota Vikings new home (U.S. Bank Stadium) have all been in the US$11-$14 million per year range. Although they didn’t get quite as much per year, at least they didn’t have to wait. If the Los Angeles Rams get what they want, they will set a new record as they are asking US$30 million a year for 20 years for a total of US$600 million. MSN Sports published the top naming rights deals for stadia in the U.S. (see below). These top 20 deals have generated an average of over US$10 million per year for the respective teams. Stadium

City

Sponsor

1.

Citi Field

Queens, N.Y.

Citigroup

2.

AT&T Stadium

Dallas (Arlington)

3.

MetLife Stadium

4. 5.

No. of years

Avg. annual value (millions)

Expiration year

$400

20

$20.00

2028

AT&T

$400

20

$20.00

2036

Metropolitan Life Insurance Hard Rock International

$400

25

$18.00

2036

Hard Rock Stadium

East Rutherford, N.J. Miami, FL

$250

18

$13.90

2034

Mercedes-Benz Stadium

Atlanta

Mercedes-Benz

$310

27

$11.50

2045

6.

U.S. Bank Stadium

Minneapolis

U.S. Bank Corp.

$220

20

$11.00

2036

7.

Levi’s Stadium

San Francisco

Levi’s

$220

20

$11.00

2033

8.

NRG Stadium

Houston

NRG

$310

31

$10.00

2032

9.

SunTrust Park

Atlanta, GA

SunTrust Bank

$250

25

$10.00

2042

10. Mercedes-Benz Superdome

New Orleans, LA

Mercedes-Benz

$100

10

$10.00

2022

11. Gillette Stadium

Foxboro, Mass.

Gillette

$240

15

$8.00

2031

12. FedEx Field

Landover, Md.

FedEx

$205

27

$7.59

2025

13. University of Phoenix Stadium

Glendale, Ariz.

Apollo Group

$154.50

20

$7.72

2026

14. Bank of America Stadium

Charlotte

Bank of America

$140

20

$7.00

2023

15. Home Depot Center

Carson, Calif.

Home Depot

$70

10

$7.00

2013

16. Lincoln Financial Field

Philadelphia

Lincoln National

$139.60

20

$6.98

2022

17. Minute Maid Park

Houston

Coca-Cola Co.

$178

28

$6.36

2029

18. Lucas Oil Stadium

Indianapolis

Lucas Oil Products

$121.50

20

$6.07

2027

19. M&T Bank Field

Baltimore

M&T Bank

$79

15

$5.00

2017

20. Citizens Bank Park

Philadelphia

Citizens Bank

$95

25

$3.80

2029


Price (millions)

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Introduction to Sport Consumer Behavior Arenas on the other hand typically generate about half that per year for the host teams, or just over US$4.1 million a year. However, the recent deals are for around $10 million a year, although the naming rights for the Golden State Warriors and JPMorgan Chase might be even more. The disparity between the naming rights deals for stadia and arenas is interesting as arenas typically have a greater number of events per year than stadia, even baseball stadia. Arena

City

Sponsor

Price (millions)

No. of years

Expiration year

20

Avg. annual value $10.00

Barclays Center*

Brooklyn, N.Y.

Barclays PLC

$200

Capital One Arena

Washington, D.C.

Capital Ond Bank

$100

10

$10.00

2027

American Airlines Center

Dallas

American Airlines

$195

30

$6.50

2030

Philips Arena

Atlanta

Royal Philips Electronics

Nationwide Arena

Columbus

Nationwide Insurance

$185

20

$9.25

2019

$135

Indefinite

NA

Indefinite

TD Garden

Boston

TD Bank

$119.10

20

$5.95

2025

Staples Center

Los Angeles

Staples

$116

20

$5.80

Prudential Center

Newark, N.J.

Prudential Financial

$105.30

20

$5.26

2027

Toyota Center

Houston

Toyota Motor Sales USA

$100

20

$5.00

2023

FedEx Forum

Memphis

FedEx

$90

22

$4.09

2024

Consol Energy Center

Pittsburgh

Consol Energy

$84 -$105

21

$4-5.0

2031

RBC Center

Raleigh

Xcel Energy Center

St. Paul, Minn.

RBC Centura Banks

$80

20

$4.00

2022

Xcel Energy

$75

25

$3.00

2024

Pepsi Center

Denver

PepsiCo.

$68

20

$3.40

2019

Bell Centre

Montreal

BCE Inc.

$63.94

20

$3.20

2023

Honda Center

Anaheim

$60.45

15

$4.03

2020

HP Pavilion at San Jose

San Jose

American Honda Motor Co. Hewlett-Packard

$47

15

$3.13

2016

AmericanAirlines Arena

Miami

American Airlines

$42

20

$2.10

2019

AT&T Center

San Antonio

AT&T

$41

20

$2.05

2022

Amway Arena

Orlando

Amway Global

$40

10

$4.00

2020

Conseco Fieldhouse

Indianapolis

Conseco

$40

20

$2.00

2019

Wells Fargo Center

Philadelphia

Wachovia

$40

29

$1.38

2023

2032

NA

While college stadiums have historically been named after the team, school or home state, athletic departments have begun pursuing the pot of gold we call naming rights. The University of Minnesota signed a $35 million, 25-year deal with TCF Financial Corp., giving the company naming rights for an on-campus football stadium. The University of Louisville named their football stadium Papa John's Cardinal Stadium after the founder of Papa John’s Pizza donated $22 million. In addition, the University of Central Florida named their football stadium Bright House Stadium in return for $15 million paid out over 15 years. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior Facility

School

Sponsor

Cost (US$M)

Annual value (US$M)

Years

Expiration

L.A. Coliseum*

USC

United Airlines

$70.00

$4.70

15

2031

Alaska Airlines Field at Husky Stadium

Washington

Alaska Airlines

$41.00

$4.10

10

2025

Save-Mart Center

Fresno State U.

Pepsi

$40.00

$2.00

20

2023

TDECU Stadium

Houston

Texas Dow Employees Credit Union

$15.00

$1.50

10

2024

Kroger Field

Kentucky

Kroger

$22.20

$1.85

12

2028

TCF Bank Stadium

Minnesota

TCF National Bank

$35.00

$1.40

25

2034

Xfinity Center

Maryland

Comcast

$25.00

$1.00

25

2026

Apogee Stadium

North Texas

Apogee

$20.00

$1.00

20

2030

Spectrum Stadium

Central Florida

Charter Communications

$15.00

$1.00

15

2022

TD Ameritrade Park Omaha^

College World Series

TD Ameritrade

$15.00

$1.00

15

2022

UFCU Disch-Falk Field^

Texas

University Federal Credit Union

$13.10

$0.87

15

2021

Albertsons Stadium

Boise State

Albertsons

$12.50

$0.83

15

2029

Although naming rights and other sponsored signage is not a consumer behavior like attending the game, watching it on TV, or purchasing team merchandise, consumer behavior affects and is affected by these elements. Sponsoring organizations want to partner with sport organizations that have similar target markets. Hopefully we will never see the Duke Blue Devils sponsored by Depends. Thus, understanding the consumer values, beliefs, and life styles, assists marketers in proposing naming rights packages or other sponsoring packages to the appropriate sponsor, which will more likely generate sponsorship revenue for the team or athletic department.


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Sponsorship Sponsorship is defined as a business relationship between a provider of funds, resources, or services and a sports event or organization which offers in return some rights and as association that may be used for commercial advantage (Sleight, 1989). According to IEG, sport is the largest category of sponsorship spending (70%) in North America, remotely followed by Entertainment in second place at 10%, with Causes in third at 9%. In 2017, IEG projected that over US$23.2 billion would be spent on sports sponsorships, up from $7.21 billion in 2003, a 300% increase in a about a decade and a half. As is evident in the graph below, sport sponsorship spending has slowed since 2008, but is still slighlty ahead of both advertising and direct marketing/promotions in 2017, as the latter two decreased spending tremendously in 2009. This seems to indicate that if businesses are going to continue to spend, the money is more likely to go to sponsorship than advertising or promotions.

% Growth in Category by Year Percent Growth

15.0% 10.0% 5.0% 0.0% -5.0% -10.0%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Sponsorship

8.4% 8.9% 10.5%11.5%11.0%-0.6% 3.9% 5.5% 4.4% 4.50% 4.1% 4.1% 4.6% 4.5%

Advertising

7.4% 3.0% 2.9% 2.8% 6.0% -7.1% 2.2% 3.2% 3.4% 1.8% 3.9% 3.4% 4.3% 4.4%

Sales Promotion 4.7% 5.3% 3.8% 3.8% 2.0% -4.6% -3.3% 3.6% 0.7% 4.3% 5.5% 4.2% 4.0% 3.0%


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Introduction to Sport Consumer Behavior Corporate partnerships (sponsorships) cover college and professional teams equally well. The Golden State Warriors just signed an advertising deal with Rakuten (a Japanese tech company), selling the space on their jerseys for about US$20 million a year. However, this pales in comparison to how much Rakuten’s partnership with FC Barcelona is though (US$235 million for 4 years!). On the college side, the University of Louisville just signed a long-term extension (10 years) with Adidas for US$160 million. Marketers need to understand whether or not fans will be as loyal to the sponsors as they are to the team. In general, this is not the case; however, research has shown that in some instances fans may show more loyalty to sponsor products than to other similarly situated products. The Crux of the Issue So why is all of this information important? The preceding information indicates that sport managers and marketers need to put renewed emphasis on maintaining and developing fans. The SportsBusiness Journal along with the ESPN Sports Poll has shown that the percentage of fans earning less than $30,000 a year has dropped in all of the major sports. The percentage of fans earning more than $100,000 has increased in all sports. The numbers indicate two things. First, leagues have started to price lower-income fans out of the stadium and arena. This is evident by evaluating the price increases over the last decade and a half across all four major leagues (see graph below). Second, more fans are earning larger incomes. While the latter point is potentially encouraging, the former certainly is not. Lower-income fans cannot afford to take in a game anymore. This is causing an economic disconnect with this market segment and creating disgruntled fans. It is reasonable to expect that these fans will be less likely to purchase merchandise. Eventually these people may cease to be fans because of their dissatisfaction with the team and their perception of how they were treated. Many of these fans have been loyal for quite some time and perceive that their loyalty is not being rewarded. Copyright 2018 Galen Trail

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As is evident from the graph depicting the cost of tickets, both the NBA and the NHL figured this out in the early 2000’s and cut prices by 18% and 14%, respectively. Unfortunately, prices have crept higher again, although they were slowed in the NHL by the lockout in the 2004-2005 season (but not by the 2012-13 lockout). In 2012, for the NBA, the average ticket price was actually .5% lower than in 2001, primarily due to the 18% price cut in 2002, but now is the highest it has ever been ($55.88). For the NFL, there was a slight dip from 2000 to the 2001 season (4%), but the price increases have continued unabated since then. By 2016, the average ticket cost $92.98 and was 96% more than in 2001. Although MLB is the cheapest ticket of the four major professional leagues (average ticket price was $31.00 in 2016), there has been a 76% increase in the average ticket price since 2001. As a point of reference, the inflation rate from 2001 to 2016 averaged 2.4% over the 16 years for a little over a 40% increase, or less than half the amount of a football ticket increase and slightly more than half of the amount of a baseball ticket increase. Is it any wonder why lower-income fans do not come to the games anymore? So, marketers have a difficult job ahead of them. As long as management keeps raising prices and fans have less discretionary income, marketers will be hard-pressed to convince fans and spectators that the product is worth their entertainment dollar. In addition, marketing the sport product is dramatically different than marketing a different kind of product. Sport differs from most other products in substantial ways, and marketers need to take those differences into account when trying to build relationships with fans and spectators. Let’s briefly discuss some of those differences here, but we will go into them in different ways in future chapters as well. Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior How Sport Differs from other Products Prior sport researchers and writers have identified a variety of different ways that sport differs from products like Coca-Cola or Levi’s. I’ve pulled the following 10 from a variety of places and list them here, but will discuss each a little more below.

1. Sport is simultaneously produced and consumed. 2. Sport is unpredictable and inconsistent. 3. Sport is experiential, subjective, intangible, and ephemeral. 4. Sport has a high degree of social facilitation. 5. Sport can elicit strong emotions and can be strongly personal. 6. The core product is beyond the marketer’s control. 7. Sport organizations simultaneously must cooperate and compete. 8. Sport consumers think of themselves as experts in the field/industry. 9. The price of the product can be a small part of the total cost or experience. 10. Indirect revenues can be greater than direct revenues.

Sport is simultaneously produced and consumed. Unlike a can of Coke or a pair of Levi jeans, which are produced in a factory somewhere and then shipped to a store or to a warehouse and then directly to you, the sporting event (game) is produced on a field or in an arena (or elsewhere) and the consumer consumes the game when the game is played either at the venue or through media. Obviously, there are exceptions through media consumption if the broadcast is delayed or if the consumer records it to watch later. However, in most instances, the fans consume the sport event at the time it is played (produced). Sport is unpredictable and inconsistent. Unlike other products, sport is unpredictable; fans don’t know the outcome ahead of time. If you buy a can of Coke you pretty much know what you are going to get. However, it is obvious that sporting events aren’t predictable, otherwise there wouldn’t be people betting on various outcomes before they happen. For example, last night, the Cleveland Cavaliers (one of the best teams in the NBA) played one of the worst teams (Sacramento Kings). Cleveland was favored by more than 10 points and ended up losing by 14; an unexpected and unpredictable outcome. In addition, it was inconsistent as all sporting events are. The game is never the same, even if the same players play on the same field on consecutive nights. However, most other products are consistent. If I go buy a can of Coke today and another next week, they are going to be pretty much the same both times. Sport is experiential, subjective, intangible, and ephemeral. To a degree, sport is much more experiential, subjective, intangible and ephemeral than other products. Yes, you have an experience when you drink a can of Coke or wear a pair of Levi’s, but that experience pales in comparison to when you go and watch a game, especially in-person. In addition, the subjectivity of that experience is considerably greater than with other products, usually. If you and I go to a Seattle Seahawks game, I might enjoy it tremendously (especially if they win) and you might dislike it intensely because you hate football, the Seahawks, and being outside in the rain in December in Seattle. Yes, we might also disagree about that can of Coke as well. It is one of my favorite beverages and you might detest it, but games still are typically more subjective. Sporting events are also more intangible than most products. You can’t touch a sport game. It Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior doesn’t have a physical presence. Most other non-entertainment products have a physical presence. Similarly, most other products last longer than a sporting event. Sports are ephemeral, meaning they are transient, fleeting, only last a sport time. Most tangible products hopefully last a lot longer than a 90-minute football match, although the toys my nephews received at Christmas sure didn’t. Sport has a high degree of social facilitation. Unlike most other non-entertainment products, people usually consume sport with other individuals and part of the consumption process is interacting with others; making comments to friends and family, or just other spectators, about plays, poor reffing, whatever. These interactions are a big part of why people consume sport. In most of our research, only about 10% of people will watch a sporting event by themselves. The social interaction is a big component of the experience. Most other products do not have that type of social interaction as part of the experience. Sport can elicit strong emotions and can be strongly personal. I’ve kind of alluded to this before, but sport typically elicits much greater emotions when people consume it compared to other products. During an exciting game, people will be cheering, yelling, booing, screaming, etc. Most other products don’t elicit the same depth of emotion, although some other entertainments do, and my wife does get rather emotional about double chocolate cheesecake. Obviously, the more attached people are to ‘their’ team, the more personal the consumption of the game is as well. The core product is beyond the marketer’s control. This is a big one. Marketers have no control over the core sport product (for our purposes we are going to consider the game on the field/court and the core product). Marketers can’t predetermine the quality of play or whether the team will win or lose. Therefore, they can’t necessarily market those things and if they do they run the risk of being wrong, which is much worse, as we will discuss when we talk about expectancy disconfirmation. For other products, marketers know ahead of time what the product will be, what it will look like, how it will perform, etc. They can plan for these things. Sport marketers can’t do these things. They must focus on the peripheral aspects that are more controllable. Sport organizations simultaneously must cooperate and compete. Unlike most competing products or brands, sport organizations must cooperate with each other. Coke and Pepsi don’t need to cooperate to sell their products. However, teams can’t play matches by themselves, they need someone to play against, so they must cooperate to some extent to produce the product on the field. Marketers sometimes collaborate to market the game, or the league has marketers that promote the games between competitors. This doesn’t happen with non-sport products.


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Introduction to Sport Consumer Behavior Sport consumers think of themselves as experts in the field/industry. Unlike most other industries, sport consumers think they are experts and can coach better than the coaches and sometimes think they can play better than the players. Sport fans are always secondguessing play calls or player decisions by coaches, that’s why rotisserie leagues are so popular. Some fans irrationally think that they can do better than the players themselves. I think that’s why some of the video games are so popular; it gives the fans an opportunity to ‘pit’ themselves against ‘professional’ players. This doesn’t happen in most other industries. The price of the product can be a small part of the total cost or experience. We have kind of talked about this earlier in the chapter and we talk about more later in other chapters, but the cost of going to the game might only be a small part of the total cost of the experience. For example, I can pick up a Seattle Mariners ticket for US$10, but if I drive and park it is going to cost me another $30 for mileage and parking. If I get hungry and thirsty (gotta have my $6 Coke at the game), I could easily spend another $30 on food and drink. Need a program. Another $6. Need a new Mariners hat and maybe a shirt. Another $50 at least. And so on. That $10 for a ticket, just morphed into a $125 experience. Most other products aren’t 10% of the total cost of the experience. Indirect revenues can be greater than direct revenues. Finally, for sport organizations, the indirect revenues gained can be greater than the direct revenues. For most of the big professional leagues around the world, other revenue sources, such as media contracts, bring in more money than ticket sales. This doesn’t happen with other products or in other nonentertainment industries. The point being is that marketing sport is entirely different than marketing other products and dealing with sport consumers is entirely different than dealing with consumers of other products. Now don’t get me wrong, there are similarities as well, and there are certainly foundational principles that are the same, but if someone has no understanding of sport and the sport consumer, there is no way that they will be successful creating relationships with the fans and marketing the sport product. Summary The figures and information presented in this chapter clearly illustrate that people like to attend sporting events, watch sports through media, discuss it through social media, and buy team licensed merchandise. Consumers have a direct impact on sport organizations through their attending, viewing, and purchasing behaviors. Consumers also have an indirect impact in terms of the attractiveness of a consumer base for prospective sponsors and other business partners. The stagnant attendance numbers Copyright 2018 Galen Trail

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Introduction to Sport Consumer Behavior and the increasing costs of attending and watching sporting events and buying merchandise suggest there will be even more competition in the future for sport consumers’ time and money. Moving forward it is imperative that the people working in sport organizations and sponsors of sport teams and events better understand why spectators consume and how to get them to consume more. As you continue reading, it is important to understand that this book is designed to help us better understand sport consumers. In the following chapters we present content on sport consumer behavior in general, theories of consumer behavior, the role of segmentation for identifying key consumer groups, and discussion of how people become sport consumers in the first place. There are also chapters examining the motivators of sport consumer behavior, including influences such as culture, context, personality, individual needs and values, attitudes, and the organizational environment. This book is not a sport marketing textbook, though there are several points at which I do discuss implications for sport marketing as I did above. The two topics are intertwined because understanding sport consumers better is an important element of sport marketing. The primary goals, however, are to aid those working with, or who will be working with, sport consumers to understand why fans and spectators do consume, and to equip readers with the tools to develop strategies to stimulate more sport consumption in the future. In the next chapter I present a model of sport consumer behavior that provides the framework around which the key issues and topics are presented. The model also provides the framework around which the remaining chapters are structured. So game on…


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Galen T. Trail

Theories of Consumer Behavior

Chapter 2 Theories of Consumer Behavior

Definition of sport consumer behavior

The American Marketing Association’s (2018) dictionary of terms has the following entry for consumer behavior, “The dynamic interaction of affect and cognition, behavior, and the environment by which human beings conduct the exchange aspects of their lives.” A textbook definition of consumer behavior is generally, “The study of individuals, groups, or organizations and the processes they use to select, secure, use, and dispose of products, services, experiences, or ideas to satisfy needs and the impacts that these processes have on the consumer and society” (Hawkins, Best, & Coney, 2004, p. 7). So, what do the definitions really mean? What is consumer behavior? A simple definition of consumer behavior could be, “The study of why people buy.” The study of consumer behavior is essentially an attempt to understand what influences a person’s decision to consume a product. The AMA entry illustrates that consumers are influenced by their own thoughts and Copyright 2018 Galen Trail

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Theories of Consumer Behavior feelings, other people, and the environment or situations in which they live. As I illustrated in the first chapter, it is important for professional sport leagues and university athletic programs to at least maintain a base of sport consumers, and in more cases than not, for organizations to work on increasing their levels of attendance, viewership, social media and merchandise consumption. The competitive environments in which most sport teams and leagues operate make it a necessity to better understand consumer behavior and the factors influencing such behavior. A study of consumer behavior can include individuals, groups, or organizations; I will focus on the individual consumer. More specifically, I will consider in depth sport consumer behavior. A simple way to define sport consumer behavior is to modify the formal definition so that the focus is on sport products: The study of the buying units and the exchange processes involved in acquiring, consuming, and disposing of sporting goods, services, experiences, and ideas. The study of sport consumer behavior can be very broad and include efforts to understand the acquisition, consumption, and disposal of some product. We are going concentrate first on the acquisition process, or how consumers get to the point of purchasing or consuming a particular sport product (e.g., a ticket to a game, a team jersey, watching a game on TV, or streaming a game on a smart phone), and then consider how experiences associated with consuming the sport product influence future choices and behaviors.


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Theories of Consumer Behavior Applications of sport consumer behavior – why study human behavior?

Hawkins, Best and Coney (2004) noted that all marketing decisions and regulations should be based on knowledge about consumer behavior. They suggested there are four main reasons to understand consumer behavior. First, to be successful, businesses need to base marketing decisions on knowledge, research and theories about consumer behavior. Second, organizations need to collect information about their consumers, so that they understand how to market to them, how to segment them, and how to establish long-lasting relationships that make a satisfied and loyal customer. Third, organizations need to understand that consumer behavior, and the motivations to consume, is a very complex process, that varies across individuals. Fourth, if organizations understand the motivations and influences that affect consumption behavior, then marketing practices can be designed to influence and shape that behavior to the benefit of the organization. I will discuss each of these in more detail below. Marketing decisions—Creating better marketing campaigns to satisfy target consumer needs/desires and to deliver greater value to consumers. As Hawkins et al., (2004) noted, decisions based on research and theory “are much more likely to be successful than are decisions based solely on implicit intuition” (p. 9). Unfortunately, in the world of sport, many marketers and managers do not make decisions based on research. They either do what they have always done historically in that particular organization, or if they do try something new, it is something they heard about from a friend of theirs at another organization. Typically, there is no evidence that these marketing or managing decisions actually work and increase return on investment (ROI). Consider the example of an intercollegiate athletic department that gave away tshirts as a promotional item in an effort to increase attendance. The sponsoring organization was a local business whose product had no meaningful connection to the team or sport. The shirts advertised a hair salon and did not even have the team logo on the shirt. Very few shirts were taken when handed out, and most that were received were left in the stands after the game. Another athletic department spent $140,000 advertising one of their non-revenue producing teams. Research after the season determined that the advertising did not influence attendance in the slightest, did not improve intentions to attend, did not improve perceptions about the team, and did not even increase awareness about the team (awareness was already almost 100% in the local market). A lack of research and knowledge of theories about the effectiveness of advertising campaigns in certain situations caused this athletic department to


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Theories of Consumer Behavior waste $140,000. The athletic department literally would have saved money and generated greater attendance numbers if they had paid people to come to the games instead. Consumer information – Collection of information about the consumers that will allow for segmentation. All organizations that sell to consumers recognize the need to collect and analyze consumer information in order to better understand their consumers. Unfortunately, many sport organizations have been slow, or have actually failed, in collecting consumer information. When information is collected, it may only be the basic information necessary to contact people for billing purposes. For example, recently I was helping a professional team with some market research. They had contact information for most of their fans, but it was split among three different databases (Access, Excel, and one I had never heard of). They had some demographic information in the Excel database, but it was not connected to the contact information, so was pretty much useless. Unfortunately, demographic information will not help segment the team’s consumers into useable market segments because research has shown that consumers typically do not differ much on their beliefs, attitudes, intentions, or behaviors, based on demographic variables, as we will show in future chapters. Cutting-edge sport organizations have focused on collecting information about psychographic variables that are much better predictors of actual behavior and allow the markets to be segmented more usefully. Advertising can be more effective when it is designed to reach specific market segments. The difficulty arises in how to collect information, how to store it, and how to analyze it once it is collected. Now, more than ever, it is requisite that franchises have people dedicated to doing exactly this. Luckily some franchises have realized this and are on the way to being able to use collected data more efficiently than in the past. If you read any modern marketing textbook you will learn that one way to store and easily access information about consumers is with some type of management information system (MIS). A management information system is simply a plan for collecting, storing and disseminating information. Any data base program can be used to maintain consumer information records. The challenge as we noted is having a plan to actually utilize the information. The Phoenix Suns offer a good example of collecting and utilizing information with their customer relationship management (CRM) system. The system implemented by the Suns maintains one file for each customer; the file includes information about all transactions and interactions a consumer has with the organization. The system links all business units together and information is accessible to the various units based on particular security protocols. Demographic information collected when a person purchased his or her season tickets is included in the record, along with information about tickets purchased. When the consumer uses his or her credit card to purchase concessions or merchandise at the ballpark, the record is updated with consumption behavior. Going further, if a fan has a team affinity card and participates in the rewards program, she or he will answer periodic questions using the ballpark kiosks that will update information in the record and add whatever content is queried, including psychographic measures. More and more sport organizations are realizing the benefits of CRM programs and are using them; some organizations are considerably more effective than others in doing this. In the case of the Suns organization, the CRM system is maximized through the linkage of multiple organizations – the Phoenix Suns, the Phoenix Mercury, the Arizona Rattlers, and Talking Stick Resort Arena (formerly US Airways Center). A consumer’s transactions and interactions with all of the linked Copyright 2018 Galen Trail

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Theories of Consumer Behavior organizations are maintained in one record. The linked organizations are able to maximize cross-selling and up-selling of products. The in-depth information enables the organizations to maximize sponsor relationships, guest relations programs, and community relations activities. The Phoenix Suns and their partners are, however, an exception to the norm. Many athletic departments and professional franchises are not anywhere close to collecting and utilizing in-depth consumer information in an effective and efficient way. In addition to developing and implementing a plan to collect and utilize consumer information, it is just as important to appreciate the unique features of sport consumers. Organizations daunted by the task of collecting, analyzing, and utilizing information hire marketing firms that charge a large amount of money and yet do not understand the unique characteristics of the sport market as I pointed out in Chapter 1. Consequently, the “hired guns” are more often than not providing sport organizations with information and analysis that is either irrelevant or in some instances flat out wrong. These marketing firms need to understand the theory behind sport consumption and the relationships among the constructs that explain actual sport consumption, rather than just assuming that knowledge about consumption of jeans, cars, and soda pop is applicable to the sport context. Understanding consumer behavior—Comprehension of the values, beliefs, attitudes, goals, motivations, and situational characteristics that explain sport consumer behavior. As noted above, too frequently sport organizations have no idea why sport spectators and fans consume their products. It is necessary to comprehend what motivates people to consume sport. As we will discuss later in the chapter and in the rest of the book, peoples’ values, beliefs, goals, and attitudes are all potential motivating factors for why people consume sport. However, just understanding that is not sufficient. Understanding the relationships among the different motivating factors and how these motivators may be influenced, either increased or decreased by situational factors, is critical to explaining consumer behavior. Knowing a person’s values will not necessarily help a marketer understand why that person came to today’s game. However, understanding how a person’s values influenced their beliefs or attitudes toward the team, and how those attitudes might be modified by environmental constraints, will help the marketer understand what satisfies the consumer and will enable the marketer to create a greater connection to the team/organization, thus increasing repeat patronage. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Using the type of knowledge just described, I helped an athletic department improve their marketing strategy by identifying why some people supported the women’s basketball team and attended games. I identified a segment of consumers and potential consumers who were motivated by the idea of supporting women’s causes. These people were motivated to connect with the women’s basketball team because they saw it as support for a cause that promoted the development of young women. They believed that through sport women learned how to become leaders, how to work as a team, and how to be disciplined individuals. Because of these beliefs, this segment of fans showed their support for the team by attending games, buying merchandise, and promoting the team through word of mouth. I suggested that the team develop this market by encouraging existing fans with these beliefs to bring like-minded individuals to the game. I suggested that the team hold a pre-game dinner with a speaker from the Women’s Sports Foundation and encourage all fans to come. Every fan that came was given a voucher for one free ticket to a future game with paid admission so that they could bring a friend to a game. Shaping consumer behavior—With knowledge of fan and spectator motivations and situational constraints for sport consumption, consumption can be modified and increased. As marketers gain a greater understanding of consumer behavior (i.e., what motivates consumers and how to satisfy consumers), they can develop promotional activities and/or advertising campaigns that have greater effectiveness in influencing people to consume more. In addition, as marketers and managers understand the environment surrounding the consumer and what may positively or negatively influence consumption, they are able to either change the environment in some situations or put a positive spin on aspects that sometimes appear to negatively influence consumer behavior. For example, some colleagues and I did some consulting for an Arena League football team and determined that many fans who attended with their children were very upset with other fans who were obnoxious, vulgar, and had too much alcohol to drink. We suggested the team create a “Family Friendly” zone where no alcohol could be served or consumed, where only families could sit, and where greater supervision was provided by ushers to moderate any potentially unruly fans. We also suggested that alcohol sales be stopped earlier and that the team create a Fan Code of Honor that would be promoted and encouraged. Some of these suggestions were implemented by the team and season ticket-holder retention increased. Copyright 2018 Galen Trail

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Theories of Consumer Behavior In sum, sport marketers need to understand why people consume sport, what motivates them to consume sport, what satisfies them and makes them a happy consumer, what makes them a loyal consumer, so that they can do a better job marketing to the consumer. That is, sport managers need to be able to understand how to segment the market better, create marketing (advertising) plans that are specific to the identified market segments, and thus the marketing plans should create a greater ROI. If managers understand consumers better, they can provide a more satisfying consumption experience, whether it is when the consumer attends the game, watches it on television, consumes social media, or buys some merchandise. Research has shown that building a relationship that the customer finds satisfying creates a more loyal customer who is less likely to defect during price increases or specific to sport, when the team is not successful.

The study of consumer behavior – Where we have been

The study of consumer behavior includes ideas drawn from disciplines such as psychology, sociology, anthropology, and economics. Writing about consumer behavior across time, Zaichkowsky (1991) illustrated how various disciplines have influenced the study and application of consumer behavior. During the 1940s, study of consumer behavior was influenced largely by economic theory. The “Economic Man” theory was widely held, which regards consumers as rational and calculating. From this perspective individual buyers are expected to purchase products that provide the most satisfaction relative to one’s preferences and price. As Belz and Peattie (2012) noted, from a marketing perspective, these early days could be summarized as “how to sell more stuff to people” (p. 13). The influence of psychology emerged in the 1950s when consumers came to be thought of as irrational, impulsive decision makers. In this era Zaichkowsky suggested that consumers came to be regarded as passive, open, and vulnerable, and subject to external influences. This period was also marked by the emergence of business schools and the training of researchers in business schools.

The growth of business schools produced a blending of economic and psychological perspectives, resulting in new theories of consumer behavior (Zaichkowsky, 1991). During this period Copyright 2018 Galen Trail

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Theories of Consumer Behavior the marketplace grew, and segmentation became a primary topic. Emerging in this time was the idea of the consumer as a “Cognitive Man,” one who actively searched for information about desired products. This era marked the emergence of many of the decision-making models which still direct the study of consumer behavior today and the emphasis in marketing toward understanding the customer’s needs and wants (Belz & Peattie, 2012). The period of the 1980s included a cluttered marketplace with literally thousands of products for consumers to evaluate, and consumers having less discretionary time to engage in extended decision making processes (Zaichkowsky, 1991). The view emerged that buyers were cognitive misers who had less time for shopping decisions and concurrently had greater choice in the marketplace. This environment focused the attention of researchers on individual decision-making processes. Moving through the 1990s into the 2000s, there has been increasing attention to globalization and the “shrinking” world. Ironically, the study of consumer behavior has considered not the increasing similarity across consumers, but the potential for greater diversity. Zaichkowsky (1991) noted that in the United States, the growing senior (or grey) market, the aging of the baby boomer generation, and the growth of “minority” populations, actually serves to create more differences relative to potentially different values and certainly different cultural roots which impact consumer behavior. de Mooij (2004) advocated that consumer behavior is not converging as a result of globalization, rather it is becoming more differentiated. Thus, there is an even greater need to study consumer behavior now than in the past, particularly the influence of individual and external forces on consumption. Belz and Peattie (2012) suggested that people interested in consumer behavior should help organizations focus on the relationship that is formed and maintained with the consumer. In addition, they suggested that there is a lack of fit between how we typically think of consumer behavior and how we use that information to market to the consumer. Belz and Peattie proposed that the ecological and social realities of the environment need to be taken into account. Extending this idea, it is important to study consumer behavior relative to specific contexts such as sport consumer behavior, but within the wider ecological and social environment. Copyright 2018 Galen Trail

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Theories of Consumer Behavior How to study consumer behavior

A variety of models, frameworks, and theories have been used to try and explain consumer behavior in general. However, very little research has been done to explain consumer behavior in sport. The following is a brief overview of some of the consumer behavior models.

A Variety of Models

Vakratsas and Ambler (1999) identified five different types of models that have been used to explain general consumer behavior: Market Response Models, Cognitive Models, Affective Models, Persuasive Hierarchy Models, and Low-Involvement Hierarchy Models. Each of these models has positive aspects and negative aspects, and some do a substantially better job of explaining and predicting consumer behavior than others. Furthermore, marketing supposedly has different influences on consumer behavior and influences consumer behavior in different stages depending on the model. Although Vakratsas and Ambler focused on the influence of advertising in many of their models, we are expanding their premises to include all marketing aspects, rather than just advertising. Market Response Models focus on repeat purchasing behavior. These models depict how advertising, price, and promotional measures directly influence sales, market share, and brand choice. Vakratsas and Ambler (1999), after a thorough review of results based on Market Response studies, determined that advertising only has a short-term effect, noting that 90% of the advertising effect is gone within 3-15 months. Advertising typically works better for durable products than nondurable products. A sporting event such as a baseball, basketball, football, or other type of game, in terms of attending a game or watching it on television, is a non-durable product (unless you record it). Sport merchandise, such as a team jersey or jacket would be considered a durable product. So, advertising is expected to be more successful for merchandise than for games. Advertising works better for new products/brands than for existing products/brands. Within sport, this would indicate that if a marketer is trying to increase awareness about a new team in a city, then advertising is probably going to be effective, and may influence sales. Although, as Vakratsas and Ambler noted, advertising has diminishing returns. The first exposure will increase sales, but after that advertising is considerably less effective, if at all. For established brands, advertising has little if any effect. In the case of a sport product such as a sports team, advertising may create awareness about the time or day of a game, but typically has no effect on whether people attend. This is especially relevant if the product is frequently purchased and the consumer already has a fair amount of knowledge about it. Finally, Vakratsas and Ambler noted that promotions typically have greater short-term effects than advertising, but the effects are even shorter than advertising effects. This is especially evident in sport promotions. Several baseball teams in both the major and minor leagues have noted that during the Copyright 2018 Galen Trail

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Theories of Consumer Behavior height of the Beanie Baby craze, people would buy a ticket to the game so that they could collect the Beanie Baby give-away and then leave before the game even started. These people certainly were not attending for the core product (the game). Luckily, most teams have learned from these mistakes and do not do such promotions anymore. Furthermore, promotions make non-loyal consumers more price sensitive than they were already. Some sports and or leagues frequently have two-for-one, or half-off, ticket nights. Individuals, who are not psychologically connected to the team, but enjoy going to a game once in a while for entertainment purposes, will wait for these promotions and take advantage of a price reduction. Sometimes this occurs frequently enough that people never have to pay full price if they do not attend that often. Once the consumers become used to the reduced prices, they never want to pay full price, and will not attend if they do not have some type of discount. Supposedly, advertising makes consumers less price sensitive and more loyal to the brand. If this is the case, then organizations need to weigh the positives and negatives of both advertising and promotions to determine whether the benefits outweigh the costs. The next two types of models that Vakratsas and Ambler (1999) categorized, Cognitive Information Models and Pure Affect Models, are antithetical to each other. Cognitive Models assume that consumer decision making is solely rational. The consumer evaluates a product and makes the choice to purchase or not based on the attributes of the product and how badly the consumer needs or wants the product. If there are two similar products, the consumer compares and contrasts the product attributes and makes a rational choice between the two. No emotion is involved, and advertising or marketing theoretically plays no part in influencing product evaluation other than by creating awareness or providing information about specific product attributes. This might work when comparing two different brands of green beans, but it certainly does not work in terms of consumption of most sport products. The Pure Affect Models are dramatically different from the Cognitive Models. The Affect Models focus on affective responses (emotional responses) and typically include no reference to cognition at all. With a pure affects model, consumers are believed to form preferences based on whether they like a product, and advertising is supposed to induce feelings or emotions that influence consumers to purchase the product. These types of models are impractical at best because some type of cognitive functioning is present in the consumer if only at the awareness-of-the-product level. However, what is important is that marketing that evokes emotions typically is effective to some extent, but certainly needs to be paired with cognitive appraisal of the product before consumption will occur. Furthermore, when consumers like an advertisement for the product, they are more likely to form a positive attitude toward the product. Persuasive Hierarchy Models are represented by the presumption that a hierarchy exists in which Cognition precedes Affect which precedes Behavior, and that marketing can “persuade” or influence behavior by moderating the relationships. Vakratsas and Ambler (1999) suggested that there are two mediators that intercede between advertising and the cognition-affect-behavior relationship: involvement with the ad and attitude toward the ad. However, there has not been a lot of support for these models because the relationship between affect and behavior has typically been low, with correlations (r) ranging between 0.00 and 0.30. One of the reasons for this low relationship might be due to intervening variables that were not considered, for example, intentions to consume the product,


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Theories of Consumer Behavior or constraints that prevent consumption of the product even though the individual would like to consume it. The final category of models is the LowInvolvement Hierarchy Models which consist of several types. The first is depicted by a Cognition ⇒ Experience ⇒ Affect hierarchy. Consumers are aware of the product, consume it, and then determine whether they like it or not. Experience with a product is the primary determinant in future consumption behavior. This type of model might work when involvement is low or if the product is inexpensive, for example, a candy bar. When Derek Jeter formerly of the New York Yankees came out with a candy bar, people might have bought it solely to see if it was any good. After eating it, they either liked it or not, which determined whether they would buy it again. Several other low-involvement models have been proposed as well, including all of those listed in Figure 2.1. Figure 2.1 – Low Involvement Hierarchy Models Conation is the intention to do something. So, the first model depicts that a consumer is aware of the product (Cognition), has some type of feeling for the product (Affect), and has an intention to consume the product or not (Conation). Although different combinations of these aspects have been proposed, some of them do not make a lot of sense in terms of sport consumption, specifically the ones in which Conation precedes either Cognition or Affect. Furthermore, as Peterson, Hoyer, and Wilson (1986) pointed out, “The question ‘did the person think first or feel first’ is not very meaningful. Individuals are always in a stream of thinking or feeling; therefore, it is irrelevant to say, ‘Are there any thoughts preceding affect?’ or ‘Is there affect preceding cognition?’ The important issue to be addressed is how affect and cognition interact to influence behavior” (p. 142). Copyright 2018 Galen Trail

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Theories of Consumer Behavior Models based on Interaction Effects Most models depict that affect and cognition interact on some level to influence conation, which then leads to behavior. However, many are predicated on Lavidge and Steiner’s (1961) hierarchy of effects model. In this section, we will give an overview of their model and then focus on several different interaction effects models that concentrate on motivation for consumption. Lavidge and Steiner’s (1961) Step Model of Advertising is a hierarchy of effects model, which means that consumers must progress through a sequence of stages before they end up purchasing the product. This model shows that initially the consumer is unaware of a product and therefore, through some means, must become aware of it. This could be through advertising as suggested by Lavidge and Steiner, or it could be by word of mouth from a friend or associate, among other ways. After the consumer is aware of the product, the consumer must learn what the product has to offer. Then the consumer must come to like the product and prefer it over other similar products available. Finally, before the actual purchase can be made, the consumer must desire the product and believe that the purchase will be a wise one. Figure 2.2 – Lavidge & Steiner’s (1961) Step Model of Advertising

According to Lavidge and Steiner the consumer must follow the progression of stages, but each stage does not have to be equally weighted. That is, the consumer may pass through some stages more quickly than others, and much more easily. For example, if the consumer is buying a car, the cognitive stages may take longer to progress through than the affective or conative stages due to the cost of the Copyright 2018 Galen Trail

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Theories of Consumer Behavior product. However, if the product is an impulse purchase that is relatively inexpensive, the consumer may not even be aware of passing through the first few stages and be at the emotional and conative stage rather quickly; the “I like it” and “I want it” stages when one is hungry and sees a candy bar. Most consumer behavior models are built on some type of hierarchy, but the focus and the type of stages differ dramatically. Furthermore, most models also suggest that interactions exist and that the stages do not exist in isolation. Motivation for Consumption Model Ratneshwar, Mick and Huffman (2000) developed a motivation for consumption model (Figure 2.3) based on the “who, Figure 2.3 – Motivation for Consumption Model what, when, where, why, and how” of consumption. Each of these aspects interacted to determine consumption. In the “Who” aspect, Ratneshwar et al. suggested that there are cross cultural, interindividual, and inter-group differences that exist in consumers. These differences interact with situational and contextual differences (the When & Where of the model) and the motives, goals, and desires (the Why of the model). The consumers’ cognitive and affective processes interact with the motives to determine consumption. To some extent this is cyclical; specifically, past consumption influences present motives, which then influences future consumption. Cohen and Warlop (2001) noted that one of the problems of looking at motives, per se, is that motives themselves are contingent on central traits, values, self-images, and desires; this is the “Who” aspect of Ratneshwar et al.’s (2000) model. In addition, motives tend to be salient, easier to measure, and more relevant than the more remote sources of the motives. For example, personal values may be important to consumption but are mediated by more salient aspects such as the desire to go with a group of friends to a game. The same problem exists with respect to the economic, social, and cultural context of the consumption. There may be economic influences, but typically they are mediated by the motives. If the economy is poor, that may have a negative influence on some people’s sport consumption behavior. However, some people will choose to make other sacrifices and still keep their season tickets with the Dallas Cowboys. The goal to be a loyal fan outweighs the economic impact of the ticket cost. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Means-End Chain Theory Means-End Chain Theory proposes a conceptual model for the cognitive organization, structure, and content of product knowledge in memory (Claeys & Abeele, 2001), and can be used to explain goal directed behavior. According to Pieters, Allen, and Baumgartner (1995), goal-directed consumer behavior is organized hierarchically, ranging from concrete observable movements to abstract, personal goals. A goal is identified by the individual, for example, losing weight, as is depicted in Figure 2.4. However, the goal does not exist in a vacuum. There are reasons (the motivation) that someone might want to lose weight, and then there are the practicalities of how to actually accomplish the goal. Losing weight can be accomplished by a combination of dieting and exercising. However, there are several ways to go about dieting. The individual might decide to buy “light” products, actually use them (rather than just try them once, decide they are nasty, and leave them in the refrigerator until they go bad), and try to not eat any snacks. There are many ways to exercise and our example consumer in the figure decided to play tennis and walk to work. Walking to work was also motivated by being able to save money. Exercising is specific to the goal of losing weight, but also has a secondary motive of being fit. Losing weight, in turn is motivated by being healthy and wanting to be seen as attractive. Being attractive has a higher order motive of wanting to be liked by others. As is apparent, the motives, goals, and processes used to achieve those goals can possibly Figure 2.4 – Means-End Chain Theory be an ever-spreading network. It is possible to figure out


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Theories of Consumer Behavior other goals that could easily fit within this diagram and be compatible with these motives. Additional higher order motives could also be found. The point is that when an individual chooses a goal, that goal is motivated by something (typically a higher order goal or motive). In addition, the individual needs to determine the processes by which the goal can be achieved. However, different individuals can have the same goal, be motivated by different things, and use dramatically different processes to achieve the goal. Hierarchical Model of Consumer Goals Another model that examines motivation for consumption is Huffman, Ratneshwar and Mick’s (2000) Hierarchical Model of Consumer Goals (Figure 2.5). They suggested that individuals have six hierarchically ordered goal levels: Life Themes & Values, Life Projects, Current Concerns, Consumption Intentions, Benefits Sought, and Feature Preferences. Those goals that are more central to the individual’s existence and represent the individual’s personal ideals are labeled “Being.” Those goals that are immediate, or in the present, are represented by the concept “Doing.” The goals that focus on the external entity’s attributes are labeled “Having.” Life themes are based on an individual’s personal history. They are an accumulation of the experiences, beliefs, and values of the individual, which the individual tries to incorporate into a coherent whole. Both life themes and values are invariant, stable, and represent the core conception of the self. They also guide selection of lower order goals and behavior. Huffman et al. (2000) suggested that life projects are the creation and Figure 2.5 – Hierarchical Model of Consumer Goals


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Theories of Consumer Behavior maintenance of key role identities. These roles vary to some extent over the life span as events change. Let us use Joe Fan as an example. When Joe got married, he took on the role of husband. When his first child was born, he took on the role of father. When he got his first coaching job, he took on the role of coach. Each of these identities can change to greater or lesser extents based on situations and life events. They also interact to influence each other. For example, Joe decided that his roles of coach, father, and husband all required a lot of time to do each well. Based on his values he decided he could not continue to coach and be a good husband and father. So, he quit coaching and chose to get his Ph.D. in Sport Management so that he could become a professor at a university. He felt that being a professor would not require him to be away from his family as much, but he could still have a job focusing on sport. By quitting coaching, his identity of “coach” was eventually snuffed out. Current concerns are defined by Huffman et al. (2000) as short-term activities or tasks. These are current activities that an individual is trying to accomplish but are typically directed by the values and role identities of the individual. For example, if Joe Fan decided to write a textbook, that would be a current activity that is guided by his identity (life project) as a university professor. Huffman et al. (2000) defined consumption intentions as an individual’s intent to consume a particular product. These intentions are predicated on the current concerns, life projects, and values of the individual. If we continue the example above, to write his book (Joe’s current concern), he needed to buy a better computer (that’s his story and he is sticking to it, despite what his wife thinks!). The next two stages of Huffman et al.’s (2000) model are similar and differ only in terms of abstractness. Benefits Sought are the consequences desired from the ownership and usage of the product to be consumed. They are subjective and outcome-referent. Feature Preferences are the desired product features that can be evidenced in concrete or financial terms and are relatively objective and specific to the product. For example, after having decided that he needed a new computer, Joe had some ideas about what kind of benefits he wanted to get from the computer. He wanted something of good quality, reliable, inexpensive, and fast. These were the subjective benefits sought. After researching various computer options, he determined the specific feature preferences that he wanted. He decided on a Dell® because a well-respected consumer magazine indicated that only 16% of consumers who owned Dells reported needing repairs or having serious problems, which was the third best rating. The Dell was the least expensive of all similarly rated computers with the features that he wanted. Furthermore, in that price range the Dell had the fastest processor. As is apparent, benefits sought and feature preferences are very closely tied together. However, there are several limitations with this model, as there are with most. The first is that it addresses only goal-driven behavior, not autotelic (having a purpose in itself) behavior (such as sports attendance). Second, the framework is mainly cognitive and does not include affective (feelings) dimensions to a large extent. Third, some of the dimensions may not be distinct, or at least are very similar. There are additional theories that are not specifically consumer behavior oriented that I have used to explain behavior and I give a brief overview of some of them next. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Identity Theory The concept of identity is based on the symbolic interactionism of Mead (1934) in which the self is made up of interdependent and independent, mutually reinforcing and conflicting parts. One definition of identity is the, “parts of a self-composed of the meanings that persons attach to the multiple roles they typically play in highly differentiated contemporary societies” (Stryker & Burke, 2000, p. 284). How we think about identity comes in part from James’ (1890) idea that people have as many identities as distinct networks of relationships in which they occupy positions and play roles. Identities are cognitive schemas that reflect internally stored information and meanings that serve as frameworks for interpreting experience. Typically, people’s identities are organized in a hierarchy reflecting the importance of the identity to the particular individual and identities are usually stable across time and situations. However, sometimes in certain situations the hierarchy of importance changes; an identity becomes activated and comes to the forefront. This is called identity salience and is defined as the probability that an identity will be invoked in a certain situation. For example, Joe Fan is a father, he is a Seattle Seahawks fan, he is a husband, he is an employee, he is a brother, he is a friend, and he is a partier. All of these are roles or identities that he has. If you were to ask him which role was most important to him he might say husband or dad. If you asked him which role is least important to him he might say partier. However, on game day, the roles of fan and partier may come to the forefront; that is, those roles become more salient because of that particular situation. That does not necessarily mean that the other roles change in the hierarchical order within the individual, it just means that at this particular time Joe is focused on being a fan. However, if Joe was at the game and he received a call on his phone from the baby sitter that his 1-year old daughter was sick, the role of Dad would come to the forefront, replacing the role of fan. Joe would immediately leave the game and go home to take care of his daughter. The identity is internal, consisting of internalized meanings and expectations associated with the role. The role itself is external and is linked to social positions with the social structure (Stryker & Burke, 2000). Each role or set of roles is embedded in one or more of a variety of groups that provide context for the meanings and expectations associated with that role. People typically are embedded in multiple role relationships in multiple groups and they hold multiple identities. These roles may reinforce one another, but often do not. When they do not, conflicts can arise that create cognitive dissonance. Let us go back to our example with Joe Fan above. Joe is at home taking care of his sick daughter, being a good dad. Monday rolls around and Joe’s daughter is still sick. He calls in to work and says that he needs to stay home with his daughter. The boss tells him to get in to work because he has a very important presentation to give to some clients that afternoon. His boss tells Joe that if he does not give the presentation he is fired. This example shows a situation when two roles are in direct conflict with each other. Does Joe stick with the role of father and stay home to take care of his sick daughter, or does he switch to the role of employee and give the presentation to keep his job. Obviously, this situation will cause a lot of stress for Joe, that is, cognitive dissonance. Copyright 2018 Galen Trail

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Theories of Consumer Behavior The Identity Theory Model consists of four central components: Identity Standard, Cognitive Comparison, Perceived Situational Meanings, and Behavior (Figure 2.6a). The identity standard is the self-perceived role Figure 2.6a – Identity Theory Model that the individual has, for example “fan.” Let us say that Sally believes that a fan should act in a certain way (watch games, support the team) and have certain attitudes (be loyal to one’s own team, dislike the rival). Sally also perceives that in certain situations fans are expected to do certain things because of that particular environment; for example, yell at the officials if there is a bad call against the fan’s team, be happy and perhaps go celebrate with a beer after upsetting the rival. The perceived situational meanings are typically socially determined or influenced. However, sometimes the identity standard and the perceived situational meanings do not match. In our previous example, if Sally does not like to drink, there is a disconnect with what her identity standard is for being a fan and what she perceives many other fans do after a big win. Sally will either modify her own standards to mesh with those of the social group that she is “hanging with” or Sally will change social groups to one that matches her own standards. Whichever she chooses, a behavior will result, but it will differ based upon her choice. Stryker and Burke (2000) call this type of behavior goal-directed. If Sally chose to find a different group of friends, her choice would change the situation she would find herself in the next time she went to a game. She would instigate the change in order to match meanings to her standards. Motives and expectations influence the interaction between the identity standard and the situational meanings, resulting in the cognitive comparison (Stryker & Burke, 2000). For example, Sally’s motivation to watch the women’s Olympic gold medal basketball game was to see aesthetically pleasing, high-quality basketball. Her expectation was that because it was the gold medal game (perceived situational meaning), the play would be aesthetically pleasing and of high quality. Her identity standard is that of a basketball fan. As the game progressed, she cognitively compared the game to that of her identity standard. She did not think the quality of play was very good nor was the game aesthetically pleasing. Because the game did not meet her expectations about what motivated her to watch it in the first place, her behavior changed. She stopped watching it. Thus, the model can be expanded to look like this. Emotion can be incorporated directly into the model also (Stryker & Burke, 2000). An increasing discrepancy between the identity standard and the perceived situational meaning results in a negative


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Theories of Consumer Behavior emotion. A decreasing discrepancy would result in a positive emotion. Thus, the resulting model would look like Figure 2.6b. Positive emotions influence behavior that supports the identity. If the identity is confirmed the importance of the Figure 2.6b – Identity Theory Model identity will be reinforced. If not, then the importance of the identity is likely to diminish (Stryker & Burke, 2000). For example, Sally goes to a game to support her team the Tampa Bay Rays against the Boston Red Sox. Based on the historical lack of success that the Rays have had against the Red Sox, Sally is not holding out a lot of hope for a Rays win. However, the Rays pull off the upset and beat the Sox. Sally is jubilant, so she decides to go the second game of the series as well. Her identity as a fan of the Rays is being reinforced. She went to a game and she enjoyed herself. Thus, she decided to go to another game. As we noted earlier, these roles are typically hierarchically ordered. Tsushima and Burke (1999) distinguished between lower-level roles which pertain to programs of behavior, and higher-level roles which pertain to general principles and values that guide the lower-level roles. So, if we incorporate values into the model and also include identity salience and importance from above, the model looks like Figure 2.6c. The interaction of all of these variables theoretically explain people’s behavior. Figure 2.6c – Identity Theory Model


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Theories of Consumer Behavior Social Identity Theory Social identity theory varies from identity theory on several key points, but they are also similar in many regards. In social identity theory, “a social identity is a person’s knowledge that he or she belongs to a group” (Stets & Burke, 2000, p. 225). Thus, the identity is a common identification with a collectivity or social category and has focused on category-based identities (e.g., Black or white, Christian or Muslim). An identity within identity theory focuses on role-based identities such as student, teacher, father, son. As is evident, the identity of fan can certainly be category-based or role-based, thus, both social identity theory and identity theory have been used to examine the identity of “fan.” As Stets and Burke noted, self-categorization and social comparison are two important processes involved in social identity formation. Specifically, they suggested that: The consequence of self-categorization is an accentuation of the perceived similarities between the self and other in-group members, and an accentuation of the perceived differences between the self and out-group members. This accentuation occurs for all the attitudes, beliefs and values, affective reactions, behavioral norms, styles of speech, and other properties that are believed to be correlated with the relevant intergroup categorization (p. 225). However, the reason that we have chosen identity theory rather than social identity theory is because social identity theory does not explain variance within the collectivity; in other words, it does not explain why fans act differently from each other. As the identification model above depicts, fans act differently because the identity standard (role identity) is specific to the individual and varies to the extent that the individual wants it to vary relative to the perceived situational meanings. Despite this drawback, social identity theory has been used extensively within sport research (including by yours truly, before I smartened up) to explain why people are fans. Theory of Planned Behavior According to Azjen (1991), the theory of planned behavior is an extension of the theory of reasoned action, primarily through the addition of the construct of perceived behavioral control. As in the theory of reasoned action, intentions are central to the model in the attempt to predict behavior. Obviously, the theory of planned behavior focuses on behavior over which the individual has at least some level of control, that is, the individual has volition and is planning to do the behavior. For example, Joe Fan intends to go to the game, and later in the day he actually attends the game. However, as Azjen (1991) points out, although most behaviors meet the requirement in which if a person decides to do something then they will probably carry out that decision and actually do the behavior, there are situational factors that come into play that may decrease the likelihood of accomplishing the objective and decrease or eliminate the likelihood of actually carrying out the behavior. For example, at the end of the season, Joe Fan intended to go to the playoff game, however he could not get tickets, the game was sold out, and scalpers were asking $500 per ticket, which was more money than Joe had in his life savings (Joe does not plan his finances well either, obviously). Thus, even though Joe intended to go to the game, due to the situation and a lack of planning on Joe’s part, he was not able to go. Azjen (1991) suggested that there were three factors that influenced behavioral intentions and then behavior: perceived behavioral control, subjective norms, and attitude toward the behavior. Perceived behavioral control refers to “the perceived ease or difficulty of performing the behavior and it is assumed to reflect past experience as well as anticipated impediments and obstacles” (Azjen, 1991, p. 188). This idea is similar to Bandura’s (1977, 1982) idea of perceived self-efficacy. An individual who is Copyright 2018 Galen Trail

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Theories of Consumer Behavior high in self-efficacy believes that she can accomplish whatever the specific task is. The idea of selfefficacy is similar to self-esteem, except that self-efficacy is typically situationally specific, whereas selfesteem is general. For example, Sally Super Fan is very confident about her abilities in general; she has high self-esteem. On game day, Sally and her friends go to the arena. A business is sponsoring Make-aSign-Night, where fans can draw pictures or make slogans on their signs and get on TV (of course the sponsor gets their name plastered all over everywhere as well, in addition to getting TV time on the signs that the fans make). Sally immediately grabs some pens and draws a good picture with a slogan. All of her friends are impressed with her ability. In that specific situation, Sally had high self-efficacy in her ability to make a sign. As she and her friends make their way into the arena, they come across a sign-up for the halftime contest in which the contestant has to dribble through an obstacle course and then shoot a three-point shot. Sally’s friends immediately try to get Sally to enter, but she will not. When Figure 2.7 – Theory of Planned Behavior Model

asked why, she finally admits that she is not a very good dribbler (she was a center in high school and the team would not let her bring the ball up court she was so bad). Because of her poor dribbling skills, she is afraid that she would embarrass herself in front of the crowd. In this situation, she has low selfefficacy in terms of dribbling. Individuals who have low self-efficacy in many situations, probably will have low self-esteem as well. In a review of several studies, Azjen (1991) determined that when individuals felt that they would be able to perform the behavior with ease, then they were more likely to do the behavior if they intended on doing so. Or put another way, if the behavior poses no serious problems of control, intentions will predict the behavior fairly accurately. The second antecedent to intentions is the attitude toward the behavior and “refers to the degree to which a person has a favorable or unfavorable evaluation or appraisal of the behavior in question” (Azjen, 1991, p. 188). In other words, it is how much the individual likes doing the behavior. Azjen determined that the attitude toward the behavior predicted intentions about as well as perceived control, and in some instances better. This really should not be surprising as the two variables are typically fairly highly correlated with each other. It makes sense, if the individual believes that he is good at something, he is probably going to like to do it. For example, Joe Fan likes to tailgate before the game. He thinks that he is good with the barbeque grill and his friends seem to eat his ribs readily enough. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Because he thinks he is good at grilling and his friends agree, Joe really likes to grill. He loves the compliments. The third antecedent to intentions is termed subjective norm, and it is a social factor that refers to “the perceived social pressure to perform or not to perform the behavior (Azjen, 1991, p. 188). Azjen has found that even though subjective norms are typically not as good predictors of intentions as the other two factors, in some instances, they are better predictors than perceived behavioral control. Azjen listed a study that found that the social pressure to use condoms was a better predictor of intentions to use condoms than perceived control. Azjen also listed another study that showed that the social pressure to commit multiple traffic violations was a greater influence than attitude toward committing traffic violations. However, in most other instances, subjective norms did not contribute to explaining intentions well. In the sport fandom world though, subjective norms (social pressures) are often seen in people’s behavior. This is often called social facilitation or known as going along with the crowd. For example, Joe Fan finally figured a way into the playoff game (do not ask him what happened to his car). During the first quarter, the people near Joe started heckling the referee over perceived bad calls. Because everyone around Joe was doing it, he decided to do it as well. However, when everyone else stopped during a lull in the crowd noise, Joe yelled out something derogatory at the official in a very loud voice that everyone heard, including the referee. Security came and escorted Joe out of the arena and banned him from the game. Attitude Theory According to Eagly and Chaiken (1993) attitudes motivate behavior and they defined attitude as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (p. 1). Eagly and Chaiken also defined psychological tendency as “a state that is internal to the person” (p. 1) and defined evaluating as “all classes of evaluative responding, whether overt or covert, cognitive, affective or behavioral” (p. 1). For example, Sally graduated from the University of Tennessee and was good friends with the former women’s basketball coach Pat Summit. Sally’s attitude toward the women’s basketball team (the entity) at Tennessee was very favorable (positive). Based upon her experiences at Tennessee she evaluated the women’s basketball team cognitively (noted the associations that she had both with the university and with the coach); she also evaluated the team affectively (when she was around the team she felt happy); and she evaluated the team behaviorally (she enjoyed herself when she went to basketball games). Evaluation is probably the most critical aspect of attitudes. Most people typically form an attitude about all entities that they happen to come across in life. Entities can be tangible (the women’s Copyright 2018 Galen Trail

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Theories of Consumer Behavior basketball team) or intangible (racism). When an individual forms an attitude the individual typically ascribes two level of goodness or badness to the entity. Joe Fan likes the Seahawks and hates the Cowboys. The Seahawks are good, and the Cowboys are bad. When Joe evaluates the Seahawks, his responses are typically those of approval, favor, liking and attraction. However, when Joe evaluates the Cowboys, his responses are typically those of disapproval, disfavor, disliking, and aversion. Attitudes are comprised of both valence and intensity. The valence or direction of the attitude refers to the positive or negative evaluation of the entity. The intensity or extremity of the attitude refers to the degree of the feeling: slightly, moderately, or very. There exists a neutral point between positive and negative attitudes (Figure 2.8). Eagly and Chaiken (1993) prefer to look at Figure 2.8 – Attitude Valence and Intensity

attitudes that fall on the neutral point as a degree of evaluation that may not have either valence or intensity, but still is evaluative (p. 11). The object of the evaluation, in addition to being called an entity is also termed an attitude object. Once an individual has evaluated an object and assigned some type of favorable or unfavorable meaning to the object, the individual’s attitude toward that object persists for at least a short time, and according to Eagly and Chaiken (1993), energizes and directs behavior. For example, Joe Fan was watching his Seahawks play the Washington Redskins and during the game a Redskins linebacker hit the Seahawks quarterback late and out-of-bounds, drawing a penalty. Joe was incensed. He immediately assigned a very negative meaning to the linebacker and developed a very intense negative attitude toward the linebacker. This negative attitude toward the linebacker was evidenced in Joe’s behavior toward the player. Joe shouted things at the player that cannot be printed in a textbook. Now every time Joe hears that player’s name he gets upset and complains that the player should have been thrown out of the game. This attitude toward the player will last some time, but eventually will fade. Attitudes have three different types of antecedents: Copyright 2018 Galen Trail

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Theories of Consumer Behavior cognitive, affective, and behavioral (Figure 2.9). According to Eagly and Chaiken (1993), a “cognitive learning process is assumed to occur when people gain information about the attitude objects and thereby form beliefs” (p. 14). An affective process occurs when an individual has an emotional experience, great pleasure or distress. Attitudes can also be derived from past behaviors as well. Oftentimes the three processes happen in concert, all occurring at one time. However, it is possible for them to happen separately. For example, if Sally read that an opposing coach had criticized the Tennessee Lady Volunteers basketball team, she would form an attitude about that opposing coach, and it probably would not be a positive one. The process of forming the attitude would be cognitive, but in all likelihood, there would be affective processes happening at the same time because as Sally read the article she would be experiencing some type of emotion as well. Although some researchers claim that there can be cognition without affect, when dealing with attitude formation they typically occur together. Once an individual has formed an Figure 2.9 – Attitude Theory Model attitude toward an entity, there are

typically three potential classes of responses. Eagly and Chaiken (1993) conceptualize evaluative responses of the cognitive type as beliefs. They feel that affective evaluative responses consist of feelings, moods, and emotions that people experience in relation to attitude objects. Eagly and Chaiken regard evaluation and affect as conceptually distinct because an individual can have an affective reaction to something before evaluating it consciously and can potentially evaluate an entity without having an affective reaction to it as shown on the diagram on the previous page. Evaluative responses of the behavioral (or conative) type consist of the overt actions that people exhibit in relation to the attitude objects. Eagly and Chaiken argue that behavioral responses also can be regarded as encompassing intentions to act that are not necessarily expressed in overt behavior. This is absolutely true, which suggests that rather than having only three response categories, there should be a fourth as well, titled Conative Responses. Conation is the intention to do something. However, research has shown that the relationship between intentions and actual behavior can vary quite a bit based on the situation and potential constraints. Continuing the example from above, Sally, after reading what the opposing coach had said, decided to write a letter to the newspaper editor, slamming the opposing coach. Later, after calming down a bit, she decided that it would not do any good to write the letter, but that she would just wait and see what the Volunteers did next time they played the other team. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Eagly and Chaiken (1993) suggested that attitudes allow people to express their personal values and self-concepts. This means that both values and self-concepts influence the formation of attitudes. Self-concepts can be identities, such as that of fan. This certainly makes sense on both counts There are many other models that attempt to explain consumption behavior, or behavior in general, some of which are hierarchical, some of which are interactive, and some are both. Although there seems to be a plethora of models explaining consumer behavior, very few have been proposed to explain sport consumption specifically. In the following section we present additional content regarding the nature of sport consumption followed by our model of sport consumption behavior for your consideration. Constraint Theory Constraints are defined as factors (or reasons) that prevent or prohibit an individual from participating and enjoying some activity. Much of the information we have regarding constraints comes from people that study leisure activities. According to Crawford and Godbey (1987), leisure constraints can be divided into three main categories: intrapersonal, interpersonal, and structural constraints. Intrapersonal constraints include an individual’s psychological states and the attributes that might have a negative influence on leisure preferences. Crawford and Godbey (1987) suggested several examples:  stress,  religiosity,  reference group attitudes,  prior socialization into specific leisure activities,  perceived self-skill,  subjective evaluation of the appropriateness and availability of various leisure activities. The list is not an all-encompassing, but it certainly gives us an idea of some of the potential intrapersonal constraints. Interpersonal constraints focus on the relationships, or more specifically, the lack of relationships, with others; for example, lack of a partner that will participate with you in some activity like sky diving (who would turn down a chance to sky dive?), or lack of someone to attend a game with. The impact of the (lack of) relationship might be a negative influence on both leisure preference and participation (Crawford & Godbey, 1987). Here’s another example, the fan in the picture on the left only attended one OSU Buckeye game that year because he had no one that wanted to go to the game with him  (not really true, but we needed an example). Copyright 2018 Galen Trail

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Theories of Consumer Behavior Structural constraints are characterized as factors that interfere between leisure preferences and participation. Said another way, these are constraints, physical or environmental factors, that prevent an individual from participating in a leisure activity (Godbey & Crawford, 1987). Examples of structural constraints include: financial resources, season, climate, and the scheduling of work time. However, in much of the research on leisure constraints, there was quite a bit of overlap between the interpersonal constraints and intrapersonal constraints. In addition, there was a lack of content validity in the items representing each category of constraint (i.e., items in the wrong categories). So, Kim and Trail (2010) proposed a new model in which there were only two dimensions, rather than three: internal constraints and external constraints. They defined internal constraints as internal psychological cognitions that deter behavior (Kim & Trail, 2010). The definition combines both the interpersonal and intrapersonal constraints proposed by Godbey and Crawford (1987). Kim and Trail suggested four internal constraints lack of knowledge, lack of someone to attend with, lack of success by the team, and no interest from others – would have a particular influence on consumers. There are many more internal constraints, but those are the ones they listed. External constraints were defined as social or environmental aspects that prevent or decrease the likelihood of the individual performing the behavior (Kim & Trail, 2011). There are many potential external constraints, and many are situation specific, but most can be categorized into either environmental constraints or leisure alternative constraints. For the former, some of the constraints that have been identified are distance to the game, bad traffic both going to and coming from the game, parking at the venue, time it takes to get to the venue and into the game, the location of the venue, access to public transportation, the cost of going to the game, and weather. For external leisure alternatives, some of the constraints that have been identified are: exercising and recreation opportunities, other leisure activities, watching other sports on TV, entertainment alternatives, participant sport activities, and others. However, I am proposing that the external constraints dimension can be separated into two categories, the first being those constraints in the external environment and second, those that are in the internal organizational environment (i.e., those constraints that are at least nominally under control by the sport organization). The internal constraints are those that are specific to the customer’s internal perceptions. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Models of Sport Consumption People may argue that consumer behavior models should suffice across all products and services, but that is not the case. The range of psychological connections consumers may form with a sport product compared to other goods and services make the sport product unique. Consider the example of attending a National Football League game. Some consumers may attend because of the novelty of going to a professional football game. Others may attend because of an opportunity to spend time with friends or family members. For others, attending a game is a chance to entertain existing or prospective business clients. Still others enjoy being part of the collection of fans and derive some sense of belonging from going to games. There will also be individuals for whom the team is a target of identification, and who “have to” attend because they think of themselves as part of the team. While the purchase and consumption of a piece of sporting equipment or sporting apparel may not be arguably different from other goods, there are definite differences with respect to sport services, particularly the product we know as sports teams. What we need is a better understanding of sport consumption. Figure 2.10 – Sport Consumption At the most basic level, sport consumption can be explained in terms of the interaction between motivation and activation (Figure 2.10). Activation is the external instigator for the action and motivation is the internal reason for wanting to act. The interaction between the two causes the individual to consume a sport product, whether it is purchasing a team jersey, watching a game on television, or attending a game at the stadium or arena. After the product is consumed, the individual evaluates both the product that was consumed and the consumption process as a whole. For example, after Joe Fan gets back from the game, he rehashes how the game went, how his team played, how the other team played, the refereeing, etc. He is evaluating the product. At the same time, he may evaluate the concession service, the length of the line in the men’s restroom versus the length of the line trailing down the concourse from the women’s restroom, his attempt to find parking, and the traffic after the game that made him miss the 11 o’clock news. All of this and much more is part of the consumption process, but is ancillary to the actual product itself, the game. All of these things combine to make Joe Fan happy or depressed, or perhaps mad, and to some extent may influence whether Joe goes back for another game. There are two models/frameworks that try to address sport consumer behavior extensively. Trail and colleagues (Trail & Fink, 1998; Trail, Anderson, & Fink, 2000) proposed their Model of Sport Consumer Behavior (MSCB) first, followed by Funk and colleagues’ (Funk, Gladden, Howard, James, Kahle, Mahony, Nakazawa, & Trail, 1999; Funk & James, 2001) Psychological Continuum Model Copyright 2018 Galen Trail

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Theories of Consumer Behavior (PCM) one year later. Both have positives and negatives, but we will discuss Funk and colleagues PCM model first and then show why Trail and colleagues’ model is better both theoretically, empirically, and practically. Psychological Continuum Model (PCM) The PCM is a hierarchy of effects model similar to the AIDA (Awareness Interest Desire Action) models that preceded it, of which Lavidge and Steiner’s (1961) is the most well-known. It is also based upon the Transtheoretical Model (TTM) of Prochaska & Figure 2.11 – Psychological Continuum Model DiClemente (1982) and Mullin, Hardy, and Sutton’s (2000) Escalator Model. Funk and colleague’s PCM has four stages (Figure 2.11): Awareness, Attraction, Attachment, and finally Allegiance. At the first stage, Awareness, the individual becomes aware of the team or object through socializing agents extrinsic to the individual. At the second level, Attraction, the individual “acknowledges having a favourite team or favourite sport based upon various socialpsychological and demographicbased motives” (Funk & James, p. 121). At the third stage, Attachment, the individual develops a psychological connection to the team based “based upon the perceived importance attached to physical and psychological features associated with a team or sport” (p. 121). At the fourth stage, Allegiance, the individual develops loyalty to the team, represented by consistent and durable attitudes and behaviors. Funk and James suggest that Allegiance leads to Behavior Loyalty even though it is not depicted as a fifth stage. As noted by Funk and James (2006), the original PCM had a variety of problems and therefore needed to be modified. They did so by changing the first three stages (Awareness, Attraction, and Attachment) to processes and creating Outcomes for each Process. For example, the Level 1 Outcomes of the Awareness Process, through socialization, are knowledge, realization, and extrinsic aspects. These Copyright 2018 Galen Trail

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Theories of Consumer Behavior Level 1 Outcomes then influence the Attraction Process, (hedonic motives, dispositional needs, and social situational factors) and Level 2 Outcomes. Supposedly, the interaction between Level 1 Outcomes and Attraction Processes create Level 2 Outcomes (attitude formation, social utility needs, individual needs, and something called P & P features – which aren’t explained). The Level 2 Outcomes then interact with Attachment processes (meanings, self-concept, and values) to impact Level 3 Outcomes (attitude strengthening, identification, and “decreased likelihood of replacing the team with another object” (p. 192). However, Funk and James suggest that there is a feedback loop between the Level 3 Outcomes and the Attachment Processes. In addition, the Level 3 Outcomes and the Attachment Processes then influence Allegiance Outcomes of durability and ‘impactfulness’. However, Funk and James (2006) Figure 2.12 – Revised Psychological Continuum Model no longer mention behavioral loyalty as a potential end-product. They also mention that it would be difficult to test this model due to its complexity. They prove this by attempting to establish some relationships between Level 2 Outcomes and Allegiance, Level 2 Outcomes and Level 3 Outcomes, and a mediated relationship between Level 2 Outcomes and Allegiance through Attachment Processes. Unfortunately, only some of their hypotheses were supported. In addition, they did not test their model as depicted either, by ignoring the other potential paths and the feedback loop that they suggested existed between Attachment Process and Level 3 Outcomes. Lastly, their model does not have good theoretical support either. In Beaton and Funk (2008) they claim that the PCM has been applied and tested in sport spectator settings, which is not accurate, as the whole model has not been tested, and the parts that have been tested are incomplete at best. Later, in Beaton, Funk, Ridinger, and Jordan (2011), they show a slightly modified version of the PCM and claim that PCM is applicable to a variety of situations, which may or may not be true because there is no support. Most recently, in Alexandris, Du, and Funk (2016), they noted that the model has provided “limited discussion on how internal and external forces influence actual behavior,” has a conceptual limitation on “how behavioral experiences influence movement along the continuum stages,” and “how personality traits influence stage-based progression of an individual’s attitude” (p. 223). Unfortunately, their results didn’t help much in resolving those issues or many others that still exist. Luckily, Trail and colleagues’ model does a much better job in providing theoretical justification for their model and the relationships therein. It has been tested in its entirety and shown to work well both from a statistical, theoretical, and practical standpoint. Best of all, it solves most, if not all, of the issues that Funk and colleagues have been unable to fix. So, without further ado, let’s examine the Model of Sport Consumer Behavior. Copyright 2018 Galen Trail

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Theories of Consumer Behavior Model of Sport Consumer Behavior As noted earlier, Trail and colleagues (Trail & Fink, 1998; Trail, Anderson, & Fink, 2000) proposed the Sport Consumer Behavior model in 1998. They tested and presented the original model in 1999, and then published the initial statistical tests of the measures in 2001 (Trail & James, 2001) and 2002 (Trail, Anderson, & Fink, 2002; Fink, Trail, & Anderson, 2002), and then tested the whole model in 2003 (Trail, Fink, & Anderson, 2003). There were some parts of the model that did not work as well as they had hoped, so they modified the model slightly and retested it with much better success, publishing those results in 2005 (Trail, Anderson, & Fink, 2005). Trail and colleagues continued to improve and test parts of the model in the following years (Kwon, Trail, & Anderson, 2006; Harrolle & Trail, 2007; James & Trail, 2008; Woo, Trail, Kwon, & Anderson, 2009; Lee, Trail, & Anderson, 2009; Kim & Trail, 2010; Harrolle, Trail, Rodríguez, & Jordan, 2010; Lee & Trail, 2011; Kim & Trail, 2011; Shapiro, Ridinger, & Trail, 2013; Ballouli, Trail, Koesters, & Bernthal, 2016; Trail, Anderson, & Lee, 2017). The most recent published version of the model of Sport Consumer Behavior (Figure 2.14) was in an earlier edition of this text (Trail & James, 2015), but has not been tested in its entirety. That’s not totally true, it has been tested, but the results have not been published as of yet; hopefully soon. These models are structural models that are testable using structural equation modeling procedures. The advantages of this model over Funk and colleagues’ model is that it is testable and earlier versions of the entire model have been tested and published, unlike Funk and colleagues’. Part of the Copyright 2018 Galen Trail

Figure 2.13 - Original Sport Consumer Behavior Model

Figure 2.14 - Current Sport Consumer Behavior Model

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Theories of Consumer Behavior reason that this model can be tested is that it looks at sport consumers at one point in time (i.e., it is a cross-sectional model). It doesn’t show a progression along some type of consumer pathway like Funk and colleagues’ model, which hypothesizes a progression from Awareness to Allegiance. As Funk and colleagues noted, that makes a model much more difficult to test because either researchers need to track sport consumers longitudinally along the path or researchers need to be able to segment a large number of sport consumers (and potential sport consumers) by their status on stages, and then test each stage. Although Funk and colleagues purport that they have been able to do so (Beaton et al., 2011), their results leave much to be desired. Until recently though, Trail and colleagues’ model did not show a progression along a consumer pathway, except for the feedback loop from post-consumption reactions/evaluation to attitude toward the product/brand. Based on Trail’s (2016; Trail & McCullough, 2017) sustainability campaign pathway for sport fans, which is based on Young’s (2010) and similar consumer pathways, such as Lavidge and Steiner’s (1961), I have added a consumer pathway for sport fans to the Model of Sport Consumer Behavior, and also modified the MSCB once again. The Revised MSCB (R-MSCB) now has two components (depicted in the Figure 2.15). The first is Consumer Pathway for Sport Fandom and the second is the Environmental Insight Framework. Figure 2.15 – Revised Model of Sport Consumer Behavior

The consumer pathway consists of seven stages that people progress through to achieve true sport fandom: Awareness, Interest, Active Consideration, Purchase (Conversion), Consumption (Usage), Repatronage, and Lifestyle Change. Individuals will progress along the pathway, hopefully in a positive direction but not necessarily, influenced by aspects within the Environmental Insight Framework (EIF). The EIF consists of the external environment, the internal organizational environment (the environment within the sport organization), and the customer environment (the potential sport consumer). Each of Copyright 2018 Galen Trail

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Theories of Consumer Behavior these environments influences progress along the Consumer Pathway. This is a theoretical model and is not statistically testable as depicted. To test this new model, a structural model must be drawn that includes most of the components, so the Revised Structural Sport Consumer Behavior Model is depicted in Figure 2.16. We will discuss all Figure 2.16 – Revised Structural Model of Sport Consumer Behavior of the components of the Revised Model of Sport Consumer Behavior in upcoming chapters, but for now we will just give a brief overview. The External Environment includes cultural aspects, contextual aspects, socialization factors, and external constraints. The External Environment impacts both the Organizational Environment and the Customer Environment. The Organizational Environment includes activation of marketing/communication plans, relationship building, and the brand associations (product related attributes, non-product related attributes, benefits, and organizational constraints) among other organizational aspects. The Customer Environment includes internal motivators such as the personality of the individual, the individual’s personal needs, values, and beliefs, perceived internal constraints, and demographics. The interaction between the External Environment, Organizational Environment, and Customer Environment all influence Copyright 2018 Galen Trail

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Theories of Consumer Behavior the individual’s brand attitude (i.e., attitude toward the sport product/brand). Points of attachment will also have an impact on brand attitude. Brand Attitude is the primary determinant of whether or not people intend to consume the product. Intentions lead to actual consumption, but not as often as one would think. This is because there are external factors that constrain or prevent people from both intending to consume and also actually consuming. For example, Joe Fan would like to have season tickets to the Los Angeles Lakers, but they cost too much money, Joe lives in Seattle (a long way to travel to Laker games), and there is a waiting list for season tickets (scarcity). Each of these things can be considered external constraints, among many potential other constraints, some of which are listed in the different environmental categories but come into play here. If the individual ends up consuming the product, expectancies about the experience are either confirmed or disconfirmed in either a positive or negative way. This confirmation or disconfirmation of expectancies creates an emotional response in the individual. For example, Lisa Loyal-Fan expected her team to win when she attended the game. Her team did win, confirming her expectations in a positive manner, so she is happy and satisfied that she went to the game. This may make her feel good enough about herself that her connection with the successful team increases her feelings of self-esteem. (Don’t worry if this does not make sense yet; we will talk about it more in a later chapter.) All of this might make her feel even more connected to the team (developing affective loyalty) and intend to come back to another game in the future (repeat patronage). In the next Section of the book (Section II), we will discuss the External Environment, including culture, context, socialization, and external environmental constraints, and how those things impact both the organizational environment and the customer environment. In Section III we will discuss the Organizational Environment and how that impacts the customer environment and progression along the pathway. In Section IV, we will discuss the customer environment and consumer pathway. Finally, in Section V we will cover the consumption and post-consumption reactions/evaluation.


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External Environment

Galen T. Trail


Chapter 3 External Environment As was noted in the previous chapter, the external environment can impact the organizational environment, the customer environment, and specifically the potential consumer’s movement along the Consumer Pathway (see Figure 3.1). I’m dividing the external environment into three general categories for ease of explanation: Context, Culture, and Socialization. Many of the aspects listed under context and culture can have both positive impacts (Motivators) and negative impacts (External Constraints) on the socialization of people into fandom and progress along the Pathway, potentially causing a pause or regression. In the Context category, we will examine three primary aspects (the economy, local/regional markets, and market trends/forecasts specific to the industry), even though there could be more aspects listed within Context. Similarly, we will cover three aspects within Culture (Demographics, Psychographics, and Geographics) even though there are more potential subcategories. Within each of the categories, I’m going to discuss whether they can be both motivators and constraints to fandom. It is critical that the Figure 3.1


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sport marketer/manager understands how the external environment and all of its components impact the potential sport consumer’s progression along the pathway. Context Economy. The focal economy that the sport marketer needs to examine could be either local, regional, national, or international, depending on the geographic impact of the sport marketer’s specific brand and product. For example, Manchester United or FC Barcelona are certainly considered international brands, whereas the Seattle Sounders FC is more of a regional brand specific to the greater Seattle, Washington, USA, geographic region and perhaps slightly beyond. In the latter case, a sport marketer for the Sounders probably would not need to concern herself with the economy much beyond the greater Seattle area because it would not have that much impact on the decision to purchase or the initial usage of a Sounders product, such as tickets or merchandise. That’s not to say that people from outside that geographical area are not fans and will not purchase the product or follow the brand. However, the impact of those from outside the area and the economy from those other areas will not have a substantial enough impact on which to spend significant resources. There are multiple measures of how well the economy is performing, but we will address only a few of them. The inflation rate is a valuable measure of how well the economy is Inflation rate - A measure of doing. If the inflation rate is too high, then consumers have how fast a currency loses its greater difficulty affording things they want to buy. On the other value. That is, the inflation hand, if the inflation rate is too low (or negative), producers will have trouble making money and surviving as an organization. rate measures how fast The inflation rate that is most typically used is the CPI prices for goods and (Consumer Price Index). The CPI is a “measure of the average services rise over time, or change in prices over time in a fixed market basket of goods and how much less one unit of services” (Bureau of Labor Statistics – BLS, 2018). This particular currency buys now ‘basket of goods’ is comprised of prices of “food, clothing, compared to one unit of shelter, and fuels, transportation fares, charges for doctors' and currency at a given time in dentists' services, drugs, and the other goods and services that people buy for day-to-day living” (BLS, 2018). As the CPI the past increases, people typically will spend more money on necessities and will have less disposable income to spend on entertainment, including sport products. In the graph below (Figure 3.2), for Portland, Oregon, USA, the CPI is low for all items from the beginning of 2014 through the end of 2016, indicating that inflation is low, and prices are not increasing very quickly. However, there is a substantial jump during the first half of 2017. This jump probably is not enough to cause most people in the Portland vicinity to stop going to Portland Trail Copyright 2018 Galen Trail

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Blazers basketball games or buying Blazers merchandise. However, if there were several years of increases at that same rate, it might constrain spectators from purchasing tickets. Figure 3.2

Unemployment rate is another measure of how well the economy is doing. Low unemployment means that more people are working, theoretically making more money, and thus more likely to have money to spend on sport. There are certainly some caveats here. First, even though the unemployment rate might be low, people might not be in jobs that pay a sufficient salary that would allow them to have disposable income to be spent on sport. Second, unemployment measures typically don’t count people who have given up looking for work. Third, they also don’t measure those in retirement. That said, if unemployment is lower, Figure 3.3 that usually means that more people do have more money and may spend it on entertainment. If we look at Portland again, we find that the unemployment rate has been dropping since the beginning of 2010, indicating that more people are working now than previously (Figure 3.3). However, when unemployment is high, it can constrain people from spending money on sport. Copyright 2018 Galen Trail

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Other measures of the economy are median household income level, real estate prices, economic growth, and disposable income. As is indicated in Figure 3.4, median household income level had been increasing in Portland since the recession in 2009-2010, but hadn’t reached prerecession levels until 2015. The Portland real estate market continues to increase, topping $360,000 for the median house price at the beginning of 2017 (Figure 3.5). All of these factors come into play in one way or another when people are trying to determine whether they can afford to buy tickets to a game or buy the latest merchandise. Figure 3.5 As noted above, sport marketers need to inform themselves about economic factors that are applicable in their geographic area and determine which ones may impact their fans and spectators. Random ticket price increases when the local economy is tough typically reduces the number of people attending games (a constraint). Figure 3.4

Market Trends/Forecasts. A market trend is the movement of a market over time. For our purposes, this refers to the spectator sport market and how it may vary; specifically, how it may vary in the future. Ideally, we would like to be able to predict with a fair amount of certainty whether the sport market will continue to improve. This is what is known as forecasting or a market trend analysis. Sport marketers, managers, owners, etc., all are interested (or should be if they aren’t) in how the market will change in the future. Market trend analysis “looks at how your industry started in the market, how it has grown, and where it is expected to go.” Copyright 2018 Galen Trail

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Specific to sport, PricewaterhouseCoopers (PwC) did a market trend analysis for the North American sports market. As Forbes (2017) reported, the sports market is expected to continue to grow through 2019 at about 4% annually (Figure 3.6). However, certain parts of the sports market will grow Figure 3.6

faster than other areas. For example, they predict that media rights will increase by 7.2% annually, whereas merchandizing will increase only by 1.4%. Sponsorship will increase by 4.5%, but gate revenues will only increase by 2.6%. However, the national trend may not represent the local or regional trend for a specific sport franchise. For example, Minneapolis/St. Paul, Minnesota, U.S.A., has a professional football team (Vikings; NFL), baseball team (Twins; MLB), hockey team (Wild; NHL), women’s basketball team (Lynx; WNBA), a major university (University of Minnesota) with all of its teams, and in 2017, added a new professional men’s soccer team (Minnesota United FC; MLS). A market trend analysis on the sport industry for the greater Minneapolis area might reflect dramatically different figures than the national analysis shown above given the plethora of sport options that to some extent may cannibalize each other. That is, sport fans in Minneapolis may not have sufficient disposable income to attend all of these events and thus choose only one or two. Obviously, the more competitors one has in the market, the greater the likelihood that the competitors may influence the market and how it trends. Copyright 2018 Galen Trail

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Local/Regional Markets. The third category of contextual factors that may impact how people progress (or regress) on the consumer pathway is called the consumer market. A market is the place where buyers and sellers interact; that is, a place where the sellers of a product will attempt to sell the product to the buyer. It can be either a physical location or a virtual one. For our purposes, we are interested primarily in local/regional markets again because most sport teams focus most of their marketing to sell tickets in their regional geographical market. For example, the greater Seattle area market extends from Everett, WA, on the north, to Tacoma, WA, on the south, west across the Puget Sound, and east past Bellevue to the Cascade Mountains. This market includes about 3.8 million people and covers about 5,900 square miles. A key component of the local/regional market is the media specific to that market. Broadcast (TV and radio) and print (newspapers) media have had a declining influence on the sport industry as digital media has expanded considerably as I noted in Chapter 1. However, in the local market, the local media still plays an important part of the distribution of information about the local sports teams and impacts how people are socialized into fandom and move up and down the pathway. The major newspapers still have a print section designated to sport every day. Most of the local TV stations newscasts will have a segment on sport during the evening and night-time newscasts in addition to carrying some of the games when they are not nationally broadcast. Usually there will be several radio stations that cover local sports and will broadcast games as well. In addition, all of these media outlets also have websites that provide sport coverage. This media coverage not only provides free marketing for the teams (usually positive, but sometimes negative), but also helps sell the media outlet’s products as people look for information about their teams. Sport coverage is typically the biggest draw for many media outlets. For example, many times, the most read story in the Seattle Times (my local newspaper) is a sport article. Thus, the tie between the media and sport is very symbiotic which is why the media is always very supportive of having sport teams in the local market. A potential constraint within any market would be a lack of a particular sport organization that fans may have interest in. For example, within the greater Seattle area market, there is no professional basketball team (NBA franchise). So even if someone in the Seattle area wanted to attend an NBA game, they would have to travel 180 miles south to Portland to do so. Most Seattleites are unwilling to root for any Portland team because of the rivalry between the two cities in many things, sport and non-sport related. In addition, there is no NHL (major league professional hockey) franchise currently (2018), although an announcement should be made within the year as to whether the NHL will award Seattle a franchise (it is highly expected to). If an NHL team starts playing in Seattle, then the minor league hockey teams in Kent (Seattle Thunderbirds) and Everett (Silvertips) could see their bottom lines impacted negatively. This brings up the next topic; within each market there will be direct and indirect competitors to sport organizations. Copyright 2018 Galen Trail

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Direct Competitors. Direct competitors in the market refer to competitors that are specific to that industry and are competing for the same consumers. From the above Minneapolis example, many of those sport organizations are in competition for the same sport fans. However, just because there are many sport organizations in the same market, that doesn’t necessarily mean that they are all direct competitors. For example, the Minnesota Vikings (American professional men’s football) and the Minnesota Lynx (women’s

professional basketball) may not be competing for the same sport fans because the crossover effect may be low. Cross-over effects. Are NHL fans NFL fans? MLB fans? NBA fans? One important piece of information that marketers need to know is the cross-over effects for avid fans of their sport, or any fans actually. Cross-over effects refer to the percentage of fans of one sport that are avid fans of another sport. For example, looking at the avid fans comparison chart (Figure 3.7; generated from information in the Sports Business Journal/ESPN Chilton Sports Poll), if a director of marketing for an athletic department wanted to know whether it would make sense to market the men’s basketball program at the school’s football games, she could look at the column of people who said they were avid collegiate football fans and then scroll down to the avid college basketball fan row to determine the cross-over effect. The chart shows that 46.4% of avid college football fans are also avid college basketball fans. Thus, the marketer could expect that advertising the university’s basketball team at the university’s football games would be accessing a fair percentage of people who have interest in college basketball. However, it would not make any sense to advertise at professional figure skating events, as only 16.2% of the people who consider themselves avid figure skating fans also like college basketball, and they may not even like that specific school. However, if the marketer is advertising at her own football team’s games she probably has a much better shot at assuming that most of those who indicated being an avid fan of college basketball, are a fan of the specific university. Copyright 2018 Galen Trail

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Avid Fan NFL

Avid Fan MLB

Avid Fan NBA

Avid Fan NHL

Avid Fan Pro Soccer

Avid Fan College Football

Avid Fan College Basketball

Avid Fan Pro Golf

Avid Fan Pro Tennis

Avid Fan Auto Racing

Avid Fan Pro Boxing

Avid Fan Pro Figure Skating

Avid Fan-NFL

100

58.2

63.7

61.1

42.4

67.9

59.3

55.9

48.1

45.5

61.7

28.8

Avid Fan-MLB

37.9

100

41.1

40

33

40

40.6

40

35.8

27.8

34.9

23.3

Avid Fan-NBA

33.8

33.6

100

26.4

32.4

33.2

47.9

26.8

37.9

18.2

40.9

17.6

Avid Fan-NHL

16.7

16.8

13.6

100

21.7

16.5

14.7

17.1

16.3

15.2

15.1

9.9

Avid Fan-Pro Soccer Avid Fan-College Football

7.8

9.3

11.2

14.5

100

8.7

10.1

9.4

17.2

7.7

11.3

8.2

43.6

39.5

40.1

38.7

30.4

100

56.5

43.6

36.3

29.8

37.6

18.9

Avid Fan-College Basketball

31.3

32.9

47.6

28.4

29

46.4

100

36.2

34.1

20.7

32.3

16.2

Avid Fan-Pro Golf Avid Fan-Pro Tennis Avid Fan-Auto Racing

17

18.7

15.4

19

15.6

20.7

20.9

100

23.8

12.7

13.5

10.2

9.5

10.8

14.1

11.7

18.4

11.1

12.7

15.4

100

7

11.6

13.8

18.8

17.6

14.2

22.9

17.4

19.1

16.2

17.2

14.6

100

21.2

12.2

Avid Fan-Pro Boxing Avid Fan-Pro Figure Skating

23.3

20.3

29.1

20.9

23.2

22.2

23.2

16.8

22.2

19.5

100

10.6

13.5

16.7

15.5

16.9

20.9

13.8

14.4

15.7

32.7

13.8

13.1

100

Figure 3.7 Avid Fans Comparison

SPORTSBUSINESS JOURNAL/ESPN CHILTON SPORTS POLL (2000). To interpret this table: Start with the column heading and read down the column. For example, 43.6% of Avid NFL Fans are Avid College Football Fans, but 67.9% of Avid College Football Fans are Avid NFL Fans.


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To answer the question about whether NHL fans are NFL, MLB, and NBA fans, read down the NHL fan column. The figures indicate that 61.1% of people who are avid NHL fans also claim to be NFL fans. This drops off to only 40% who claim to be avid MLB fans and only 26.4% who are NBA fans. This may be somewhat disconcerting for NHL teams, as it indicates that if an NHL fan has a limited entertainment budget, spending money on tickets and merchandise for a hockey team might be in conflict with spending money on an NFL team, especially if they are in the same market. Even if the entertainment budget is not limited enough to force a financial choice, the overlap between the NFL season and the NHL season may decrease attendance and viewing habits of the NHL teams until the NFL season is over. However, the information in the table also shows that the perception that the NBA takes away entertainment dollars from the NHL is probably not true as the overlap here is considerably smaller. The flip side is also true as we saw when the NHL locked out their players in 2004-05. The media hypothesized that the NBA would benefit from the NHL not playing, as fans would flock to other sporting events, specifically basketball as the seasons were played at the same time. This was not the case. NBA attendance was not affected at all. Further evidence of the crossover effect comes from a marketing research project for a Major League Baseball team that Jeff James and I completed a while back (the team shall remain nameless to protect the innocent). The project included a random sample of all the MLB team’s season ticket holders. One piece of the larger study was a measure of how many season ticket holders also owned tickets for other sports. For the MLB team sponsoring the research, other sport teams in close proximity included an NBA team, an NFL team, an NHL team, and a major Division I university. Among all the MLB season ticket holders surveyed, 45% reported that they owned season tickets for another team in the area (Figure 3.8). The results were interesting in that there was no one other sport that stood out. It was not possible to say that MLB season ticket holders were more likely to be season ticket holders for an NFL team compared to an NBA team. Among the MLB season ticket holders, 21% also had season tickets for the local NBA team; 18% had season tickets for the NFL team; 17% had season tickets for the college football team; and 11% had season tickets for the NHL team.

Figure 3.8 - MLB Season Ticket Holders Buying Season Tickets for Other Sport 1% 4% 2% Season tickets for the MLB team only 11%

Season tickets for 2 sports (MLB team + 1) Season tickets for 3 sports (MLB team + 2)

27%

55%

Season tickets for 4 sports (MLB team + 3) Season tickets for 5 sports (MLB team + 4) Season tickets for 6 sports (MLB team + 5)

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One additional component of the project looked at how many different sports people might purchase season tickets for. As illustrated in Figure 3.8, close to one-third of the team’s season ticket holders purchased season tickets for another sport. The majority of MLB season ticket holders, however, only purchased tickets for one sport. An important finding for the MLB team was learning that while there was some cross-over from one sport to another, a majority of the season ticket (55%) holders WERE NOT spending money on season tickets for another sport. At the same time, the MLB season ticket holders that did attend other sporting events on a regular basis were able to afford season tickets for two different sports. The marketing staff members for the MLB team could take the results and consider some type of cross-promotion with another local sport team. To do so, however, would likely take more evidence to demonstrate that there are substantial numbers of consumers following multiple sports. Information about cross-over effects is critical when athletic department marketers are trying to cross-promote women’s and men’s sports. Too frequently marketing directors in athletic departments believe that promoting the women’s teams during men’s sporting events will generate more interest in the women’s games. Unfortunately, this is not the case. According to the SportBusiness Journal (2000), only 5% of college football fans are women’s college basketball fans. In addition, based on research that I did for a Southeastern University, I found that people who identified themselves as fans of the men’s basketball team for that university were not fans of the women’s basketball team. The relationship was even more strongly negative between football fans and women’s basketball. The more avid a fan of the university’s football team, the less likely the person was to ever attend any of the women’s basketball team’s games (Figure 3.9). In fact, most football fans indicated that they had never attended, and never intended to attend. Obviously, all of the promotions that the marketing department was doing for the women’s basketball team during the football games were falling on deaf ears.

Figure 3.9 Percent of respondents in Football Booster Club attending Women's Basketball Games Percent of football boosters

70% 60% 50% 40% 30% 20% 10% 0% 0

1

2

3

4

5

6

7

8

9

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14

15

Number of Women's Basketball Games Attended


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If we go back to our Minneapolis, Minnesota, example, we know that only 7.8% of avid NFL fans are also avid fans of professional soccer in the U.S. However, 42.4% of avid pro soccer fans are also avid NFL fans. If these numbers reflect the percentages in Minneapolis, then this would mean that it is unlikely that adding a new professional men’s soccer team to the Minneapolis sport scene would negatively impact Minnesota Vikings fans buying tickets for Viking football games as only 7.8% of Vikings fans are soccer fans. However, the reverse is not true. Since 42% of avid soccer fans are also football fans, if a substantial number of those soccer fans had to choose between purchasing football tickets or soccer tickets and chose football, it would negatively impact the new soccer franchise in Minneapolis. So, what does recent research say about direct competitors as potential constraints to attendance? Relative to Attending Other Similar Sporting Events that are going on at the same time and the spectator must choose which to attend, there isn’t much research that I know of because there aren’t many places that schedule multiple sporting events of the same sport and level in the same area at the same time. There are exceptions, the Chicago Cubs might be playing at the same time as the Chicago White Sox, or the New York Yankees are playing at the same time as the New York Mets. There are other examples in big cities, but in most, fans of one team are not fans of the cross-town rival. Not surprisingly in the data I collected on college women’s basketball game attendees, those people indicated that attending another college women’s basketball game wasn’t a constraint; quite likely because there were no other similar (women’s basketball) sporting events available to attend in the near vicinity. Obviously, this needs to be investigated further, but this might be a constraint in some of the situations noted above. Attending Other Dissimilar Sporting Events. So, if there are not similar sporting events that are prevalent in many markets, how about dissimilar sporting events? For example, during late fall in some markets there are professional football games, college football games, hockey games, professional and collegiate basketball games, volleyball matches, soccer matches, and high school sports. Many of these events overlap and most individuals cannot afford to attend all of them. What we found is that for the people we surveyed about the women’s collegiate basketball team, there was one big constraint: the


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men’s collegiate basketball team! People were much more likely to go to the men’s games and as the number of men’s games attended increased, the number of women’s games attended decreased. However, in a data set of women’s professional basketball game attendees, I found that they were not very likely to be constrained by either the possibility of attending a MLB game or by attending a professional soccer match. This gets back to the concept of the crossover effect that we discussed earlier in the chapter. Some sports have large cross-over effects, whereas others do not have any. In the example of the professional women’s basketball team, that organization thought that fans of the men’s professional team were also likely to be fans of the women’s basketball team because it was the same ownership group and the two teams were associated. However, when they checked to see how many men’s season ticket holders also had season tickets with the women’s team, there were only 13. There was no cross-over effect from men’s to women’s professional basketball for those two teams. Zhang et al. (1997) found that although hockey fans attended other sporting events at least occasionally, this really did not influence their hockey attendance that much. In fact, 77% of the hockey attendees said that they occasionally, sometimes, often, or always, attended baseball games. Because hockey season and baseball season typically do not overlap that much, if at all, going to a baseball game would not prevent someone from going to a hockey game, however, if people were limited by economic considerations, one might prevent the other. This was not shown though as 99% of the variance in hockey attendance had nothing to do with baseball. Thus, it is critical that sport marketers understand who their direct competitors are in their specific markets. It is very likely that the direct competitors in one market won’t be the same type of sport franchises as in another market. Sport marketers will need to do their own market research to determine who their direct competitors are and to what extent those competitors will affect them. One way of doing this is to do a growth and share analysis. Copyright 2018 - Galen Trail

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Growth and share analysis. This type of analysis was originally created by the Boston Consulting Group but modified by many since the 1960s. The Economist (2009) gives an overview of the matrix and the concepts. The matrix has four quadrants that vary across Market Growth potential and Market Share (see figure below): Cash Cows, Stars, Question Marks, and Dogs. • Cash Cows are businesses that have a high market share (and are therefore generating lots of cash) but low growth prospects. They are often in mature industries that are about to fall into decline. • Stars have high growth prospects and a high market share. • Question Marks have high growth prospects but a comparatively low market share. • Dogs, by deduction, are low on both growth prospects and market share (Economist). To do this analysis, one creates a chart like Figure 3.10 below and plots the competitors on the graph, along with one’s own company, in one of the four quadrants. I have done this for the Minneapolis sport market based on attendance for growth and share. Figure 3.10


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The Minnesota Vikings attendance was down in 2014 and 2015 because they were playing in a temporary venue waiting for their new stadium to be finished. In 2016, the first year in the new stadium, they averaged 66,786 fans per game, which is greater than the official capacity of their new stadium, and then 66,721 in 2017. Thus, they have maxed out their potential for growth of number of fans per game, but they can always increase ticket prices to generate more revenue, even though in 2016, average Viking ticket prices were the highest ever ($160; Figure 3.11), but they actually decreased the cost in 2017. This still generated over US$73 million in ticket revenue in 2017. In terms of share, the Vikings averaged the most fans per game during the most recent season of any of the sport organizations in Minneapolis, but not the most fans per season with only 533,768 attendees for the season. Thus, we have put the Vikings Figure 3.11

solidly in the Cash Cow quadrant because their market share is high, but their potential for growth is limited by how much people will be willing to pay for tickets and capacity of the stadium. The Minnesota Twins attendance has fallen every year since their new stadium opened in 2010. In 2010 they averaged 39,798 (at 100%+ capacity and in 2017 they were down to 25,640 per game or 62% of capacity. Even so, total attendance in 2017 was 2.05 million, considerably more than the Vikings total attendance. Ticket prices for the Twins have averaged around $32/ticket over the last couple of years. This means that in 2017 they generated approximately US$65 million in ticket sales. The Twins can increase the number of tickets sold substantially if they start winning again and could even increase their ticket prices a little assuming they win. Thus, we put them in the Question Marks quadrant. The Minnesota Timberwolves attendance is one of the lowest in the NBA. They averaged only 14,809 in the 2016/17 season (76% of capacity), for a total of 607,203. This is considerably down since 2012. At an average ticket price of $33, they generated slightly over US$22 million in ticket sales. Thus, even though they had more total season attendees than the Vikings, they generate only 12.5% of the ticket revenue. I plotted the Timberwolves into the Dog quadrant because they are generating very little from their ticket sales, attendance has been decreasing (although it was up slightly in 2016-17 and has been up so far in 2017-18) and although they have plenty of room for growth, they are not showing that they are doing so currently. The Minnesota Wild averaged 19,070 per game in the 2016/17 season (more than 100% capacity) for a total of 781,879. This was slightly down from 2015/16, but above all of the previous years since 201011. With an average ticket price north of $70/ticket, the Wild generated around US$55 million in ticket sales. Thus, they can’t really increase the number of tickets sold much, and they are also charging a fair amount for the Minneapolis/St. Paul market per ticket, so there is not a lot of room for growth. We put them in the Cash Cow quadrant. The Minnesota Lynx averaged 10,407 per game in 2016, for a total of 176,919. This is the highest they have averaged ever. There are no reported average ticket prices for the Lynx as far as I can determine but based on ticket prices listed on their website I am going to estimate around US$25. If so, then they Copyright 2018 - Galen Trail

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will generate around US$4 million in ticket revenue. Once the Target Center is remodeled, seating capacity will be around 20,000, so there is plenty of room to increase attendance. The Lynx is the best team in the WNBA right now, so it probably can’t charge too much more per ticket or they would already be doing so. Their market share is the lowest of all of the professional teams and although they have the opportunity for growth, they haven’t shown any indication of doing so even though they were WNBA champs in 2011, 2013, 2015, and 2017 (apparently they win in odd numbered years), and in the finals in 2016. I have put them in the Dogs quadrant. The Minnesota United FC averaged 20,538 per match in their first season, for a total of 349,146 attendees. The average ticket price for the Loons was around US$30, which would generate US$10.5 million. When their new stadium is completed it should hold approximately 19,400 fans. One might be tempted to suggest that they will max out their new stadium, but they were pretty bad in 2017, with a 10 win, 18 loss, 6 draw table. Their overall market share is low, but they have potential for some growth in ticket revenue. With only one season behind them, I am putting them on the line between Question Marks and Dogs. It is very hard to plot a university on the same figure as I did with all of the professional sports because university athletic departments have so many teams across both men’s and women’s sports. However, if we focus on the two typically revenue-producing sports (men’s football and men’s basketball), we should get somewhat of an indication. The University of Minnesota men’s football team averaged approximately 43,500 spectators per game in 2016, down 8,500 from 2015. It seems as though 2015 was an anomaly as attendance had been down multiple years since 2010. The average ticket price is around $60/game, but that does not include the “gift amount” that most season ticket holders must pay to get season tickets, which ranges from $100 to $800 for the 7-game season. However, let’s ignore the gifts and just calculate ticket revenue. Overall attendance was approximately 304,500; at US$60/ticket that equals slightly over US$18 million in ticket revenue. These football numbers put them behind the Vikings, Timberwolves, Twins, and Wild in terms of revenue and spectators. If we add men’s basketball to the equation, we are potentially confounding the issue. Some of the attendees for football are going to be the same for men’s basketball, but we have no idea how many. So, we can’t combine the two sets of data, but also keeping them totally separate isn’t accurate either because they both represent support for the UM brand. For men’s basketball, there were 214,185 total attendees in 2016-17 for an Copyright 2018 - Galen Trail

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average of 10,195. Although attendance has fluctuated considerable since 2010, it is down almost 3000 per game. The average ticket price for non-student tickets is around $30/game, excluding ‘gifts’. Therefore, in 2016-17, UM men’s basketball generated around US$6.4 million in ticket sales. This would put them behind all of the aforementioned professional teams. Looking at all of the information causes us to put UM in the Dogs category as well. Doing a growth and share analysis of the local/regional market helps the sport marketer understand what potential direct competitors are doing and how they may influence people in that market to buy their team’s product, but it is not sufficient. In addition to a growth and share analysis, sport marketers should also look at doing a competitive analysis, which entails comparing at least the following four things against competitors: product quality, pricing, place (distribution), and promotions. For sport organizations, the most important product quality aspect is typically success, represented by winning percentage, playoff berths, championships and the like. People are more likely to follow successful teams; thus, a sport marketer should compare his/her organization’s success to other organizations’ successes. Price is the other big component. Typically, a lower price will generate more interest in comparable products. However, too low a price will indicate to the consumer that the product may not be very good. For sport organizations, place (or distribution) refers to the venue the team plays in. A new venue generates interest (novelty effect) and a very poor facility will turn people away if they are dissatisfied with the facility. Extremely good promotions can usually bump up attendance for a onetime only increase. If competitors have very good promotions with a similar product, that might cause some spectators (not fans) to switch for one specific game. Sport marketers need to understand what the competitors are doing and take this into account when doing their own marketing. However, in addition to understanding the competitors, the sport marketer needs to understand the competitors’ influence on the sport marketer’s own spectators and fans. The greater the number of direct competitors that the sport organization has, the greater the potential external constraints that prevent people from attending and purchasing merchandise, assuming a limited entertainment budget for the typical sport fan. In addition though, it is not sufficient only to evaluate direct competitors, it is necessary to evaluate indirect competitors as well. Indirect Competitors For sport organizations, indirect competitors can be many and varied. Different organizations will be impacted by different indirect competitors in different ways. Just because one organization may have a particular indirect competitor, that doesn’t necessarily mean that another one will. Sport marketers need to identify the indirect competitors that are relevant to their own particular organization/team. In general, indirect competitors can be entities such as movie theatres, concerts and other musical entertainment, bars and restaurants, recreational opportunities, gyms and other fitness businesses, etc. Previous research has reported that environmental factors such as other entertainment options (Hansen & Gauthier, 1989; Baade & Tiehen, 1990) are negatively related to attendance. This does make some sense. If a person chooses to go to a movie or a show instead of attending a game, then that might have a negative influence on attendance if it happened frequently enough. In a data set I collected referent to a Southeastern University women’s basketball team, I investigated several potential activities that might be indirect competitors to the team and have negatively impacted game attendance if the potential spectator chose to do that activity instead of attending the game. The potential constraints were: exercising and other recreation opportunities, other leisure activities, watching other sports on TV. The responses were scored from 1 “no influence at all” to 7 “a large negative influence.” As can be seen Copyright 2018 - Galen Trail

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in Figure 3.12 below, exercising and recreation opportunities were not really constraints for any of the three groups (non-attendees, past attendees, and present attendees), neither were other leisure activities. However, for all three groups, watching other sports on TV was a potential constraint, but the groups did not differ meaningfully. The correlations between these three variables and future attendance intentions did not indicate that any of these prevented future attendance. So, let’s talk about each of these in more detail.

Level of Constraint

Figure 3.12

External Leisure Constraints to Attendance

4 3.5 3 2.5 2 1.5 1 0.5 0

Non-Attendees Past Attendees Present Attendees

Constraint Type

Exercising & Recreation Opportunities Often people indicate that they feel constrained to attend games because they have too many other things to do. One of the many things that people do when not working is exercise, play recreational sports, go camping or some other type of outdoor recreation opportunity. Those types of activities might be constraints that would keep people from attending games. Apparently, this was not the case in this situation for college women’s basketball. We also tested the same hypothesis with a professional women’s basketball team and found that working out or participating in recreational sports is definitely not a constraint. Zhang, Pease, Smith, and Jambor (1997) found the same thing; neither working out nor playing recreational sports had a meaningful negative effect on Copyright 2018 - Galen Trail

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going to hockey games. If people want to come to the game, they do so, instead of working out. (That might explain the prevalence of overweight fans. ☺) Other Leisure Activities Another potential constraint is that perhaps people get too busy with other types of leisure alternatives and do not go to a game. For instance, people might choose to attend a movie, go to a restaurant, go to a concert, or go to a bar. Neither the potential spectators at the women’s college basketball game nor the attendees at the women’s professional game were dissuaded from coming by these other leisure activities. In fact, those who attended often paired up several of the activities at once, such as going out to eat before the game and then going to a bar after the game (of course never on a week night!). Zhang and his colleagues (1997) also found the same thing with the hockey fans. Most sport managers are aware of these complementary activities and that is why venues more often than not have restaurant and bars. This is a benefit to the owners of the venues or whoever gets the revenue from the restaurants and bars, but this typically hurts these types of establishments in the surrounding area if there are such. From a marketing standpoint, it might pay to emphasize that not only can spectators attend the game, but they can go to an in-venue restaurant or bar both before and after the game as well. Watching Other Sports on TV Another potential leisure alternative that might be working as a constraint would be people staying home to watch other sports on TV rather than going to the game. For the people that answered the survey about the collegiate women’s basketball team, as noted above, they perceived that it might be somewhat of a constraint, but in the end, it didn’t prevent them from attending most of the time. Again, though this might just be evidence that these people preferred different sports over women’s basketball, so whether or not this is really a constraint or just a lack of interest is easily debatable. Zhang et al. (1997) did not find this to be the case with hockey fans though. Even though 66% of the hockey fans watched sports on TV, it did not interfere with their attendance. Regardless, marketers need to realize what entertainment alternatives may be their competition and figure out strategic plans to address potential issues if their market research indicates that there are any. Once indirect competitors have been identified specific to the sport organization, a growth and share analysis or a competitive analysis can be done, whichever is more applicable depending on the relevance of the indirect competitors. That is, a growth and share analysis is not applicable to outdoor recreation as much as perhaps concerts might be. Copyright 2018 - Galen Trail

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Opportunities and Threats the OT of SWOT Marketers can also do a SWOT analysis once the above analyses have been completed. Actually though, only the OT (Opportunities and Threats) part of SWOT is applicable relative to external factors (the SW—Strengths & Weaknesses—part is more relevant to the organizational environment, and we will talk about those later). Sport marketers need to identify potential opportunities for their team based the above external environment factors. For example, the Minnesota Timberwolves have an opportunity to substantially increase their attendance numbers and then increase their ticket prices. Right now, many ‘experts’ think that the Timberwolves are a team on the rise and most are predicting that they will make the playoffs for the first time in 15 years. In addition, the Wolves have a bunch of young exciting players. This combination should increase attendance this year. If they meet or exceed expectations, they have the opportunity to substantially jump their ticket sales. Furthermore, if they are successful, they will have the opportunity to increase their ticket prices considerably because they are some of the lowest in the NBA and they are approximately the same as many of their lower level local competitors (University of Minnesota men’s basketball, the Loons, and the Lynx). On the other hand, if those other organizations/teams continue to be more successful or increase their success rate, and the Wolves don’t, then all of them could be potential threats. Another threat is if the remodel of the Wolves’ venue does not go well and people stop going; regenerating that interest may be very difficult. Regardless, sport marketers need to identify potential strengths and weaknesses and plan to maximize the former and minimize the latter as much as possible. In addition to all of the aspects noted above relative to the environmental context, sport marketers need to understand the culture (psychographics, demographics, geographics) and potential associated constraints of their regional market. These aspects of the regional market do not necessarily reflect the sport organization’s fan market. In other words, the culture of the greater Portland area may be considerably different from the culture of the Trail Blazers’ fan base. Astute sport marketers understand the potential differences. So, let’s talk about environmental culture for a little bit. Culture Culture includes “shared beliefs, attitudes, norms, roles, and values found among speakers of a particular language who live during the same historical period of a specific geographic region” (de Mooij, 2004, p. 26). Additionally, culture can be passed onto future generations (Isajiw, 1992; Korzenny & Korzenny, 2005; Naylor; 1997) in both an objective and subjective manner. Culture is furthermore characterized as a concept containing a sense of self and space, time consciousness, food and feeding habits, communication and languages, beliefs and attitudes, values and norms, and work habits and practices (de Mooij). Culture fluctuates and is continually developing through social interaction (Zea, Asner-Self, Birman, & Buki, 2003). Naylor (1997) suggested that, Some Americans simply see culture in terms of customs or traditions, a way of life, or the heritage of people. Others see it as an assemblage of observable behavior (the practices) of people, while others see it as a set of rules that generate behavior. For still others, culture is a combination of beliefs, behaviors, and the physical and social products these produce. Apparently, how culture is viewed depends on one’s interest and purpose. This means that culture can be used for a number of different things all at the same time (p. 6). For example, culture can be defined as actual artifacts of a group (e.g., books, fashion, and music) or defined through values and norms (e.g., patriarchal family dynamics or emphasis on multiple generational housing). Due to the complexity of the overall concept of culture and the broad perspectives Copyright 2018 - Galen Trail

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researchers have regarding culture; it is difficult to fully understand the complete concept of culture without first bringing each of the components of culture together into one definition. Based on previous research Figure 3.13 and Naylor’s (1997) definition, we have organized culture into four dimensions: shared contexts, shared cognition, shared affect, and shared behaviors (See Figure 3.13). The shared contexts dimension includes a shared historical period/experience, a shared geographic region (e.g., living in the same area or community), and shared rules and standards (de Mooij, 2004; Isajiw, 1992; Korzenny & Korzenny, 2005; Naylor, 1997). The second aspect of culture, shared cognition, is comprised of beliefs, values, roles, and norms (de Mooij, 2004; Berry, 1996, 2001; Isajiw, 1992; Korzenny & Korzenny, 2005; Naylor, 1997; Phinney, 1990; Zea et al., 2003). Shared affect, the third aspect of culture, contains shared attitudes which are essential to a cohesive culture (Berry, 1996; 2001; de Mooij, 2004; Isajiw, 1992; Phinney, 1990). The final aspect of culture, shared behavior, has been expressed as tangible items, such as food, dress, gestures, architecture, and so on, which are external manifestations of culture characterized as objective culture (de Mooij, 2004; Isajiw, 1992; Korzenny & Korzenny, 2005; Phinney, 1990; Zea et al., 2003). Culture, as expressed by these four dimensions, encompasses a vast expression of an individual’s selfidentity. Thus, culture can be derived from all of these four components (shared contexts, shared cognition, shared affect, and Copyright 2018 - Galen Trail

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shared behaviors) or any combination of components. For example, culture can include shared historical experiences, values, and lifestyles. Culture is a fluid concept that changes based on the individual, the group/social dynamic, and the levels of identification with any specific group. The last idea, the impact of identification, is a topic we need to think about in more detail, so press on. Identification “Identification and its resulting product, identity, are theoretical, psychological formulations based on external and internal observations” (Pollock, 1993, p. xv). In previous literature, the examination of cultural identity has concentrated on two primary theories: social identity theory (Tajfel, 1981) and identity formation theory (Erickson, 1968). In order to understand the members of any cultural group and their respective identification with their cultural group, one must understand the conceptual framework of these two theories. I talked about both in Chapter 2, but will cover them again here. Social Identity Theory Tajfel (1981), using social identity theory, claimed that individuals simultaneously maintain membership in multiple social groups. For example, an individual can be a member of a sorority/fraternity and be a member of a university community. Social groups can positively and negatively affect an individual’s self-image, and the self-image can be defined by the dynamics in the social group along with the status in the social group. In order to maintain a high level of self-esteem or self-image, an individual will maintain or search for group memberships that will satisfy a need for a positive self-image. For example, individuals might want to associate with a successful sports team (i.e., a National Championship team) to increase their self-esteem. In terms of social identity theory, all individuals live within multiple social groups and have different levels of identification with these groups. Essentially, individuals attempt to maintain a positive selfimage through their relationships with numerous social groups (Festinger, 1954; Tajfel, 1981). Sports fans will seek out groups Copyright 2018 - Galen Trail

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(i.e., fans of teams) that will provide an opportunity to increase their self-image. As an Ohio State fan, I enjoy talking to Michigan fans AFTER the Buckeyes have defeated the Wolverines in the annual November football game. Being a fan of the winning team, and letting others know you are a fan of the winning team, can increase your self-image. Identity Formation Theory Identity formation theory deals with the developmental process where adolescents begin to develop a sense of themselves and the roles they play in social groups and society as a whole. Erikson (1959) described identity formation as a developmental process in which an individual’s characteristics, both those unique to the self and the characteristics that are shared with others, interact to form a whole. Individuals simultaneously reflect upon themselves as they compare their perceptions of others’ perspectives of themselves. As individuals become more comfortable with their identity, it engenders a sense of wellbeing and inner assuredness. During the final stages of adolescence, an individual will develop a single, whole identity that encompasses all previous significant identities (Erikson, 1968). However, an individual’s identity does not stop changing at the end of adolescence; it is a constantly developing psychological process over the lifespan. This evolution of identity explains how acculturation can take place. Acculturation. Acculturation is acknowledged as “a complex, multidimensional process of learning that occurs when individuals and groups come into continuous contact with different societies” (Stephenson, 2000, p. 77). As individuals are in contact with other groups, they may possibly form additional identities. Recently, acculturation theorists and researchers have discarded a unidimensional model of acculturation for a bi-dimensional model (LaFromboise, Coleman & Gerton, 1993; Phinney, 1990; Zea et al., 2003). From a theoretical perspective, acculturation is composed along two dimensions: identification with the minority group (e.g., ethnic group) and identification with the dominant or larger group/society (e.g., U.S. American culture; Berry, 2001). These two dimensions of acculturation are independent of each other and nested (Berry, 2001). For example, an individual could have a strong identification with the minority culture as well as have a strong identification with the dominant culture. The relationship between these two identities (i.e., identity with minority group and identity with the dominant group) are not negatively correlated. That is, as the level of one identity increases the level of the counter identity does not have to decrease. These identities may increase and decrease independently, and can be nested in one another. That is, individuals can live in the dominant society and still maintain an identity with their minority group. For example, an immigrant who moves to the United States could become a real fan of American football, while also retaining his/her fandom for team handball (i.e., a primer sport in his/her country of origin). Copyright 2018 - Galen Trail

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It is the constant contact with the dominant culture that begins the process of acculturation. The evolution of the acculturation process may develop over days, weeks, years, and even generations (Berry & Kim, 1988), where individuals increase their level of identification toward a higher level (Greenland & Brown, 2005). From a research perspective, within the past decade, numerous minority groups (e.g., African-Americans, Asians, Latinos, etc.) have been studied in terms of dominant and non-dominant language, food, media, and traditions (Gomez & Fassinger, 1994); cultural domains of language, social affiliation, activities, attitudes, media, exposure, and food (Tsai, Ying, & Lee, 2000); ethnic versus dominant immersion (Stephenson, 2000); identity, language competence, and cultural competence (Zea et al., 2003); and language ability and perceived cultural distance (Greenland & Brown, 2005). MarketTools, which was recognized Figure 3.14 by the 2006 American Business "Stevie" Awards as a finalist in the "Most Innovative Company" for their ground-breaking work in understanding the Hispanic American community, created this acculturation table (Figure 3.14). Within the sport literature, Harrolle and Trail (2007) found that individuals who were highly identified with an ethnic group (i.e., Latinos/Latinas) can also be highly identified with the majority group (i.e., American society). Their level of identification with the American culture influenced their identification with specific sports (e.g., American football). Harrolle and Trail’s results showed that as the level of identification with the dominant culture increased, the level of fandom for American football significantly increased. Cultural Assimilation. Cultural assimilation is often referred to as simply assimilation. Assimilation possesses two definitions: general and specific. In a general sense, “the core meaning is increasing similarity or likeness” (Brubaker, 2003, p.42). Thus, individuals go through a process of becoming more similar to others. According to Brubaker, assimilation in a specific sense means that an individual is completely absorbed into a system. In this situation, assimilation does not have degrees; an individual is either assimilated or not assimilated. Individuals have a need to assimilate or simply have a need to be similar to others, while also preserving their own uniqueness (Devos & Banaji, 2003). Contrary to acculturation, assimilation requires a degree of acceptance from the outside social group. As with acculturation, assimilation involves close contact with other individuals and groups (Teske & Nelson, 1974). Recently, the overall concept of assimilation has been viewed as “worn-out” and possessing a negative connotation, due to the revelation that assimilation also implies an erasure of one’s original culture (Alba & Nee, 1997).


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Cultural assimilation had not been studied in the sport management literature. However, within the leisure literature, Floyd and Gramann (1993) examined assimilation within Mexican Americans living in the southwest United States and found that Mexican Americans who were the least assimilated were most dissimilar to the Anglo-Americans in terms of recreational behaviors. They also found differences in recreational behaviors within the Mexican American sample. Floyd and Gramann suggested that managers should not assume that all ethnic groups are homogeneous and that minority groups are necessarily different from majority groups in terms of behaviors. So what relevance does all of this have to a sport marketer? Sport marketers need to understand the culture of relevant geographic area and understand how that psychographics and demographics represented in the culture could potentially impact marketing and the progression along the consumer pathway. Psychographics. For example, the psychographics (shared cognition and shared affect) of the local/regional market culture could be different when compared to the psychographics of the fan base. Psychographics can include the shared cognitions and affects such as political leanings, values, beliefs, opinions, lifestyles, religion, etc. For example, religion can be a key aspect within cultural and psychographic differences. Essoo and Dibbs (2004) noted that religion influences both behavior and purchase decisions. Their results indicated that there were differences among Hindus, Muslims, and Catholics on various types of shopping behavior (e.g., demanding shopper, practical shopper, trendy shopper, traditional shopper, etc., representing preferred shopping experience). Although significant differences existed among religions on type of shopper, the differences were not large. Because Essoo and Dibbs did not report effect sizes it is not possible to determine how meaningful these differences are. They did suggest that religious affiliation should be included in future cross-cultural research to further examine the influence of religious affiliation on consumer behavior. Fam, Waller, and Erdogan (2004) found differences between Islamic followers and Buddhists, Christians, and non-religious people on perceived offensiveness of advertising for certain types of products. However, the level of how religiously devout the individuals were, seemed to predict advertising attitudes to a greater extent than type of religion. The more devout individuals across all religions felt that advertising for some types of products was offensive. Lindridge (2005) found that people of Eastern religions felt that religion influenced consumer behavior more than people in Western cultures. Again though, this could be a function of the devoutness of the individuals rather than necessarily a function of religion, but Lindridge did not take this into account so it is difficult to know. Let’s go back to our Portland, Oregon, example. In general, people in Portland are not as religious as some places, are more liberal than conservative. Portland is known for being friendly in general and nice to strangers in particular. Portlanders wear whatever they want but will definitely have Gore-Tex on hand because of the rainy weather. People are focused on the environment and on sustainability. It is typically a progressive culture where biking, running, outdoor activities, and civic engagement is encouraged. Copyright 2018 - Galen Trail

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In general, sport fans are more conservative than non-fans and less likely to be engaged in environmental sustainability (Trail, 2016). This may or may not be the case in Portland. Thus, if the psychographics of the general Portland population are not similar to the Trail Blazer fans, it would be important for a Trail Blazers’ marketer to know. Demographics. Similarly, the demographics of the Portland culture could differ from that of the fans of the team. For example, 50.4% of the population in Portland is female, but if 60% of the Trail Blazers fan base is male, then that might impact marketing strategies. Sex and gender have often been used interchangeably when utilized as a demographic variable to segment a market. However, sex is a biologically based distinction, whereas gender is a socially based distinction. Assignation of males and females based on sexual distinction has historically been a dichotomy, although that view is changing. Typically, gender based distinctions are dichotomous as well; however, gender identity exists on a masculinity to femininity continuum. Masculine role identities are typically ascribed traits such as aggressiveness, power-hungry, and a win-at-all costs mentality, whereas feminine role identities are often defined by traits such as nurturing, caring, and cooperative. Interestingly, females above their teenage years have been segmented by marketers by whether they work or not, and whether they want to work or not. Hawkins, Best and Coney (2004) labeled the four-segment market Figure 3.15 modified from Bartos (1977) as Career Working Woman, Trapped Working Woman, Trapped Housewife, and Traditional Housewife (Figure 3.15). The first category, Career Working Woman, consisted of women who were either married or single and preferred to work, rather than stay at home. The Trapped Working Woman category included both married and single women who preferred to stay at home, but for either economic or other reasons, could not. The Trapped Housewife category included women who were typically married and preferred to work but could not because of constraints that kept them at home. Finally, the last category, Traditional Housewife, included women who were typically married and chose to stay at home and focus on family. These categories of women differed on values and life goals (Schaniger, Nelson, & Danko, 1993). Bartos (1994) however, suggested that segmenting women into these four categories was not valuable and she incorporated these four categories with the life cycle to create 16 categories. Interestingly, males have not been segmented into similar categories. I wouldn’t suggest segmenting anyone by such categories as it can be offensive and is typically is not a worthwhile endeavor. Copyright 2018 - Galen Trail

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Similarly, age as a demographic variable within the culture may be relevant. For example, if the majority of Trail Blazers season ticket are older than 55, but the median age of the Portland populace is 36, does that matter how and where a sport marketer should place messaging? As Figure 3.16 Hawkins et al. (2004) pointed out, “age carries with it culturally defined behavioral and attitudinal norms” (p. 118). What this means is that it is not the age that predicts the consumer behavior so much, as it is the beliefs, values, and attitudes that people have, that may change across age. For example, Tequila consumption decreases with age, while Scotch consumption increases with age (Figure 3.16). Neither of these consumptions changes due to age itself, but rather due to different feelings about that product along the life span. As we grow older we have a different view of what we should be, what we should do, and how we should act. If you are like the U.S. population, apparently as you age you will enjoy eating at Marie Callenders, watching CNN, and taking a cruise every once in a while. Oh, and by the way, even though we did not include the information in the graph, Source: Hawkins et al., 2004. you are much more likely to shop at J.C. Penney as you get older (assuming it still exists). Just prepping you so it does not surprise you one day ☺ Sexual Orientation. Acceptance of the LGBT markets has been slow in coming Figure 3.17 but has definitely sped up over the last several years. According to Statista, approximately 4.1% of the U.S. population self-identifies as lesbian, gay, bisexual or transgender (Figure 3.17). This is most likely an underestimate, but even if it is not, this is an untapped market for many businesses. Both American Express and Subaru determined that it was a very attractive market and pursued it effectively (Hawkins et al., 2004). Even though Poria and Taylor (2001) noted that marketers assume that the LGBT market segment has high disposable incomes and high levels of formal education that at least partially explain their behaviors as consumers, it is not Copyright 2018 - Galen Trail

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necessarily true. Hawkins et al. noted that average income for gays and lesbians is only slightly above the national average, but other information disputes that, as a Prudential study in 2012 indicated that LGBT consumers had incomes around 20% higher than the national median. Sport marketers need to understand whether the culture of the overall market differs meaningfully from their sport fan market. For example, Portland, as of 2015, had the second highest percentage of LBGT residents of any metropolitan area at 5.4%, trailing only the San Francisco area. However, the percentage of Portland Trailblazers fans who are LGBT may vary from the general Portland population. To just assume that they are the same would not show a lot of sense. Household income. According to Hawkins et al. (2004), household income does not necessarily cause or explain consumer purchases, but it does enable them. They claim that occupation and education directly influence preferences consumers have for goods and services, and income merely provides the ability to make the purchases. They give this example as to why this is the case. Individuals who make $60,000 a year are five times more likely to buy the New Yorker magazine than someone making $10,000 a year. If that person making $10,000 a year won the lottery and suddenly was bringing in $60,000 a year, that does not mean that they will run out and subscribe to the New Yorker. Although we do not necessarily agree that occupation and education are going to predict consumer behavior better than income, we do agree that income is not a good predictor of most consumption purchases. Hawkins et al. pointed out that on some types of purchases subjective discretionary income (SDI) is an adequate predictor. SDI reflects the consumer’s perception about how much money the individual has to spend on nonessentials. Figure 3.18 Back to our Portland example. The median HHI for Portlanders in 2016 was slightly under $69,000 (Figure 3.18). However, Trailblazers marketers should not assume that Trailblazers fans have that level of household income. It is quite likely that their fans actually have a higher HHI. Regardless, it is important to know what the median HHI for the market is because if the fan HHI is substantially higher than the market HHI, that means the team may be pricing themselves out of the market, a constraint. Occupation. If you think about it, occupation is how many of us segment new people we meet, after we have categorized them based on aspects we can determine visually (e.g., gender, race, age, etc.; personally, I don’t recommend this as misperceptions can lead to incorrect assumptions). When you meet someone new, what is a question you are most likely to ask? Typically, you ask them about what they do. This is categorizing them by their occupation. Knowing someone’s occupation allows other suppositions to be made (perhaps incorrectly). Oh, she’s a computer programmer. She must be a geek, but I bet she makes a lot of money. Ah, he is a garbage collector. I bet he does not make very much money (which Copyright 2018 - Galen Trail

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would be an incorrect assumption at least in Seattle as they average more than $110,000 a year). People tend to associate certain jobs with a certain social status and with a certain level of income. Many businesses have historically segmented occupations by “White collar” and “Blue collar” jobs based on the education level typically needed to perform the tasks and the amount of physical effort required to do the job. This does not make a lot of sense because, first, rather than being a dichotomy, it is a continuum, and second, there are so many exceptions, this type of segmentation does not work that well. That said, consumption differences have been found between Administrative/Managerial, Technical/Clerical/Sales, and Precision/Craft occupational classes. For example, people in Craft jobs are about 80% more likely to drink domestic beer than people in Managerial jobs. People in Managerial jobs are approximately 150% more like to own a laptop computer than people in Craft jobs. People in Sales jobs are about 20% more likely to drink diet colas than people in Craft jobs, but about 15% less likely than people in Managerial jobs (Hawkins et al., 2004). Portland has changed considerably over the last decade, losing plenty of blue collar jobs and gaining lots of white collar jobs. Portland used to be considered a “blue collar town”, but no longer can be thought of as such. Since 2010, 68% of the new jobs added in Portland have paid more than $75,000, and more than half of those pay more than $100,000 and almost all of those are in the tech field. Although sport marketers need to understand what the culture is in their community, it is also critical to understand whether fans perceive the team as blue collar or white collar. The Pittsburg Steelers are definitely considered blue collar, which typically matches the how Pittsburgh the community is viewed. Education. According to the U.S. Census Bureau, 2010, 85 percent of all U.S. civilians were high school graduates and almost 30 percent had completed a bachelor’s degree. Unfortunately, there are still way too many people that are not going to college. As is evident from the graph below based on information from the U.S. Census Bureau in 2010 (Figure 3.19), the greater the amount of education an Figure 3.19


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individual has the more money they are going to make in annual salary. Most people will average almost 70% more in annual salary if they complete a bachelor’s degree than if they stopped after high school. Again, businesses segment by education level because research has shown that differences exist. However, it is more likely due to income level than education level, but because the former is contingent upon the latter, it still works. For example, college graduates are 105% more likely to own a laptop computer than someone who only graduated from high school. They are also 60% more likely to eat at TGI Fridays and over 100% more likely to shop at Eddie Bauer (Hawkins et al., 2004). Figure 3.20 In our Portland example, the level of education of the general populace varied fairly substantially (Figure 3.20), but in general, Portland is a well-educated city with over 50% of its inhabitants having some type of college degree. It probably doesn’t matter overly much to sport marketers except when communicating to potential fans and existing fans in that all communications need to be designed for the appropriate education level. In general, it is important for sport marketers to understand the demographics of the local/regional market and be able to compare them to their own fan demographics. Geographics. The geographics of a locale can also have an impact on fans and spectators on the consumer pathway. Abandoning the Portland example for a bit, New York is comprised of five boroughs: Manhattan, the Bronx, Queens, Brooklyn, and Staten Island. People in different boroughs have different feelings about different teams in the New York area. In addition, travel from certain boroughs to sport venues in other areas of New York City can be difficult depending on location. Seattle, Washington, has much larger geographical constraints. Surrounded by large bodies of deep water on two sides, with access only by ferry on the west (without driving a considerable distance) and with access from the east only by bridge across Lake Washington, travel times to the downtown sport venues are rather long. In addition, mountain ranges on the east also have forced housing to be primarily north and south of Seattle. This of course creates tremendous traffic issues coming to games from the north or south. Other cities have Copyright 2018 - Galen Trail

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geographical factors that may influence fans and spectators, and marketers for those sport organizations must understand whether these factors have any negative impacts, and thus might be external constraints. In addition, sport marketers need to understand where their fans may live within the general geographical area, but we will talk about that more in another chapter. Another aspect that interacts with geographical aspects to impact traffic and travel is population. Obviously, if travel options are plentiful and there is no one on the roadways, then travel times will be better than in a densely populated area. Cities that are densely populated typically will have poorer travel times, but it is dependent on public transit as we will discuss below. One of Portland’s problems is that they have been a top-15 population-growth city for several years running. The metro area population is around 2.35 million currently, with approximately 640,000 living within the city limits. Trailblazers sport marketers need to know these numbers so that they can know their market size, but they also need to understand how population growth may impact existing fans’ transportation issues. As noted above, public transportation should be included in the geographical category. If we go back to our Portland example again, back in 2011, Portland was ranked #1 in the U.S. for public transit. Portland is also known as a very bike-friendly city with approximately 6% of the population commuting by bike every day. This exceeds the national average of 0.5% by 1200%. In addition, it is ranked as one of the top 10 walkable cities. However, since 2011, Portland has added thousands of people and is expected to add another 140,000 to 260,000 in the next 20 years. Their public transit system is already overloaded after only 7 years of growth since 2011. Not all of those new additions are going to want (or be able) to bike or walk to work. If Portland doesn’t do something soon, it will negatively impact the city’s economy and all of the businesses. Trailblazers’ sport marketers need to keep track of these developments and how it impacts their fans. If a large percentage of fans take public transit to games and it becomes too much of a chore and driving isn’t a viable option, it could substantially impact attendance. So not only do their sport marketers need to know the status of transit within their area, they need to know the impact on their fans to determine if it is a potential constraint. Regional weather and climate are also aspects that can impact fan interest and attendance at events, and sport marketers need to understand the potential impacts. For example, Portland is a fairly temperate climate, it rarely snows, but it tends to rain a lot! Not so much in number of inches, although it does get 44 inches a year which is above the U.S. average, but the same as Boston, Massachusetts, but more in terms of just being gray and drizzly. Portland only gets 144 sunny days a year, which is similar to Seattle, not surprisingly, and Buffalo, New York, which may be surprising to some (it was to me). Climate and weather impact how people feel and how interested they are in going to games. In some cities, Green Bay, Wisconsin, for example, snow causes people to attend the football games just so they can prove to the rest of the NFL that they are the hardiest NFL fans, although Buffalo Bills fans may beg to differ. On the other hand, if it is sunny out during the summer in Seattle, Seattle Storm (WNBA) fans often prefer to spend their time outside getting their vitamin D. Regardless, sport marketers need to understand how the local weather and climate impacts attendance. Once the sport marketer has identified all of the relevant information specific to the external environment, (s)he should create a document that synthesizes that information so that it can be used as a guide to creating marketing communications later. In addition, it should also be used as an internal document within the sport organization to rationalize and support the marketing campaigns when proposing them to others in the organization (i.e., upper management).


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Overall Assessment of Environmental Insights I typically use a tabular format when I am creating a compilation of external environmental insights for the sport organization, but you should use whatever works best for you. I make an External Environmental Insights heading on the table and then include information specific to the environmental categories that I discussed above, in the separate rows. It is easiest to show in an example (see below). This example is only going to include a sampling of External Environmental Insights. I have created a fictitious professional (minor league) basketball team playing in Renton, Washington, U.S.A., a suburb, just south of Seattle. The team is called the Renton RoadRunners and they play in a venue that is owned by the city of Renton. I have created all of the material from existing data from other teams and have adjusted it to fit this scenario. Some of the information I had to make up so that it would make this example work. In addition, I am going to use tables to depict the information. However, there are many different ways of organizing the information as noted. External Environmental Insights Economy

– Healthy local economy in the greater Seattle area, lots of disposable local income available in Seattle, but less in Renton. Renton has a poorer populace, and less disposable income. - Inflation rate around 4.2% – Unemployment rate is 5.2% – Median home value = $357,808

Market Trends/Forecasts

– The number of minor league basketball teams is increasing – The NBA D-league (now G-league) set an attendance record in 2016-17 – More than 550 NBA D-League games were streamed live exclusively via Facebook Live in the 2016-17 season – The NBA D-League set records across social media for impressions (1.1 billion, +98%) and video views (102 million, +22%). – A record 13 NBA D-League teams secured jersey partnerships for the 2016-17 season

Local/Regional Market (not specific to team) Direct Competitors

Cross-over Effects


– The RoadRunners have a few direct competitors. – Since there are no professional major league men’s basketball teams (i.e., NBA) in the geographical area, the only men’s basketball teams are either college (University of Washington, Seattle University) or high school. The only professional women’s team is the Seattle Storm. Other minor league teams that might be competitors are the Seattle Thunderbirds (minor league hockey) that operate in Kent, WA, the Tacoma Rainiers (minor league baseball), and Seattle Sounders S2 (minor league soccer) in Tukwila. – 36% of avid RoadRunners fans are Storm fans, but only 14% of avid Storm fans are RoadRunners Fans – 27% of avid RoadRunners fans are SU Men’s basketball fans, but only 5% of avid SU Men’s basketball fans are RoadRunners Fans – 37% of avid RoadRunners fans are UW Men’s Basketball fans, but only 6% of avid UW Men’s basketball fans are RoadRunners Fans – 18% of avid RoadRunners fans are Seattle Thunderbirds fans, and 20% of avid Seattle Thunderbirds fans are RoadRunners Fans – 0% of avid RoadRunners fans are Tacoma Rainiers fans, and 0% of avid Rainiers fans are RoadRunners Fans

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– 26% of avid RoadRunners fans are Sounders2 fans, but only 7% of avid Sounders2 fans are RoadRunners Fans Growth & Share Analysis

Competitive Analysis


– Product quality: The RoadRunners have been relatively successful, making the playoffs the last three years, but the product has been rather boring as the team played a slowdown offense. The coach left at the end of last year and the new coach has implemented an up-tempo game. However, the potential success for this is unknown. The UW men’s basketball team has not been successful recently either, but is also under a new coach and doing better. Similarly, the SU men’s basketball team hasn’t been very good, their coach is new this year as well, and doing better. The Seattle Storm replaced their coach mid-year and did not make the playoffs, but are doing much better this year. The Thunderbirds have been very successful recently, but The Rainiers and Sounders2 have not. – Pricing: Prices for all of the basketball teams and the RoadRunners are similar on average, although courtside seats for UW and for the Storm are considerably higher. – Place (distribution): The RoadRunners play in a city owned arena that is several decades old, but comparable to the one that the Seattle Thunderbirds play in (ShoWare Center). It is not as nice as either Key Arena (Storm and SU men’s basketball) or Alaska Airlines Arena (UW men’s Basketball). – Promotions: The RoadRunners do a very poor job with promotions/advertising. The Storm, UW men’s basketball, and the Thunderbirds do more and better. SU men’s basketball does not.

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Indirect Competitors

– Outdoor winter sports on weekends – Movies in theaters. – Going out with friends (bars & restaurants).

Growth & Share Analysis

N/A

Competitive Analysis

– Product Quality: N/A, varies too much for indirect competitors – Pricing: Outdoor winter sports (downhill skiing) typically cost more per person ($60) than attending a RoadRunners’ game ($23, ½ off on Tuesdays). Snowshoeing and cross-country skiing are typically cheaper, as are family sledding etc. Movies are cheaper in general if attending at a theater, except for on Tuesdays for the cheapest RoadRunners tickets. Can’t compare to going out with friends. Varies too much. – Place: Outdoor winter sports on a great winter day may have an advantage; on the other hand, if it is raining in the mountains, then attending a RoadRunners game might be better. Some of the nicer movie theaters are better than the RoadRunners venue. Some restaurants and other places that one might go to with friends, may have better venues, but it varies. – Promotions: Movies and theaters typically have better promotions. Some winter sports (e.g., downhill skiing at some of the resorts) have better promotions. Some restaurants or other places that potential spectators might go out to might have better promotions.

Cultural Aspect Psychographics

– Fairly moderate politically, – Environmentalism highly valued, but more-so in Seattle than in Renton – Lifestyles: Enterprising Professionals (15%); City Lights (9.7%); Metro Fusions (8.9%); Pleasantvilles (7.3%), Bright Young Professionals (6.4%), Parks and Recs (5.0%)

Demographics

– Median age = 35.5 – Male/Female ratio = 1.0/1.0 – 51% married – 52% Caucasian, 10% African American, 22% Asian – More blue collar, 43% work in services industry, 17% in manufacturing, 12% in retail – Median HHI is $65,000. – 27% are in the HH Income bracket of $60K-$100K, 17% between $40K-$60, 16% between $100K-$150K – Median net worth = $70,867 – Per capita income = $31,076 – 41% were born in the state of Washington – 20% are high school graduates, 33% have a bachelors or higher – Average spent on entertainment/recreation = $3,438 – Average spent on admission to sporting events/year $68.68 – Average spent on cable and sat. TV services = $907.97

Geographics

– Suburb of large U.S. city (Seattle), – Populations 98,678 – Arena in retail/industrial area, – Bus available, train not available close to arena. Light rail not close to arena. Most people travel by car – Temperate, but consistently rainy


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– Poor bike system in Renton, but one main trail available close to arena – Not overly pedestrian friendly over long distances, but not too bad near arena Opportunities and Threats

Opportunities: 1. For people interested in decent quality basketball during the winter, there probably is no better option currently. Of course, this depends substantially on how each of the teams does under their new coaches. 2. If the team becomes associated with an NBA team, this could be a huge draw. 3. They are only at 73% capacity, so plenty of room to increase attendance. Threats: 1. For sport fans in general, who have no preference for either basketball or hockey, but just want to go out to a game, the Thunderbirds are a very realistic threat. They have had more success recently. Up until this year, their style of play is much more exciting. There is more violence at hockey matches. All of these things draw the sport fan who has no allegiance. In addition, the venues are less than 15 miles apart. 2. UW basketball has the potential to draw on a much bigger fan base overall (alumni). If they can put a successful product on the court, it will probably negatively impact the RoadRunners a little.

Summary Sport marketers need to remember that all of these external environmental factors affect both the organizational environment and the customer environment, in addition to consumers progression along the consumer pathway. Furthermore, sport marketers need to make sure that they assess all of the different aspects in each of the different components of the external environment’s context and culture, and determine which aspects could be constraints. To understand how these aspects impact progression along the consumer pathway it is necessary to understand how people become socialized into being sport fans, so on to the next chapter.


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Fan Socialization

Galen T. Trail Copyright 2018 – Galen Trail

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Fan Socialization

Chapter 4 Fan Socialization Excerpt from an interview with a Cleveland Browns’ fan by Jeff James. Jeff James: Why did you become a Browns’ fan? Cleveland Browns Fan: “…as far back that I can remember,…it had to be the early 1960’s, I forget what year it was…but just sitting there with my grandfather, my father, my brother, and myself. I mean as far back as I remember I’ve been a fan…”

Copyright 2018 – Galen Trail

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Fan Socialization Jeff James interviewed that Cleveland Browns fan about two decades ago now. It would be interesting to interview that same fan as I write this chapter, considering that the Browns just finished the 2017 season as only the second team in NFL history to go 0 and 16. Similarly, it would be interesting to know if the fan holding the sign in the picture on the prior page, was still there as the last home game finished last season. As we discussed before, at least a little bit, sport consumers can be categorized by their level of fandom (i.e., the degree they are a fan or not), and it is absolutely critical for marketers and managers to know whether someone is a fan of their team. This knowledge will influence marketing plans, ticket sale strategies, management of seating patterns, social media strategies, communication campaigns, and many other activities. The level of fandom influences almost all purchases. Thus, it is critical to spend some time discussing how the external environment influences people to become fans in the first place. People do not just wake up one morning with absolutely no exposure to, or knowledge of, a team and decide to become a fan of that team. Something external to the individual, something in the external environment, must be the impetus that creates the initial introduction to the team and the potential start of fandom. Think about your own experiences, what are some of your earliest memories of sports? Do you think about playing catch with your mom or dad, or maybe playing basketball with your brother or sister? Do your early memories include watching a specific team on television with your dad or perhaps your friends? How did you learn about different sports such as football, baseball, basketball, or soccer? Did you first learn about a particular sport, say soccer, by playing at school? If you grew up in a northern climate perhaps the sport that everyone played or followed was ice hockey. Think about where you are at now; if you live in a different city from where you grew up as a child, is there a college or university sport program you now follow? Is the city home to a professional sports team? Have new friends introduced you to specific sports teams? Try and think back to when you first became a fan of a team, or even if you more recently became a fan of a different team; what caused you to become a fan of Figure 4.1 that team? Typically, there are a variety of factors in the external environment and potentially from the organizational environment that influence our connection to sport (Figure 4.1). Parents, siblings, friends, a local community, one’s school, even the media can influence our connection to teams or sports. These individuals and groups are socializing agents that influence how we become a sports fan. The idea of fan socialization is really an understanding of how and why an individual becomes a fan of a particular sports team.


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Fan Socialization Socialization – A fundamental understanding Socialization is a process through which people learn the attitudes, values, and actions appropriate to individuals as members of a particular society (Kenyon & McPherson, 1973). Socialization occurs throughout a person’s life and in different contexts; through socialization we each learn how to live and function within a given society. Said another way, we learn the different roles in society and how to live and act within various roles. We all fulfill multiple roles; we start out in the role of a child and grow to adolescence, young adulthood, and into adulthood. One person may be a student, another an employee and all of us are likely a friend to another person. Figure 4.2 illustrates different sport roles we may fulfill, including being a fan of a sports team. Of interest to us are the secondary consumer roles (highlighted in blues in Figure 4.2), attending games in person, following a team through some mediated form, and buying apparel and merchandise to demonstrate our connection to a favorite team. With respect to becoming a fan of a specific team and the role of socialization Kolbe and James (2000) explained: Figure 4.2

…socialization involves learning and internalizing the attitudes, values, knowledge and behaviors that are associated with fans of a team. This process results in individual-level internalizations and an appreciation of the importance of being a fan of the team. Socialization theory would suggest that an individual’s personal characteristics, significant others, and social settings contribute both independently, and in combination, to the development of knowledge, values, dispositions and self-perceptions requisite to being a team fan. These ideas indicate that becoming a fan involves more than merely focusing on team-related factors, but necessitates that some consideration be given to the effects of interpersonal factors and social environments… (p. 3). In the next section we examine what we do know about becoming a fan.


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Fan Socialization Fan socialization – What do we know? Wann, Tucker, and Schrader (1996) completed one of the first efforts to identify factors that influenced people to identify with a favorite team. The results indicated that a variety of reasons underscore the origination, continuation, and cessation of team identification. For each of three types of team identification (i.e., currently, originally, and ceased following a team) over 40 different reasons were listed. The success of the team, geographic proximity, the players, and other affiliative reasons were frequently cited (Wann et al., 1996). In a study examining how a person becomes a fan of a professional sports team, Kolbe and James (2000) conducted interviews, focus groups, and a mail survey with fans of the Cleveland Browns. The fans participating in the study were asked to identify the one person who had the most influence on them becoming a Browns fan, and how important various activities like attending games in person were to them becoming a fan. A series of questions were also included to try and gauge how important it was to be a fan of the “home team.” Among the Browns fans, 38.8% reported “father” as the one person most influential in them becoming a team fan. Approximately 56% of the respondents reported they had become true fans of the team prior to age 15, and most indicated becoming a fan between 6 and 15 years of age. Individuals in their preteen and adolescent years maintained that fathers had the most influence. Adult fans indicated a distinct difference from the other groups in that players and coaches contributed most to their attachment with a professional football team. Those participating in the study also talked about the importance of being a team fan because of the emotional connection between the city and the team. Another study on becoming a team fan was conducted by James (2001); he talked with two groups of children (aged 5-6 and 8-9) in an effort to determine what factors influenced the children to form an initial connection to a specific sports team. James found that family members, especially fathers, had a strong influence on a child learning about and thinking of a specific team as his or her favorite team. Playing a specific sport influenced a child’s interest in that sport and watching sports on television was also found to contribute to children liking specific teams (James, 2001). How the various socializing agents may exert an influence on a person to become a team fan is discussed in the next section.


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Fan Socialization Fan Socialization across the Lifecycle An important conclusion from the limited study of fan socialization is that there are different agents that can influence whether a person forms a psychological connection with a sports team. Another important point to recognize is that fan socialization can occur throughout the lifecycle. A “lifecycle perspective” is used to consider how the various agents may influence fan socialization, and to what extent specific agents may have more or less impact at different points in one’s life. There Figure 4.3 are different representations and usages of the lifecycle concept. The notion of a product lifecycle is utilized in marketing and refers to the introduction, growth, maturation and decline or further expansion phases of a product over time (Johnson & Summers, 2005; see Figure 4.3). Another usage is found in the family lifecycle concept which describes how a person progresses through different life stages beginning when one is a young single, moving on to young, married with no children, then young, married with children, etc. (Shank, 2005). From a biological perspective, the concept is associated with various stages in life beginning with birth, going through childhood and into adulthood. We will be discussing fan socialization in relation to four cycles modified from a biological perspective: childhood (birth through preadolescence), adolescence (approximately 10 to 18 years of age), young adult (college years through 20s), older adult (30s and above). The perspective we have chosen is by no means the best or the only approach with which to discuss becoming a fan. The four-stage perspective does, however, allow us to focus on key life stages and explain how the various agents may influence a person’s connection to a sports team relative to each stage. As you read the following sections you will likely recognize how the content could quickly become very complex and convoluted with a more detailed lifecycle framework. If we think about becoming a fan based on a lifecycle perspective, most people who become fans are socialized into the role by six socialization agents or mechanisms, either individually or in some


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Fan Socialization combination, from the external environment. The six primary agents illustrated in Figure 4.4 include family, friends/peers, the media, geographical location (community), participation in a sport, or association with an organization. Others, for example, Wann and colleagues (Melnick & Wann, 2010; Figure 4.4



Parry, Jones, & Wann, 2014; Theodorakis, Wann, Al-Emadi, Lianopoulos, & Foudouki, 2017) have not focused the socialization agents as concisely as we do here. They have 18 or more agents, which can be collapsed into our six. In addition, some researchers have suggested that entities like star/famous players (Parry et al.; Spaaij & Anderson, 2010) can socialize someone into being a sport fan. I don’t think so. The agents or mechanisms that we have listed are entities that introduce the team to the individual. It is highly unlikely that a star player would be the agent to introduce the individual to the team. It is remotely possible that Joe Schmoe is walking down the street and happens to bump into Ronaldo. Joe apologizes, and Ronaldo says “Don’t worry about it. No harm done. I can still play in my match tomorrow.” Joe says “What match?” Ronaldo then explains that he is the most famous football player in the world and he plays for Juventus F.C. He goes on to tell Joe all about his new team and how much better it is than Real Madrid was. Joe is amazed and decides to become a Juventus fan. In that specific case, a player could be a socializing agent. However, if Joe sees a commercial with Ronaldo, or happens to turn on the TV and see Ronaldo score a goal for Juventus, and decides he likes Ronaldo, and therefore Juventus, then the socializing agent is the media (TV in this case) and not Ronaldo because the media provided the initial mechanism for Joe to be introduced to the team and/or sport. Similarly, if Joe’s friend takes him to a game, the friend is the socializing agent, not the star player. Some of my students have argued that they would never have become a fan of a certain team if a player


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Fan Socialization hadn’t been traded to or signed by that team, therefore the player was the socializing agent. They almost had me convinced until I went back to the definition of socialization that I provided above: “Socialization is a process through which people learn the attitudes, values, and actions appropriate to individuals as members of a particular society” (Kenyon & McPherson, 1973). It is unlikely that most people would learn the attitudes, values, and beliefs of that particular team’s ‘society’ from the player. It is more likely probable that the attitudes and values, etc., are learned through friends or family or the media. The point being, the likelihood that a player is the socializing agent is so miniscule, it shouldn’t be considered. Similarly, Wann and colleagues have also suggested that someone can socialize themselves into being a fan. This, too, is not possible as there must be some intervening mechanism that creates the awareness of the team from the external environment, for example the media, a family member, or a friend. That said, it doesn’t mean that interest in a player cannot draw someone into being a fan of the team that the player plays for, however, that is not socialization, but it is a motive for why someone may become a fan of that particular team. We will cover those types of motives, which we call points of attachment, in a future chapter. Now though, let’s move back into a much more in-depth discussion of how the various agents from the environment may influence the fan socialization process across the lifecycle. Childhood (Birth through approximately 9 years) An important point to consider regarding fan socialization and childhood is the role of children as consumers. While children may have money to spend, in relation to purchasing apparel, other sport products, or spending money to attend games, parents are the key consumers. It is important to consider though, how children learn about and come to value sports and sports teams during childhood. During childhood a person may identify his or her sport and/or team, so it is important to consider how the various agents contribute to fan socialization during childhood. In the early stage of the life cycle, an important function of socializing agents is introducing a child to sports and sports teams. The work by Kolbe and James (2000) and James (2001) found that family members, particularly fathers, were an important influence during childhood. Wann and colleagues have typically found that fathers have been the most influential socialization agent as well. Across multiple data sets, they have found that fathers were the most influential agent for 35% to 49% of people. This ranged across countries including, the UK, Greece, Australia, Norway, and the United States (Parry et al., 2014). The exception was Qatar, where fathers were second most influential and only influenced 12.6% of the fans (Theodorakis et al., 2017). The importance of family in the process of fan socialization is fairly intuitive. Beginning with the first day of life, parents pick what clothes a child will wear. For some parents, it goes without saying that a child will wear clothes bearing the logo or symbol of the parents’ favorite sports team. Children are introduced to and learn about sports and sports teams based on Photo by Brian Turner their parents’ interest(s) even when they may not be old


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Fan Socialization enough to understand what sports and sports teams are. Parents, particularly fathers, may introduce and further influence fan socialization by playing games with a child, talking about a favorite team, watching the favorite team’s games on television, and by taking a child to watch a game in person (Kolbe & James, 2000; James, 2001). The various activities described may collectively reinforce the importance of a particular team. A child that is given team apparel to wear, who listens to his or her father talk about a favorite team, watches the team’s games on television, and who attends the team’s game, at the least will know about the specific team. That child will likely form a preference for a particular team and potentially become a fan early in life. The findings from Mewett and Toffoletti (2011) support these ideas as in interviews with female Australian Rules Football fans they determined that parents were the primary socializing agents early in life. In essence a child is not only introduced to sports and teams but learns to value a sport or a specific team as well. The introductory role may also come from other family members. A mother, grandfather, sister, or perhaps a favorite uncle could fill the role of introductory agent. However, these potential socializing agents pale in comparison to fathers in the data collected so far. Mothers typically were only influencing agents in around 4% of people according to Parry et al. (2014). Brothers were slightly higher with an average of around 7% of people influenced. However, even though siblings may play a role in introducing a younger brother or sister to a sports team, it is likely that the sibling’s choice of a favorite team has also been influenced by the parents, especially at this stage of the life cycle. Other extended family members have negligible impact of fandom choice, except in Greece, where uncles apparently hold a lot of sway, as 5% of all Greek fans indicated that they were influenced by an uncle (Parry et al., 2014). An important point to keep in mind regarding fan socialization during childhood is that the family, particularly parents have a very strong influence on a child’s introduction and connection to a sport and/or a sports team During childhood, particularly the preadolescent years, friends or peers may be expected to have a weak or no influence on fan socialization. James (2001) found that children may talk about their favorite teams and when playing sports imagine themselves to be players on a particular team. When it came to identifying a favorite team, however, children were more likely to talk about their father’s favorite team, not a friend’s favorite team (James, 2001). Instead of introducing a child to a specific team, friends may serve another role by introducing a child to different sports. James (2001) found that children may play with friends and learn about a sport their parents are not familiar with, soccer for example. In this sense the friend influences introduction to a sport. At the same time the sport activity may also serve as a socializing agent.


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Fan Socialization A child may learn about a sport through participation and subsequently become interested in teams that play the sport. For example, a child may enjoy playing soccer with his or her friends and become interested in watching a Major League Soccer team. At the least, children that have participated in a sport may be more likely to follow that sport even when not participating because they have some knowledge of the sport. Any sport learned early in childhood has the potential to become a sport that a person consumes later in life. A child that plays Little League may become a fan of baseball and a specific team even if the parents are not baseball fans. In fact, my son became a Florida Marlins “fan” for a year when his Little League team was the Marlins and was wearing the Marlins’ team colors and hats. He wanted to know how the Marlins were doing, whether they were winning, etc. This attachment to the Marlins lasted only one year though; as soon as the next season came around and his team was no longer the Marlins, he no longer was a fan. In terms of consumption, however, until a child has the resources to purchase tickets, apparel, etc., his or her direct consumption will be directed by the parents. The media may exert some influence during childhood by introducing a person to sports and teams or reinforcing interest in a sport or team in relation to another agent. As mentioned, a child may watch games on television with his or her father. The initial introduction would have come from the father, but the media would function as a tool to reinforce the connection. James (2001) found, however, that a child may learn about and subsequently develop an interest in a sport in which the parents were not interested. In one interview James learned that a girl participated in equestrian events; she explained that her initial interest came from watching events on television. None of her friends or family members had an interest in equestrian events prior to her involvement. With the availability of information via the Internet, and sport organizations providing content through that medium, the media may become a more important fan socialization agent in the future. Geographic location may exert some influence during childhood, but it would likely be in conjunction with another agent such as one’s father. For example, if you grow up in a town that is home to a professional basketball team you are likely to learn about that team from others and potentially it may be your father’s favorite team. Friends may talk about a team, and even the sport played by the team, because the team is part of the community. Individuals growing up in Green Bay, Wisconsin, cannot help but learn about football and the Green Bay Packers because of the prominence of the team in the community. These ideas were verified by Wann et al. (1996) in their assessment


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Fan Socialization of the origination, continuation, and cessation of team identification. However, in Parry et al.’s (2014) research, they found considerable differences by country on the influence of geographic location. In their research they called it community socialization, which makes sense, but in British, Greek, Australian, and United States fans, fewer than 2% in each sample indicated that community was a socializing agent. However, a whopping 22% of Norwegians indicated that the community socialized them into fandom. The final agent to consider, organization, will likely exert only a weak influence during childhood. The influence is thought of in the context of one’s association or connection to an organization. For example, I may be a fan of a specific university football team because I was employed by that university. For children, the influence of organization would likely be indirect, through the influence of a family member or friend. My son is a huge Ohio State Buckeye fan because I went to school there for my Ph.D. when he was in kindergarten through 2nd grade. His fandom was an extension of my association with the

specific organization, the university, probably because I hauled him to campus a lot when I didn’t have childcare and his mom was working. He probably spent as much time in the gyms and libraries on campus as I did. Another perspective from which an organization might be an influence is through community sport programs or sports played in a school program like physical education. I referred earlier to the idea of children learning about soccer from a friend; that participation with a friend might be in a community recreation league or through a physical education class at school. For the former, the influence of family, particularly parents, must still be recognized. Children, especially young children, participate in recreation programs based on mom and dad’s approval. Regarding school activities, at the least, there may be an introduction to various sports, again what we might refer to as “non-traditional” sports. More likely though, school programs will focus on activities to promote fitness and health, not sport competition, and certainly not a specific sports team. Some of Wann and colleagues’ research supports this as around 712% of the fans from Britain, Australia, Norway, and the United States indicated that school socialized


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Fan Socialization them in one way or another. However, it is not clear at what level in school this was. Apparently, there is no socializing in the Greek school system as 0% of the fans indicated such (Parry et al. 2014) and less than 1% of fans from Qatar indicated that school was a socializing agent (Theodorakis et al., 2017). Adolescence (Approximately 10-18 years of age) The adolescent years are marked by efforts to develop one’s own identity and to make decisions independent of one’s family. These are, more or less, the infamous teen years. The teen years are often characterized as a time when parents and children have different ideas about what is important and what they may value. In relation to the stereotypes associated with the teen years, one would expect friends to have a greater influence on fan socialization than parents. For example, a teenager may like the same team as his or her friends, even if that team is different from the family’s favorite team. McPherson (1976) studied high school juniors in an effort to determine what agents influenced their role as sport consumers. The behaviors included were attending games, watching games on television, listening on the radio, and talking about sports and teams with others. McPherson found that peers had the most influence on the sport consumer role for males, followed by family, then school (or organization). There was no significant influence from geographic (community) sources. For females, family had the most influence, followed closely by peers, then geographic (community) sources. There was no significant influence from school (organization) sources. The information about male and female adolescents is interesting in relation to the differences found. Family was considered an important influencing agent for females but not males. This may be due in part to the stereotype associating sports with males more so than females. It should also be noted that the McPherson’s (1976) data collection took place prior to the full implementation of Title IX of the Education Amendments of 1972 act in the United States. Much more recently, Melnick and Wann (2010) found that there were gender differences as 53% of males indicated that their fathers were a socializing agent, but only 34% of females did. Females indicated the second most influential agent was brothers (11%), but for males, brothers were a socializing agent for only 6%. In addition, 10% of females indicated that mothers were a socializing agent, but only 1% of males said so. Finally, 10% of females indicated that school was a socializing agent but only 5% of males indicated this. Similar gender differences were found in the Parry et al., (2014) research, but fans in Qatar, not surprisingly, differed quite a bit with males indicating that friends/peers (17%) were the primary agent, whereas only 8.5% of females thought so. In Korea, Yoh, Pai, and Pedersen (2009) found the reverse, as females indicated that friends and family were more influential than males did. Media was not a socializing agent in the work reported by McPherson (1976), nor in Kolbe and James’ (2000) work, where they did not find any influence from media in the creation of a team fan. However, the more recent work by Parry et al. (2014) did find a small percentage of fans were socialized into being fans by the mass media. Norwegians (7%) were socialized by media the most, followed by Americans (5%). Yoh, Pai, and Pedersen (2009) also found that different types of media influenced creation of fandom differentially, and it varied by age of adolescent Koreans


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Fan Socialization (see Figure 4.5). For example, Yoh et al. found that for 10-13 year-olds, television had the greatest impact Figure 4.5

Impact of Socialization Agents by Age Level for Korean Adolescents 4.5

MEAN SCORES (1-5 SCALE)

4 3.5 3 2.5 2 1.5 1 0.5 0

Age 10-13

Age 14-16

Age 17-19

TV

3.96

3.98

4.06

Internet

2.91

3.53

3.62

Friends

3.36

3.51

3.34

Family

3.38

2.73

2.43

Newspapers

1.78

2.63

2.82

Magazines

1.65

2.11

2.12

of any socialization agent, even more than family. However, print media (newspapers and magazines) had the least impact of any agent. The internet was fourth, behind family and friends. Remember thought that this data was collected sometime before 2009 when this was published, probably in 2007 or earlier. I would imagine that social media would be considerably more impactful now. That said, for middle adolescents, those 14-16, TV was still the primary socializing agent, but the internet had jumped to second, followed by friends, and then family. Print media was still last, but the mean scores for newspapers and magazines were getting closer to the other agents. Yoh et al. also found similar results for older teens (17-19). The only difference from the middle group to the older group, is that newspapers overtook family as the fourth most important socializing agent. Casper and Menefee (2010) found that when college students reflected back to their adolescence, their viewership behavior in sports was primarliy influenced by friends (M = 4.22 on a 5-point scale), closely followed by the media (M = 4.08) and their fathers (M = 3.99). Although Casper and Menefee do not indicate whether there were significant differences, it is unlikely due to the standard deviations (~ 1.0). Similar results were found for participation in sport. Friends were the primary influencer, followed by fathers and then the media. The same three socialization agents impacted sport paraphernalia purchasing when in adolescence, but the order was slightly different, with fathers exerting the greatest influence, closely followed by friends and the media. In all cases, media influences were in the top three socialization agents across viewership, participation and purchasing behavior.


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Fan Socialization One aspect of media to consider in the future, however, is the influence of sport video games. Media as a fan socializing agent could introduce someone to a sport if she or he were not already familiar with a particular sport. More likely, a person could increase his or her knowledge by playing a sports video game. The effort to make video games more lifelike could also influence an individual to follow a specific team. Playing a game and “being part of” a particular team could result in a person following the real team. A gamer may ultimately become a consumer of a sport or team. The idea that consumers may be socialized into a sport or into a connection to a team from playing video games, however, has yet to be substantiated as far as I know. As McPherson (1976) noted, during adolescence, organizations have some influence upon males, but no influence on females. Parry et al.’s (2014) data disputes that, if schools are considered organizations, as a larger percentage of females (almost double that of males) were socialized into sport through schools. High schools, at least in the United States, likely have the most influence from an organizational standpoint. Many people support their high school’s teams throughout their lives. However, most of those who graduate from college transfer their allegiances to the college or university, and their allegiance to a high school decreases over time. The influence of geographic location on fan socialization among adolescents is likely to be weak or to have no effect. In the case of an individual that has grown up in the same city, if there is a prominent sport or team, interest in the sport or team will likely have formed during childhood. Geography may be an influence when a person’s family moves to a new community and following a


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Fan Socialization particular sport or team could facilitate an adolescent fitting in with his or her peers. In this scenario, the peer influence is of primary importance and has an impact in conjunction with the geographic location. Sport participation as an influencing agent into sport fandom may also be inconsequential at this stage in relation to immediate consumption. It is possible that participation in sport may be a reinforcing agent. The sports and teams peers talk about may be the sports they play. Another possibility to consider is that the sport a person plays during adolescence may be a sport the individual follows later in life. A question of interest is whether a person that participates in a particular sport in high school or perhaps college will be a consumer of that sport later in life. Jeff James, a coauthor in earlier editions of this book, once sought to find out if past participation in wrestling influenced a person’s attendance at college wrestling meets later in life. People attending a university wrestling meet were asked to participate in a consumer survey. Various questions were asked, including the importance of past participation in wrestling. The particular meet was chosen because it was expected to have good attendance numbers and the program was part of a university in the Midwestern United States, an area in which high school and college wrestling is popular. The people participating in the survey were asked how much influence past participation in wrestling had on their attendance at the specific wrestling meet. A similar study was completed with college softball and baseball. The results illustrated in Figure 4.6 indicate that attendance was influenced by past participation for (approximately) 20-30% of those responding. Additional research is needed to more fully explore the role of past participation. These preliminary findings, however, suggest that past participation may be an important factor influencing a consumer’s later connection to and subsequent consumption of a sport. Figure 4.6 Perceived influence of having participated in a particular sport in high school on attendance at a college game or meet.

Percentage of people surveyed for the three sports Wrestling

Softball

Baseball

Little to no influence at all

46%

47%

42%

Some influence to influential

22%

34%

36%

Extremely influential

32%

19%

22%


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Fan Socialization Young Adults (College years through the 20s) The young adult stage includes a time when many individuals are finishing college, moving out on their own, and beginning the careers for which they have trained. As people reach adulthood, it is likely that family has a declining influence, perhaps no influence, on fan socialization. It is reasonable to expect that young adults have moved from their parent’s home, and are making decisions on their own. One aspect of family that may be important, beginning in this stage and continuing through the later adult years, is the influence of spouse. A possible scenario is that when people marry or commit to a significant other, the other person’s interest in a sport or team may be an important influence. For example, wives may watch or attend sporting events with husbands (or vice versa) in order to spend time together (Funk & James, 2001). One spouse may not have had an interest in sports or teams previously, but their spouse’s interest may influence the fan socialization. Mewett and Toffoletti’s (2011) research supported this hypothesis. They found that some female fans of Australian Rules Football were socialized into fandom due to their partners either taking them to games or just talking about the AFL with them. During this stage friends and peers are likely to have a continued influence on fan socialization (Casper & Menefee, 2010). If a person has moved to a new place or is making new friends, a desire to fit in may impact one’s fan socialization (Funk & James, 2001). In order to make friends or fit in with coworkers, for example, we are likely to talk about sports and may watch and/or attend games together. Interest in a specific team may emerge or further develop because the people we spend time with like a particular team. Considering this stage marks what we can think of as new beginnings for many people, geography may also be an important influence. If a young adult moves to a new location, the presence of a prominent sports team may be important. As just described, talking about the team, attending games, watching games at a sports bar, etc., may function to help a person make friends and feel like she or he belongs. In this sense, there is a type of interaction between the influence of friends and peers with geography, which Mewett and Toffoletti’s (2011) research supports. Media may function within this scenario as well. If a person is new to a location, the media may provide information about sports and teams. Casper and Menefee (2010) found that media was either the primary or secondary influence for viewership, purchasing, and participation in sport for this age group. These types of influences likely operate throughout the adult years. An organization may be an important agent influencing young adults. It is especially important to consider the collegiate setting for those in the late teen years and early 20s. Attending a college or university is likely to result in a person becoming a fan of a sport and/or team based on an attachment to the school. Heere and James (2007) have proposed that a person may identify or connect with a sports team because the team represents or symbolizes other relevant identities, such as a university identity. An individual may develop a stronger connection to his or her university sports teams near the end of the college years. Following one’s college team may provide a way to connect with old and new friends as a person moves on to a new job and/or to a new location. For colleges and universities, fan socialization may be particularly important because a strong connection could endure through the remainder of an individual’s life, and in some cases result in the person encouraging his or her children to attend


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Fan Socialization the university in the future, perpetuating the fan socialization into a new generation. Another facet of media to consider is the role of sport video games or fantasy leagues. Although young adults make up a large percentage of those that play sport video games and who participate in fantasy leagues as was noted in Chapter 1, playing sport video games is not restricted to a particular age group. We have previously discussed the role of sport video games relative to children; we present some content within this life stage, and more in the following discussion of “older” adults. A high percentage of “gamers” includes those in the 18 to 34 year range (millennials). More than two thirds of millennials play video games and 22% say the term ‘gamer’ applies to them. In fact, Della Maggiora (2006) reported that men in that age range actually played video games more than they watched television. It would not surprise me that this statistic has skewed even more toward video games now. As discussed earlier in this section, playing a video game and imagining oneself to be part of a specific team may lead to consumption of the real team through watching and/or attending games, buying team apparel, following the team through other media, etc. In a related vein, people that participate in fantasy leagues may do so initially to interact with friends or coworkers, and as a result develop an interest in a particular sport. Media is important for fantasy league play because it is the source of statistical information and updates on athletes. These ideas again illustrate a type of interaction between agents; friends and peers, and media specifically. A socializing agent that may not exert as much influence at this stage is sport. A person may continue his or her participation in sports that developed in childhood or adolescence, and that may include a continuing interest in following a particular sport or team. One possibility is that a person may participate in a sport; for example, playing basketball in a community or work league as an extension of playing basketball for a school team earlier in life. If a person has not been a fan of a particular basketball team previously, she or he may be inclined to watch or attend basketball games compared to consuming other sports. It is also possible to think of those playing the sport video games and participating in fantasy leagues as playing sports. In that case, there is again a type of interaction between sport and media.


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Fan Socialization Older Adults (30 and above) Into the adult years, individuals are likely to have existing interests in sports or teams. As mentioned with young adults, if people move to new cities or communities, geography may exert an influence if there is a prominent sports team in the new location. A type of interaction between geography and friends and peers may be influential as a person strives to fit in or become part of a new community. In a reverse of the above example, Lock, Taylor, and Darcy (2011) found that a combination of geography and friends were secondary socializing agents when a new team was established in their community. The primary socializing agent that they found is discussed below. Belonging to an organization may be a component as well with respect to making new friends at work or if the organization has some type of connection to a sports team. For example, a company may sponsor a sports team and on some level the connection may influence employees to be fans of the team. Or as noted above, when people are hired by a sport organization, it is assumed that they will become a fan of the team if they aren’t already. Many of my sport allegiances are because of working at various universities around the United States. Some of my fandom has lasted for some of those teams, and in other cases, not so much. Media plays an important role in the adult years as a reinforcing agent. For those with a favorite sport and or team, media sources provide the outlets to follow a team through watching games and getting information about the team and players. Especially when one is no longer in the community and can only consume the team through the media. Being a big Buckeye fan and now living in Seattle relegates me to watching the games on TV or streaming them if I can’t. I certainly track the scores and news about the teams through the internet. Another role that media plays is introduction; when new sports and teams emerge, media will be an important source for learning about the new sports and teams. Interestingly in the Lock et al. (2011) research, media was never mentioned however. Sport as an influencing agent, generated by adults participating in a sporting activity, is likely to have less influence. Older adults may participate in sporting activities for exercise or fitness reasons; such activities, however, are not likely to facilitate strong connections to particular sports or teams. As noted previously, past participation in a sport may incline a person to watch a particular sport on television or perhaps attend a game, but that type of influence is not unique to this stage of the lifecycle. However, Lock et al. (2011) found that previous interest in that particular sport (soccer) was the primary socializing agent when a new soccer team was introduced into the community. They did not determine how the people were initially socialized into the sport of soccer, but they did determine that support for the new professional team in the community was primarily influenced by their socialization into the sport itself.


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Fan Socialization A facet of sport participation that may be influential is tied to children participating in sport and is also part of an interaction with family as a fan socialization agent. As older adults have children, it is likely that the adults’ participation in sport declines while the children’s participation increases. As discussed in the section on childhood, kids may learn about and participate in sports in which parents may not have a previous interest. In the U.S., soccer is a good example. There is not a strong interest in soccer among adults in terms of participating, with only 3.4 million adults between the ages of 24-64 playing in the United States. However, it is one of the strongest sports in terms of youth participation, trailing only basketball in organized team sports and considerably ahead of baseball and football with over 8.4 million kids between the ages of 7 and 17 playing (U.S. Census Bureau, 2009). Adults that did not have an interest in soccer earlier in life may come to like and follow the sport because their children play soccer. Caspar and Menefee (2010) found support for this premise, as people who had not played soccer as kids were much more likely than those who did play soccer as kids to indicate that their children playing soccer influenced them to go to professional soccer matches in the U.S. Thus, the children may influence the parents to follow teams at the professional level. The attraction to a sport later in life through this type of reverse socialization (Snyder & Purdy, 1982) may be more likely when a community has a sport team present (e.g., if a city is home to a Major League Soccer team). The preceding ideas illustrate another interaction effect with various agents, family, sport, and geography. The idea of reverse socialization across generations has also been noted in emerging work on the influence of culture on sport interests. Michelle Harrolle conducted interviews with three generations of Cubans living in Miami. Members of five families were interviewed and in one family she found that a grandmother became interested in the Florida Marlins because her grandson talked with her about the team and shared his interests with her. The grandmother subsequently became a fan and watches the team and then talks with her grandson about how the team is doing.


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Fan Socialization At the beginning of the section on fan socialization two important points were raised. First, there are different agents from the external environment that can influence whether a person forms a psychological connection with a sports team. Second, fan socialization can occur throughout the lifecycle. We have discussed the role of six primary agents in the process of becoming a fan. Through that discussion we have explained how various agents have a differential impact in various stages, and how the agents may interact to influence a person becoming a fan. It is important to recognize that as an individual grows from childhood, through adolescence, and into adulthood, conscious decision making and internal motivators play a significantly greater part. The same social influences still play a part when an adult is introduced to a new team or makes a decision as to whether or not to make a purchase, but many more factors come into play. A test of these suppositions with two teams. Megan Shreffler and I (mostly Dr. Shreffler) did some research to see whether or not Chicago Cubs fans had been socialized into being Cubs fans differently than Chicago White Sox fans had been socialized into being White Sox fans. What we found was rather interesting. Approximately 50% of Cubs’ fans were socialized during their childhood (0-9 years) and 46.5% of White Sox fans became fans during their childhood, so not much difference there. A little more than 34% of Cubs’ fans became fans during their adolescence (10-18), compared to 40% of White Sox fans. Only 13% of Cubs’ fans and 10% of White Sox fans were socialized into fandom as young adults (18-30) and very few as older adults (2.3% Cubs; 3.5% White Sox). So not a lot of differences as to when they became fans. The more interesting differences were when the different socializing agents came into play. For those who were socialized into fandom during their childhood, it was the same agents in the same order for both Cubs’ fans and White Sox fans; family was the most important agent, followed by geography, friends, media, and participation, in that order. Organization was dead last and not a factor. What was interesting though is that for White Sox fans, although geography was second most important, the difference between family and geography was negligible (M=6.01 for family to M=5.98 for geography). This shows that these two agents were pretty much equivalent for most White Sox fans during their childhood. However, Cubs’ fans rated family (M=6.11) significantly higher than geography (M=5.67). What this means is that for White Sox fans, where they grew up in Chicago (southside) had just as much of an impact as their family, whereas for


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Fan Socialization Cubs’ fans, although geography (northside) was important, it wasn’t nearly as important as family. None of the other agents differed that much between each set of fans (see Figures 4.7 and 4.8). Figure 4.7

Cubs Fans 7.00 6.00

MEAN SCORES

5.00 4.00 3.00 2.00 1.00 0.00

Childhood

Adolescence

Young Adults

Older adults

Family

6.11

5.91

5.91

6.00

Friends

5.23

5.83

5.23

1.75

Media

5.11

5.42

5.23

3.75

Geography

5.67

5.77

5.86

5.50

Participation

5.00

4.78

5.14

1.75

Organization

3.00

3.88

3.45

2.50

During adolescence though, the order in which the socializing agents impacted potential White Sox fans didn’t change from childhood. However, for Cubs’ fans they did. Friends became the second most Figure 4.8

White Sox 8.00 7.00

MEAN SCORES

6.00 5.00 4.00 3.00 2.00 1.00 0.00

Childhood

Adolescence

Yound Adults

Older adults

Family

6.01

6.05

5.83

5.00

Friends

5.35

5.61

6.17

3.40

Media

5.17

5.41

5.44

2.00

Geography

5.98

5.78

5.83

6.80

Participation

5.10

5.14

4.61

1.60

Organization

3.73

4.27

4.11

1.00

important socializing agent, jumping over geography, even though geography became more important than it was in childhood. For White Sox fans, geography became slightly less important for those socialized in adolescence.


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Fan Socialization For those Cubs’ fans socialized into fandom in young adulthood, friends weren’t as important anymore and geography continued to become more important, almost as much as family (Figure 4.7). However, for White Sox fans, friends became the primary socializing agent in young adulthood, overtaking family, which dropped down to be tied with geography (Figure 4.8). We can’t put a lot of credence in the numbers for the older adults because there were so few of them who were socialized into Cubs’ fandom or White Sox fandom when they were over 30, but geography and family still were the primary factors compared to the rest. So why is all of this important to Cubs and White Sox marketers? Or any marketer? First, one very important point is the ranking of the media impact for both teams. Across the board at all ages for both teams, media was typically ranked 4th as a socializing agent, considerably behind the top three in most cases. This shows that at least for these two franchises, socializing people into fandom is going to be much more viable through word-of-mouth (WOM) from family, friends, and the community in general (geography), than through the media. This is potentially a good thing, because media-buys are going to typically cost a lot more than WOM will. We will talk about this more in future chapters, but whether WOM is through social media (e.g. Facebook, Twitter, Snapchat, Instagram or whatever) or across the dinner table or out on the playground, it is more effective than media and is cheaper. Second, family is the most important socializing agent in almost all cases in this data set. Marketers need to assess their own fans to determine how and when they were socialized into fandom and use that data to game-plan strategies for continued socialization of more people into fandom, probably through their families. This means that it is critical to provide opportunities to interact with the team for all family members. Marketers need to convince those that control ticket prices, not to price families out of the venue. As we saw in the earlier chapters, lower-income families probably can’t afford $500 to take the family out to the game. If you price families out of the market you lose the possibility of those kids being fans down the road. Not a good idea! Third, it is critical that sport marketers understand how people become socialized into being fans of teams and fans of sport in general. Typically, around 90-95% of the attendees at sporting events are fans of the home team. This varies with rivalry games where the number of fans from the visiting school increases. It also changes for playoff games, bowl games, and neutral site games. Obviously in these cases, the percentage of fans from one team or the other will vary on the situation. In these cases, the number of sport fans who have no allegiance to either team will also increase. For example, the Super Bowl typically has a much lower percentage of team-fans and a much higher percentage of sport fans because of the added-value of the game. We will talk about added-value in one of the later chapters. Regardless, it is critical for sport marketers to know approximately what percentage of people in the stands are actually fans of the team versus fans of the sport, the opposing team, or not fans at all. Understanding how all of the attendees were socialized into being fans of the sport will help marketers understand how the external environment impacts attendance.


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Fan Socialization In addition, if sport marketers understand how people are socialized into becoming fans they can facilitate some of that socialization. For example, in some of the research that we did for a women’s basketball team, we found that there were people who were interested in coming to the games and they were fans of the team but did not attend. We determined that the reason that they did not attend is that they did not have anyone with whom they could to come to the game. We suggested two things to the athletic department. First, provide those people with a two-for-one ticket, so that they could bring someone else for free. Second, we suggested that the athletic department determine the geographical location of these local fans and suggest that they pair up to come to the game together. In both of these instances, knowledge that people can be socialized into being fans and attending games through association with friends allowed us to make this suggestion to the athletic department. The athletic department instigated these policies and it seemed to work. Many sport marketers and managers understand how important it is to get families to come to the games together so that the children become socialized into fans. Many sport organizations now provide family zones and family activities, in addition to the game, so that children who may not originally have any interest in going to the games associate fun and good-times at the stadium or ball-park and eventually make the connection to the team itself instead of all the ancillary products and services. For example, Iowa State University started the Little Cyclone Club for kids under the age of 13. Each kid and one parent were allowed to sit in the Little Cyclone Club zone right behind the basket at all women’s basketball games. The kids were also given a team media guide and special times that they could meet with the players and get autographs. This club was dramatically successful and at one point had over a 1000 members. The objective with the kid clubs and socializing the kids to become fans at a young age is that they will typically maintain these connections for the rest of their lives. Socialization Assessment Table The socialization assessment table example continues with the fictitious Renton RoadRunners that I started last chapter. Remember that I am using some of the data from existing teams similar to the RoadRunners, but I had to modify it to some extent to make the example work. Again, this table is just one way of depicting the information, but it can be done in other ways as well. Life Stage Childhood

Socialization Agent Only 10% of fans were socialized in childhood because the team hasn’t been around very long (12 years). Thus, those that were socialize at this stage are under the age of 22 currently. In this stage, 63% were socialized by family, 26% by friends, and 11% by the organization itself going into the schools and giving out tickets, talking to kids, meeting the mascot, etc. But this could also be considered sport participation because most of the kids were on a school


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Fan Socialization

Adolescence

Young Adults

Older Adults

basketball team or AAU team and that’s how the met the team officials. Geography, obviously, played a large part, as almost all of these fans are local, but it is a secondary socialization agent, through family, friends, etc. Slightly more than 23% of fans were socialized in adolescence. Most of these are under 30 years of age now. Of this group, most were socialized by family (48%), closely followed by friends (40%), with the rest socialized by media and sport participation. Again, geography played a substantial part, as many in this group were local when they were socialized, but some have now moved out of town. Some still come back for a few games. 38% of the fans were socialized as young adults, and many of the most recent converts were socialized this way, primarily through the media (44%) and/or friends (37%), but sport participation also played a part (13%), in association with geography. Fewer of these are local, but still a majority. Media and marketing drew this group to the games due to the social aspects and Two-for-Tuesdays (two tickets, two beers, two hot dogs, etc., for the price of one). 29% of the fans were socialized as older adults: A majority (62%) through the media and marketing by the team, secondarily through their playing basketball and interest in the sport. Friends had a smaller impact (19%). Family had a small impact as well (15%), about half of whom were ‘reversed socialized’ by their kids who got tickets at school or at basketball camps. A very small percentage only listed geography (4%).

Summary So obviously it is important for marketers to understand the environment and how factors in the environment socialize people into becoming fans. Two of the agents that we suggested might socialize fans were the media and the organization itself. Working for, or being associated with, the organization is not a socializing mechanism for many people. However, the organization does impact socialization through the way that it interacts with the fans and potential fans, whether directly, or indirectly through the media. So, let’s look at how the internal organizational environment of the sport entity can impact fans, both directly, and indirectly. On to the next chapter!


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Internal Organizational Environment


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Internal Organizational Environment

Chapter 5 Internal Organizational Environment ______________________________________________________________________________ Introduction In Chapter 2, we discussed the Environmental Insight Framework and Sport Consumer Pathway (Figure 5.1). In Chapters 3 and 4 we discussed specifically how the External Environment generated insights for sport marketers, helping them also understand the fandom socialization process. So now, we

Figure 5.1

need to discuss the next part of the framework that influences both the customer environment and the sport consumer pathway (and to some extent the socialization process), and that is the Internal Organizational Environment (IOE). If marketers understand how the IOE impacts the customer environment and the consumer pathway, then marketers can create marketing plans and communication plans much more effectively and efficiently. So, the objective of this chapter is to see how the internal organizational environment and its components help marketers develop marketing plans that will effectively communicate with the customers (fans) and build better relationships to help the consumer progress along the pathway. The internal organizational environment is comprised of many components, most of which you are probably familiar with if you have knowledge of organizational systems. Here however, we are going to focus on just a specific few, and those are Vision, Mission, Organizational Structure, Products, Goals,


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Internal Organizational Environment and Content. The first four components usually direct and focus the business, marketing, and communication goals. The different types of goals, in addition to the other components of the IOE, influence how the customer progresses along the consumer pathway (see Figure 5.2) and determine (or should determine anyway) the Content. The Content is the actual marketing and communications materials that come from the organization and create Awareness, Interest, and Consideration, etc., which is a part of Relationship Building. I have put Relationship Building, which is a communications goal, as the Figure 5.2

organizational component of the consumer pathway. Let’s examine each of the components of the internal organizational environment in turn. Organizational Vision Statement To create an effective marketing plan, marketers need to do a business review. This review starts with the organization’s vision. If the organization has a current vision statement it makes things a lot easier. With any marketing plan, marketers need to make sure that the plan fits the vision of the organization and need to determine whether the vision statement provides at least some guidance specific to the goals, products, and content. Unfortunately, many organizations do not have a vision statement and/or do not have a mission statement. Some don’t know the difference. So, let’s take a bit of time to review what each is, how they differ, and whether both are needed.


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Internal Organizational Environment A good vision statement focuses on the future and is a source of inspiration and motivation for the organization, its employees, and potentially external stakeholders as well. It should not only focus on the future of the organization, but how the organization envisions an ideal future industry or society and how the organization can effect change. A mission statement, on the other hand, focuses more on the present (Foundation Center, 2015). The vision statement focuses on the organization’s reason for existence, while the mission provides an overview of how to realize the vision. More specifically though, vision statements need to provide guidance relative to the content that the organization provides the consumers, in addition to providing direction to the various types of goals. Let’s look at a couple of examples. FC Barcelona has a very brief and rather broad vision statement set out in their strategic plan: Become the most admired, loved and global sports institution in the world. Admired because of its sporting results and the way it achieves them, loved because of its bond with its members and supporters’ clubs and its social commitment and impact, and global because of its commercial and brand development. Barca’s vision focuses on the future and how it envisions that it can influence both industry and society. However, I’m not sure that it is very motivating, but maybe it is to FC supporters. Let’s compare it to Real Madrid’s vision statement: A club which is a world leader in football and basketball that, through its sporting achievements, fulfils the expectations of all of its supporters both nationally and internationally, that preserves its important historical legacy, that manages its heritage with rigour and transparency to benefit its members, and that acts with social responsibility and good corporate management. It seems to me that it is very similar, but Barca does acknowledge the business aspect a little more as it notes “commercial and brand development.” Both acknowledge a social responsibility to the community, which is good, but I don’t think either necessarily provides guidance to the content that they provide consumers except broadly in Barca’s text that says that they will be admired in the way that they achieve “sporting results.” In both cases though, the vision should influence the mission and then the product(s)/services, which, in turn, should direct the goals.


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Internal Organizational Environment Organizational Mission Statements The same issues apply to organizational mission statements as discussed in the vision statement section. Marketers need to make sure that the marketing plan fits within the mission statement of the organization. Trail (2016) suggests that a quality mission statement: • Specifies key elements of the company’s philosophy. • Specifies the foundation for strategic planning. • Identifies principal products/services. • Identifies goals. • Specifies target customers and markets. • Promotes the company’s desired brand. Trail’s (2016) list is probably too extensive for including all of those components in a mission statement. The mission would be much too long. If the organization has determined its goals and organizational values, then several of these aspects in Trail’s (2016) list are not needed within the mission statement itself. If no goals, values, strategic plans, etc. are identified by the organization, then the mission probably needs to include all of those aspects. Regardless, if sport organizations have these aspects in one place or another, marketers can more easily create effective plans. Let’s examine both FC Barcelona’s and Real Madrid’s mission statements, similar to what we did with their vision statements. Unfortunately, Barca doesn’t have much of one, or if it does I couldn’t find it. What they do have is a “Where Are We?” statement that might reflect the mission: Our starting point is one of privilege and leadership. FC Barcelona today is one of the most important sporting institutions in the world, which is why it is considered and known to be ‘more than a club’ and its identity symbols are recognised all around the world. This statement somewhat reflects the company’s philosophy of “privilege and leadership”. It also provides a foundation for the strategic plan by noting that its brand is recognized around the world, that it is known for being more than a club, and it is one of the most important sporting institutions in the world. However, the mission really doesn’t address the other aspects noted by Trail (2016) and it doesn’t need to because in Barca’s strategic plan, which directly follows the mission and vision in their document, they include the goals, identify products, specify customers, and promote the brand. We will discuss those in a bit. Similarly, Real Madrid’s mission is very straightforward: An open and multicultural club, appreciated and respected around the world for its sporting achievements and its values that are found in the search for excellence on and off the pitch, and contribute to fulfilling the expectations of its members and supporters. This mission statement identifies key elements in the company’s philosophy (open, multicultural, respected). Determining the foundational aspects for strategic planning is a little more difficult but excellence and fulfillment of expectations would work. It does not identify the principal product unless one counts “sporting achievements,” but does identify some goals such as being appreciated for the sporting achievements, excellence on and off the pitch, and fulfilling expectations. All of these are rather broad. It doesn’t identify customers or markets other than members and supporters, nor does it really promote the desired brand except relative to respect. However, Real’s values, which directly follow the vision statement on their website, do address some of the above aspects that weren’t addressed in the mission statement, such as goals, a lot more philosophy, and foundation for planning. To provide a counterpoint to the above two mission statements, I’ve included the mission for Manchester United. It is much more business oriented because it is stated within their business strategy: We aim to increase our revenue and profitability by expanding our high growth businesses that leverage out brand, global community and marketing infrastructure. This mission, or business strategy, is more clear on most of the aspects mentioned by Trail (2016), but there is no way that you would know that it was a football club. This is probably due to it being a publicly


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Internal Organizational Environment held company (i.e., you can buy stock in it). This makes it considerably easier to identify business goals as we will talk about in a bit. However, first we need to discuss how organizational structure can impact marketing and communications plans. Organizational Structure One definition of organizational structure is the hierarchical arrangement of lines of authority, communications, rights and duties of an organization. Organizational structure determines how the roles, power and responsibilities are assigned, controlled, and coordinated, and how information flows between the different levels of management. A structure depends on the organization's objectives and strategy. As you can see from this definition, organizational structure would affect a marketing plan in a couple of ways. First, organizational structure specifies who has what duties, thus showing who would be in charge of creating the marketing plan. Second, it also shows who has the power and the responsibility to approve

a plan or not. For example, a marketing director may report to a VP of Marketing. The marketing director will oversee the creation of the plan, but before the plan would be finalized, the director would typically need to get approval from the Marketing VP. It might go higher than that, depending on the expansiveness of plan and the cost. The marketing director, in association with the Marketing VP, will assess whether the proposed plan is a good fit with the vision, mission, and organizational goals. Obviously, individuals in the marketing and communications areas should have already made sure that the proposed plan would be a good fit before the proposal made it to the higher levels of the organization. Now however, let’s discuss how products/services fit into the Internal Organizational Environment Model.


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Internal Organizational Environment Products/Services The organizational vision and mission determine (or at least should determine) the choice and design of the product or service that is going to be sold to the consumer. Obviously, marketers need to understand their product extremely well so that they can market it. From the spectator sport perspective, the primary product marketers are selling is the game itself. There are many ancillary aspects of the product, including product extensions and some services as well. In addition, there are product benefits and potential constraints associated with the organization itself. All of these aspects come under the broad title of brand associations, and we will discuss all of them in detail in the next chapter. For now, we will briefly cover some categories about which marketers need to have information, so that they have insights as to why the consumer may want to purchase the product. Product Attributes and Benefits. In general, product attributes refer to characteristics that make the product or service distinct from other products or services. Attributes can include functionality, components, and features that affect the product’s appeal or acceptance in the market. There can also be non-product related attributes that are ancillary to the product but may impact the perception of the product (e.g. packaging, advertising, etc.). Product Benefits are what consumers think the product or service can do for them according to Keller (1993). Obviously, the sport product is quite a bit different from most other products, and thus so are its product attributes and benefits. Many of these product attributes and benefits have been described as motives or market demand characteristics in the sport literature and earlier editions of the book (Trail & James, 2013; 2015), but in this edition of the book, I am making a distinction between the product attributes/benefits and internal motives now. We will discuss all of the potential product attributes and benefits in much greater detail in the next chapter, but for now a partial list will suffice. Product related attributes can include: Team Success, Star Player, Quality Management/Front Office, Quality Head Coach, Aesthetic Play, Aggressive Play, Dramatic/Style of Play, Athlete Attractiveness, Athlete Skill, Team Personality, and Competition (Rivalry). In addition, there may be ancillary product attributes (non-product related) that can be relevant as well: Brand Mark/Logo, Venue Aesthetics, Venue Location, Cleanliness, Quality of Concessions, Concession Service, Parking, Employee Service, Seating, Price (Game, Concessions, Merchandise), Relationship Building, Promotions, Advertising, etc. There are also product benefits: Social Interaction Opportunities, Nostalgia (Team History/ Tradition), Diversion, Information Provision, Athlete Attractiveness, Popularity (Commitment), Pride in Place, Supporting a Cause, and Players as Role Models. Finally, there are constraints that either come from the organization or need to be ameliorated by the organization through better communications: Lack of Communication, Price, Scarcity, Poor Player/Employee Behavior, Lack of Access, Losing Team, Poor Ticketing Processes, Games being on TV/Streamed, and Games being on Radio. Most of these attributes, benefits and constraints will be relevant to marketing plans in one way or another, thus marketers need to be well informed about how consumers perceive these product attributes/benefits and constraints. Price points. Another critical component of the product or service that may impact a marketing plan are the price points at which the product is being sold. Typically, there are many price points for most sport events. There are often multiple categories of ticket type as well (season tickets, multi-packs, singlegame tickets, etc.). Each of these may have as many as 10 different price points depending on the team or organization. Marketers need to know what these price points are, how they may impact the communication campaign, and whether they are constraints or not. Sales trends. Marketers also need to know the sales trends for each type of ticket at each of the price points. Season tickets at the upper levels of the stadium may be down over the last several years, but single game tickets in the lower levels may be up. Marketers need to understand what the trends means and how they may affect the campaign. We will address all of these aspects in greater detail in the next chapter, but now we turn our attention to goals, and how the vision, mission, organizational structure, and the products/services may impact business, marketing, and communication goals.


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Internal Organizational Environment Business Goals Organizations must have “clearly defined business goals” (Scott, 2015, Figure 5.3 p. 51) and sport marketers need to make sure that they know their organization’s business goals. Nickels, McHugh and McHugh (2002) give a definition of business goals, saying that they are the “broad, long-term accomplishments an organization wishes to attain” (p. 208). Furthermore, Young (2014) suggests that marketers need to focus on outcomes and not outputs. What this means is that marketers need to be able to show how their marketing and communications plans (specifically the goals within those plans) will be able to produce viable outcomes specific to the business goals. Scott notes that “Many marketers and PR people also focus on the wrong measures of success…. What matters is leading your site’s visitors and your constituent audiences to where they help you reach your real goals, such as building revenue, soliciting donations, and gaining new members” (p. 163). Business goals (Figure 5.3) include economic goals such as increasing revenue, Triple Bottom Line. improving market share, Triple bottom line refers to the increasing profits, etc. “For corporate social, environmental, most corporations, the most and economic responsibility fully important goal is profitable integrated into all business revenue growth” (Scott, 2015, operations. Social responsibility p. 163). However, under the refers to the organization Triple Bottom Line paradigm, treating employees well, business goals also include supporting the community, and environmental goals such as being a responsible corporate minimizing environmental citizen. Environmental impact, improving the responsibility refers to reducing ecological environment, etc. the organization’s environmental Social goals are also part of the impact. The organization also has Triple Bottom Line and could a fiscal responsibility to the include enhancing the quality of stakeholders. Thus, the life of all constituents, organization must achieve the improving employee welfare, social and environmental goals etc. The organization may be while still being a financially attempting to achieve all of viable entity. these goals at the same time, or it may choose to focus on one or a few goals only. Typically, the number of goals may be limited by budget, time, and effort. With limited resources, not all goals can be achieved. Too frequently, marketers are sidetracked by outputs such as “reach (coverage), frequency, impressions, gross rating points, and cost per response” (Young, 2014, p. 47); however, these are not outcomes and these metrics do not show how the organization is meeting business goals. In fact, these metrics do not even help track marketing goals, but may be useful (to some extent anyway) in evaluating communication goals, as Scott (2015) noted, “Website traffic doesn’t matter if your goal is revenue (however, the traffic may lead to the goal)” (p. 51).


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Internal Organizational Environment One reason it is important to know how a marketing plan fits in with the business goals of the organization is that the effectiveness of the plan will be assessed at the end of the campaign to determine the ROI (Return on Investment). Many times, assessment measures are related to the goals. I will talk about assessing communication campaigns in a different book as we don’t have space here. So, let’s evaluate the official goals of FC Barcelona, Real Madrid, and Manchester United, to see how easy (or not) it would be to create a marketing plan that would help fulfill these goals. Barca has developed five lines of strategy within their strategic plan for 2015-2021: Sporting Excellence, Social Implication, Patrimony, Brand and Global Positioning, and Financial Management and Sustainability. These are goals, and within these goals, Barca has strategic targets, or sub-goals. For example, within the sporting excellence goal, their sub-goal for each professional section (of the club) is to win 1 of every 3 titles per season. A very ambitious goal! Within the social implication goal, one of their sub-goals is to structure and strengthen relations with non-sporting institutions. In the patrimony goal, two of their subgoals are to increase the value of the club’s assets and comply with the budget of 600M euros. See all of the sub-goals within the strategic lines in Figure 5.4 below. However, although their lines of strategy are primarily business goals, not all of their sub-goals are business goals, not surprisingly. Figure 5.4

Real Madrid, on the other hand, doesn’t specify goals per se; they list Values instead: Winning Sprit, Sportsmanship, Excellence and Quality, Team Philosophy, Training, Social Responsibility, and Economic Responsibility. However, their values are more like goals as you read the descriptions. For example, winning spirit is defined as aiming for the highest achievements in all competitions. That is


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Internal Organizational Environment definitely a goal, regardless of how they want to name it. They define economic responsibility as “managing material and immaterial assets of exceptional value and importance…”. The label is a goal and the description is a goal as well. However, in most of these values/goals, they do include the process through which they want to achieve each of these goals, and in those processes, the values/philosophy of the organization does come through. So, although they have labeled these aspects Values, they are more like value-driven goals for the most part. I have included the full list in Figure 5.5. Fewer of Real’s goals are business goals though. In the economic goal category (see Figure 5.3 above), we can put Real’s Economic Responsibility and perhaps Excellence and Quality. In the social goal category, we can put Winning Spirit (or at least the loyalty to supporters’ part), Sportsmanship, Team Philosophy, and Social Responsibility. Parts of Training definitely go into the social goal category as well. They do not address the environmental aspect of the Triple Bottom Line though. Values/Goals Winning Spirit Sportsmanship

Excellency and Quality

Team Philosophy

Training

Social Responsibility

Economic Responsibility

Description Figure 5.5 Real Madrid aims for the highest achievements in all competitions in which it competes, without ever giving up, and giving proof of its constant work and its loyalty to supporters Real Madrid are a sincere and honest rival on the pitch, who operate with good faith and respect for all the teams they compete against and their fans. Off the pitch, we want to maintain fraternal relations and solidarity with all other clubs, offering them and the national and international sporting authorities our continued support. Real Madrid aspires to having the best Spanish and foreign players in its ranks, instilling in them its dedication to the club’s own values, corresponding with the support of the fans with a sporting mission based on criteria such as quality, discipline and the capacity to make sacrifices. In managing its activities, it looks above all, to good leadership and the constant search for excellency. Many people make up Real Madrid; from the sportspersons to the professionals, we aim to work as a team, bringing the best of our individual capabilities to benefit the club as a collective, without personal or professional egotism. Real Madrid devotes great continued effort in the discovery and education of new sporting values, dedicating attention and necessary resources to all of its youth teams and caring not only for sports training of its young people, but also social and ethical training and citizenship. Real Madrid are aware of the high social repercussions of its activities and for that reason dedicates resources to comply with the highest standards of corporate management, and the promotion of the best sporting values, strengthening its relationship with its members, veterans, fan groups and followers, and the development of charitable projects for collective needs both in Spain and abroad. Real Madrid is conscious that managing material and immaterial assets of exceptional value and importance, to which it is dedicated to administering in a responsible, efficient and honest way to benefit its members.

Manchester United’s goals are much more business oriented because they come from their investor relations webpage and not the team webpage. They identify four broad goals: expand our portfolio of sponsors, further develop our retail, merchandising, apparel and product licensing business, exploit new media and content opportunities, and enhance the reach and distribution of our broadcast


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Internal Organizational Environment rights. Each of these includes a description which you can find on their webpage, but you can definitely see the differences between these goals and either Barca’s or Real’s. These differences as I pointed about above are primarily due to Manchester United being operated as a public, for-profit business, whereas the other two, are operated more like the clubs of old, although they still want (and expect) to make a profit, they do not have stockholders, but members instead. Unfortunately, all of the business goals listed on this part of Manchester United’s webpage are solely economic goals, but not all of the goals listed are business goals, some are marketing goals, and we will discuss those in a bit. So how would this information help a marketer design a marketing campaign and understand consumer behavior of sport fans? These goals, for each of these sport organizations, show what is important to each organization and what they ultimately want to accomplish. Thus, marketers need to understand the goals and how they fit within the mission and vision, so that they can develop marketing plans to help accomplish the goals. Marketers need to keep these goals in mind when interacting with the customers/fans because these goals show where the organization wants their customers to be on the pathway to some extent, and they also depict how the marketers should go about building relationships with fans and potential fans. In addition to business goals, we need to discuss how to identify the marketing and communications goals that will allow the marketer to show ROI for each audience segment. Specifically, in this chapter, we will discuss how the Consumer Pathway can be used in marketing to guide goal selection. In addition, we will also discuss how these goals tie back into the business goals of the sport organization and how marketing and communications goals may differ by market segment. Marketing Goals Scott (2015) noted that many marketers “cannot articulate who their buyers are and what problems they solve for them” (p. 51). That is a critical problem and shows why marketers need to understand their consumers and where they are on the Consumer Pathway. Young (2014) specifically states that marketing goals should be predicated on where consumers are on the Consumer Pathway and market segments may differ considerably. We haven’t Market Segmentation talked about market segments or segmentation yet, as that is a is the process of whole different chapter, but for now see the sidebar. Young (2014) subdividing a market into distinct subsets of noted that there seem to be two reasons that marketing campaigns customers that behave fail. The first reason is that people in the organization have neither in the same way or clear understanding of the marketing goals nor what the key have similar needs. performance indicators (KPIs) should be specific to the marketing Each subset may goals. The second reason that marketing campaigns may fail is that conceivably be chosen marketers have not established a viable process with which to as a market target to evaluate the progress and success of a marketing or be reached with a communications campaign. Marketers need to establish the distinct marketing marketing goals, determine how the marketing goals tie in to the strategy. business goals, designate appropriate KPIs, and develop an evaluation process for assessing the progress of the marketing campaign.


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Internal Organizational Environment There are a variety of marketing goals that marketers may be interested in Figure 5.6 achieving (Figure 5.6). In addition, some marketing goals may be reflective of more than one business goal. The marketing goals that we will discuss are consumer participation, trial, obtaining new customers, reacquisition of lapsed customers, increasing purchases, consumption (usage), repatronage/retention, and improving brand image. There are probably others and not all organizations will use all of these, as some would not be applicable across all organizations. Trial is a marketing goal that is typically reflective of economic goals of the organization. The objective of trial is to get the consumer to test out the product without the consumer having to commit any financial outlay. Most of you have probably had some experience with this in a grocery store or even perhaps outside a sports venue where marketers are giving away free samples of their product. Obviously, the objective here is to show the potential consumer that the product is a good one and that the consumer should purchase it in the future. Free ticket-offers that are redeemed by people that have never attended before (or at least are not in the database) would be one example of trial. Young (2014) notes that it is possible to use media and especially social media to drive trial. For example, watching a game at home on TV or streaming it on a smart phone is a type of trial, with minimal additional financial outlay depending on the cable package or cell phone contract that one already has. The KPI for trial is typically the number of people that try the product. Ratios or percentages can be calculated if the organization knows the total numbers of the segments. Obtaining new customers is a marketing goal that is fairly self-evident and prevalent in most marketing campaigns. Again, this ties in to the Economic goal of the organization. New customers equate to new revenue streams. The objective here is to be able to convert intentions into actual purchases at the point of purchase. One way of doing this is to focus targeted advertising on the touch points (Young, 2014). What this means is that when a potential customer is at the specific point of intending to make a purchase, the marketer needs to make sure that there is a communication at that moment in that particular situation that encourages the consumer to make the purchase. For example, if someone is watching the game on TV and the team wins, then marketers should be able to have an ad or banner that runs across the TV screen encouraging the person to buy tickets to the game instead of


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Internal Organizational Environment watching it through media. Yes, it is not always possible to know when the team will win the game, but it is always possible to have an ad or banner ready to go with an agreement with the media provider to run it near the end of games that the team looks like it will win. The easiest KPI for this goal is just to track the number of new customers per market segment. Obviously, this entails determining which segment each customer falls into though and which ones are new ones. Reacquiring lapsed customers in an important marketing goal. Lapsed customers are those individuals who have purchased your product previously, but for some reason, are no longer purchasing the product. Young (2014) suggests using reminder-based tactics to encourage the consumer to return, but that typically is not sufficient. Usually it is necessary to determine why the consumer is no longer purchasing the product and fix any issue that caused the customer to go elsewhere. This marketing goal also ties back into the Economic goal of the organization, specific to increasing revenues and decreasing costs, as it is cheaper to retain a customer (or recover a lapsed one) than to generate new ones. Customers, or in our case fans, might have held season tickets for many years and all of a sudden not renewed. The sport organization needs to contact them and determine why they stopped owning season tickets. If there is a specific reason they stopped, and it can be rectified, then the organization needs to do so. The KPI for this goal can be a total count or a ratio of reacquired lapsed customers to those lost per segment. However, the latter KPI presupposes that the organization tracked the customers that they lost, which most don’t. Consumer participation is a very broad marketing goal and most organizations will need to narrow its definition or modify it to be applicable to their own situation. For sport organizations, participation typically refers to fans and spectators consuming sport in one way or another. This marketing goal is to generate a designated percentage of each market segment to participate as much as possible. It is not reasonable to expect that everyone in each segment will participate, thus a 100% participation rate is not viable. For most sport organizations, the more that fans are vested in the organization and the more that particular target segment has similar values, the more likely a high rate of participation would be a feasible goal. Whereas for a segment of spectators (not highly loyal) who do not have similar values or needs, it is unlikely that a high participation rate is possible. For example, marketers might want spectators and fans to participate in a specific promotional campaign to increase the number of people registered in their database. Indirectly this could tie back into an economic goal if you can get previously unregistered individuals to buy tickets. However, the promotional campaign could instead be focused on getting people to recycle more or participate in some other way. Then it would be tied beck to an environmental goal rather than an economic one. This is why we need to understand our market segments and their consumer attitudes and behaviors, before trying to establish marketing goals. Establishing the same goal across segments will not work typically. In addition, marketers need to establish specific KPIs for each target market. One that seems easy on the surface is participation rate. However, to calculate the rate, the total market segment needs to be known and the number of people in that segment who are participating needs to be known. Sometimes this is very hard to calculate. Sometimes it is easier to just count number of fans within each segment who are participating in the campaign and track the numbers over time.


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Internal Organizational Environment Increasing the purchasing of existing consumers is another potential marketing goal that is tied to economic goals of the organization, specifically increasing profits. It is often easier and cheaper to get existing fans to ‘ramp up’ their spending, especially if they are loyal fans, than to get new fans to spend anything. In addition, loyal customers are typically less price-sensitive and thus are willing to spend more. This spending, obviously, increases overall profits. This goal has one of the easier KPIs and that is to track the additional money spent above a designated baseline for each consumer. These can be changed into ratios or percentage increases as needed. Consumption (or usage) of the product is a marketing goal that historically has not been a part of marketing or communications plans in the past because the assumption is that if the ticket is purchased, for example, then the purchaser will use it. This is not always the case as you well know. The purchaser may have bought it for someone else, may decide not to attend, or may decide to sell it to someone else. We can’t assume that whoever purchases the ticket initially, is the end user. This may be the most critical part of the whole process and as Young (2014) points out, consumption needs to reinforce the brand through customer-service interactions. As you are well aware, if the product does not perform as expected and you are not satisfied with the product or the purchase experience, you are highly unlikely to repurchase the product and you are unlikely to use the product again. This has many ramifications. First, you become one of those lost (lapsed) customers we discussed above, which decreases revenue. Second, it is unlikely that you buy another product from the same brand, which decreases profits. Finally, you will probably tell many of your friends, and might complain on social media, about your poor experience, thus potentially causing others to refuse to buy the product and potentially hurting the brand image. Thus, marketers, and the sport organization in general, need to make sure that the consumer’s usage experience is a good, and when it is not, the organization needs to fix it and make it right for the customer. The KPIs for this goal are many. First, positive post-purchase interactions could be tracked. Second, the percentage of complaints by dissatisfied customers that the customer service department solved would be another good metric. Another KPI could be satisfaction ratings. Retention and repatronage is the next marketing goal. It is considerably cheaper to retain satisfied customers than it is to get new ones as we noted above. Satisfied customers become loyal customers who buy the product again (repatronage). Loyal customers buy more products within the same brand and are willing to pay a premium for the brand and product over alternatives, thus leading to greater profits meeting economic goals of the organization. This applies to both potential sustainability campaigns and social justice campaigns as well. By keeping fans participating in the campaign, it is more likely that they will participate in future campaigns and participate more fully in existing campaigns. Retention rate is a typical KPI for this goal. Money spent after initial purchase is another potential KPI.


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Internal Organizational Environment Improving brand image is the final marketing goal. Brand image is the “perception of a brand in the minds of persons. The brand image is a mirror reflection (though perhaps inaccurate) of the brand personality or product being. It is what people believe about a brand, their thoughts, feelings, expectations” (AMA, 2015). Everything we have discussed so far ties into brand image in one way or another. If people have a poor perception of the brand they are unlikely to purchase any of the brand’s products, more likely to denigrate the brand to others, and more likely to purchase a competitor’s product. Brand image ties back into economic goals, environmental goals, and social goals. The better the brand image is in a consumer’s mind, the more likely they are to spend money on the brand. Marketers can improve brand image relating to environmental goals as well by promoting campaigns about how the team and the fans are partnering to minimize the environmental impact of attending a game. Similarly, the team can improve its brand image relating to social goals by communicating how the team is enhancing the quality of life for its employees by increasing their wages and giving full benefits. Not only does this help those employees, it also helps the community at large. The KPI for brand image is usually a measure taken by a survey of the populace regarding favorable brand perceptions. As I noted above, these are not all of the potential marketing goals, but it is a good representation. There are other goals that some people have included under marketing goals, but I have included under communication goals instead. So, let’s talk about those. Communication Goals According to Young (2014), “effective communication goals need to have a direct line of sight between the communications and business outcomes” (p. 49). I disagree to some extent. I think that the communication goals need to be directly contingent upon the marketing goals, which in turn are contingent upon the business goals. I have depicted this in Figure 5.7 below. However, you will note that although there are arrows that show Business Goals determining Marketing Goals, which in turn determine Communication Goals, there are no arrows from specific business goals to specific marketing goals or to specific communication goals. The reason that this is the case is that, as I have said before, some marketing goals can be relevant to multiple business goals and most communication goals can be relevant to many of the marketing goals. If I included arrows for every possibility, the figure would be a mess and unintelligible. Although Young (2014) suggests that the communication goals need “to inspire ideas and creative ways” (p. 49) to fulfill the marketing and business goals, that’s not quite the case. It is not the


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Internal Organizational Environment communication goals that need to do so, but the processes through which the Figure 5.7 communication goals are completed that need to be inspirational and creative. The goals themselves (awareness, involvement, active consideration, relationship building, customer satisfaction, attitudinal loyalty, and advocacy) are rather pedantic in and of themselves. However, the way in which awareness, for example, might be generated, needs to be unique and exciting. Two other important aspects are needed referent to communication goals. First, marketers need to “identify relevant, influential, and penetrating platforms and activation in the media” (Young, 2014, p. 49) and second, marketers should be able to create “strategic, value-added activities that can be quantified” (Young, p. 46), specific to the goals. Young suggested that there are four steps to accomplishing these endeavors and developing communication goals: 1. Break down the business goal into specifics. 2. Identify how and where media can help 3. Quantify what success will look like. 4. Prioritize and socialize the goals (p. 50) To some extent I have already discussed the first step, although I did not go into specifics such as designating specific monetary goals for sales, revenues, market share, or profits within the economic goals, or in the environmental or social goals either. That would not be productive in this book because it would not be relevant to each marketer’s specific situation. However, marketers do need to understand that when creating a marketing plan they will need to be more specific at each level of goal. I will give an example later in the chapter. I will discuss steps 2-4 in greater detail in another book (Trail, forthcoming), but for the moment what you need to know is what the general communication goal categories are, and how they are relevant to marketing and business goals. Although Young (2014) uses his consumer pathway to frame communications goals, because I have separated communication goals from business and marketing goals, and because I have added several communication goals, and modified the consumer pathway, it is no longer feasible to use the stages in the consumer pathway as communication goals. However, some of the steps and components in the Sport Consumer Pathway are still relevant as Communication Goals: Awareness, Interest, and Active Consideration. I have incorporated Relationship Building from Kim and Trail (2011), Customer Satisfaction


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Internal Organizational Environment from Oliver (1997), Attitudinal Loyalty from Solis (2015), and Advocacy from Ottman (2011). In addition, I have included within those seven communication goals, ideas from Belz and Peattie’s (2012) list of eight objectives for communications: awareness, informing consumers, reminding consumers, persuading consumers, reassuring consumers, motivating consumers, rewarding consumers, and connecting with consumers. So, let’s discuss each of the Communication Goals and where they fit with the Marketing Goals. Awareness. Obviously, consumers need to be aware about the product. According to Young (2014), to generate awareness the marketing strategy should focus on effective frequency levels. Effective frequency is defined as “the number of times a person is exposed to an advertising message before responding” (Young, 2014, p. 52). He suggests that marketers should use awareness tracking models and data to set and evaluate optimal frequency levels, with the objective of maximizing the marketing plan’s reach and coverage of the specified market segment as efficiently as possible. Ottman (2011) recommends providing interesting and educational information on one’s own website as an inexpensive way to accomplish this; however, that is rarely sufficient because if no awareness exists, how will people know to go to the website. Most sport marketers though, are working for a sport organization that already has the primary product (the team’s games) and an established brand (usually). Sport marketers only need to tie the new campaign into the existing brand and communications objectives. Still generating awareness needs to be done in a variety of ways and will vary by target market. Interest. Interest can entail many things, but in general, interest incorporates both positive emotion about, and improved perception of, the product or brand. Young (2014) notes that marketers need to be able to increase the brand’s appeal and that the best way of doing that is to communicate the strengths of the brand or product. This may include helping the consumers understand the benefits of the product, the reliability of the product, the value of the product, and the competence of the service (Hiebing, Jr., Cooper, & Wehrenberg, 2012; Young, 2014). If the organization can accomplish these things then typically that improves the perception of the product or brand and brings it to ‘top-of-mind’ when the consumer is considering that type of product or service (Hiebing, Jr., et al.). Interest as a communication goal can tie back into the marketing goal of trial. Communications need to motivate the consumers to test a free product, do a trial, click through to a website, etc. (Belz & Peattie, 2012). Relative to the communications campaign, team marketing professionals are trying to


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Internal Organizational Environment show the fans/spectators how the campaign ties into the values of the fans, how the sport organization and the fans need to work together to accomplish campaign goals, and how being involved in the campaign is what loyal fans do. As the fans become more involved in the campaign, then they are more likely to move to the next stage or communication goal, active consideration. Active Consideration. Active consideration is a communication goal that causes the conversion of consumer involvement into consumer preference and intention to consume the product or brand. Marketers need to be able to show the consumer how their brand or product is better than alternative brands or products and need to be able to create such a favorable comparison that the marketer’s product is the one that the consumer will end up choosing (Young, 2014). Belz and Peattie (2012) concur, noting that at this stage marketers need to be able to change the consumer’s behavior, whether it is to get them to try a new product or to switch their brand loyalty. Of course, for our purposes, this stage is where we want the fan to commit to participating in the campaign and then actually participate (a marketing goal). As we have noted earlier in this chapter, these preceding communication goals should all lead to the marketing goals of participation, obtaining new customers, reacquiring lapsed customers, increasing purchasing, etc. The next communication goal (relationship building) focuses on what happens after the purchase, consumption, or initial participation. Relationship Building. Relationship building is probably the most important communication goal and is key to all the marketing and business goals. Relationship building, according to Grant (2007), also relies on an empathy approach, which focuses on reciprocity and enduring acknowledgment of the other entity in the relationship. Kim and Trail (2011) suggested that relationship marketing involving sport consumers is a “set of marketing activities to establish, enhance, and maintain a relationship with sport consumers for the mutual benefit of both the sport organization and the sport consumers” (p. 58). The key component of this definition is the focus on mutual benefits to the consumers and to the organization. The other key is the quality of the relationship. If the relationship does not have an excellent quality, then the likelihood of it lasting is low. The Seattle Seahawks typically have a very good relationship with their fans, the 12s. Relationship quality can be defined as an "overall assessment of the strength of a relationship, conceptualized as a composite or multidimensional construct capturing the different but related facets of a relationship" (Palmatier, Dant, Grewal, & Evans, 2006, p. 138). Kim and Trail (2011) suggested that relationship quality consists of five distinct but related concepts: trust, commitment, self-connection, intimacy, and reciprocity. According to Morgan and Hunt (1994), and paraphrased by Kim and Trail (2011), trust is “based on a judgment that the relationship partner is reliable and has high integrity and trust reduces opportunistic behavior and conflict in relational exchanges” (Kim & Trail, p. 61). Belz and Peattie (2012) concurred, indicating that trust was a key component in establishing marketing communications.


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Internal Organizational Environment Commitment, the second component of relationship quality, is defined as "an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is, the committed party believes that relationship is worth working on to ensure that it endures indefinitely" (Morgan & Hunt, 1994, p. 23). Obviously, the key to commitment is that both parties value the relationship and work very hard to maintain it for a long time. Kim and Trail (2011) noted that Fournier (1998) suggested that self-connection was a key component of relationship quality and defined it as a "relationship quality facet [that] reflects the degree to which the brand delivers on important identity concerns, tasks, or themes, thereby expressing a significant aspect of self” (p. 364). This is where the marketer ties in the values of the organization and the campaign to the personal values of the fan, showing that the organizational identity is similar to the fan’s self-identity. The marketer needs to show that the organization has the same concerns as the fans do. The fourth aspect of relationship quality is intimacy. Kim and Trail (2011) conceptualized intimacy as “the degree of familiarity, closeness, and openness to relationship partners” (p. 61), similar to how Fournier (1998) theorized it. Belz and Peattie (2012) also noted that communications need to be open so that they build trust and create innovation. The key in any communication is to make sure that all information is disclosed so that fans and spectators don’t feel that there is some type of disinformation going on. As long as there is an open dialogue that allows fans to be part of the decision-making process, then the relationship is sound (Belz & Peattie). Reciprocity is the final aspect of relationship quality as per Kim and Trail (2011). They noted that reciprocity is a “moral norm guiding social interaction among individuals” (p. 62) and according to Gouldner (1960) “evokes obligations toward others on the basis of their past behavior" (p.170). The key here is the feeling of obligation that each partner has toward the other because of past behavior. Within the communications campaign, this is focused on the feelings the fan has toward the organization and the team regarding perceived benefits that the fan receives and whether the sport organization also acknowledges the benefits that it receives from the fans. If there is equivalent reciprocity, then the fan is much more likely to participate in a campaign proposed by the sport organization because there is a feeling of obligation by the fan that it is his/her duty to participate. Solis (2015) suggested that there is a sixth component and that is empathy. Empathy is the ability to share feelings with another, showing that you understand what they are going through. If both partners in the relationship have empathy and


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Internal Organizational Environment understand the experiences and goals of the other, then the quality of the relationship is considerably better. This is critical to a communications campaign. If the marketer understands what the fans will need to go through to participate actively in the campaign, then the marketer is much more likely to develop the campaign in a way that, although it may be slightly challenging for the fan, doesn’t make it impossible to participate and be successful. Marketers need to understand all of these aspects of relationship building because developing and maintaining quality relationships with the fans is the key communications goal affecting the consumption and/or participation experience, which will lead to attitudinal loyalty and repatronage or retention of the consumers/fans (marketing goal). This in turn, leads to increased revenues and profits relative to economic goals and possibly greater participation relative to environmental and social justice goals. Building long-lasting quality relationships reassures the consumers and retains them in the face of criticism from competitors (Belz & Peattie, 2012) and negative publicity. One additional point needs to be made here. A key to developing and maintaining quality relationships with the consumers/fans is having a good customer-relationship management program and training employees how critical relationships with the consumers are (Young, 2014). If the organization and the consumer have a good relationship, then the consumer is much more likely to be satisfied with their interactions, which is the fifth communication goal. Customer Satisfaction. Another very critical communications’ goal is customer satisfaction. Marketers (and managers) need to be extremely aware of how customer satisfaction impacts movement along the consumer pathway. The better the relationship with the customer, based on the above concepts, the more likely the customer will be satisfied. When there are aspects that are displeasing to the customer, if the customer feels that there is open communication, trusts that the sport organization will solve the issue, and will alleviate concerns, then assuming the organization actually follows through and does solve the problem, the customer will probably still be satisfied overall. We will talk about satisfaction from the consumers perspective more in a future chapter, but in this section, marketers and managers need to realize that customer’s assessment of satisfaction starts with the first interaction the customer has with the sport organization, which may be at almost any stage of the consumer pathway except perhaps awareness. If the consumer is at the awareness stage, and has yet to develop any interest, then there is nothing to be satisfied or dissatisfied about. It is only after at least some level of interest has been engendered that the consumer starts assessing whether their interactions are satisfying or not. If you think about it, it makes sense. If you are aware of a new team in the area but have absolutely no interest in it (maybe it is team curling), then you don’t care enough to have an opinion, and


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Internal Organizational Environment thus have nothing to be dissatisfied about. Once you do have an interest though, then any interaction from then on could create satisfaction or dissatisfaction. For example, you live in Seattle, and have heard that there is going to be a new NHL team. So, you look it up on the internet and find their webpage.

Unfortunately, the web page is horrible. It loads slowly, doesn’t have the content you are interested in, has broken pages or links, etc. So, you shoot a message to the team using their ‘Contact Us’ link on the page, and the contact link to the service rep is broken as well. The whole experience is bad. Your dissatisfaction may prevent you from developing any relationship with the team ever, and they just lost a potential fan with a life-time value of thousands of dollars. However, let’s say that the contact link to a customer service representative on the webpage is not broken and you shoot off a message detailing your displeasure with the website. Within minutes, you get an apology from the team, an explanation that the website was in beta-testing and wasn’t supposed to be live yet. In addition, the service rep asks if there is anything that she can do for you or any questions she can personally answer for you. If she is able to alleviate your dissatisfying initial experience, then you are probably willing to continue to develop a relationship with the organization, assuming continued interest on your part. Eventually you might develop attitudinal loyalty to the organization, which is the next communication goal. Attitudinal Loyalty. Solis (2015) suggests that if the organization can engender attitudinal loyalty in the customers, then it increases the personalization of communications and interactions. In addition, it allows the organization to uncover insights and preferences the customer has leading to even better relationship building, more repatronage and greater retention. As Belz and Peattie (2012) note, the organization needs to reward the customers for their loyalty however. I would argue that although this is certainly one way of increasing retention, building quality relationships, as noted in the above section, will typically engender a much deeper loyalty than just receiving rewards. Specific to marketing plans though, because there is a range of attitudinal loyalty across fans and spectators, organizations should determine which segments might respond to rewards and which segments might feel affronted by rewards. For example, would you reward your significant other for being loyal to you? If so, you might want to examine your relationship a little more closely ☺. If you are in a committed relationship, giving your partner a new jacket because he didn’t cheat on you would hopefully affront him. Similarly, if you give


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Internal Organizational Environment loyal fans rewards for doing things that they feel are part and parcel of a quality relationship you might offend them. A quality relationship with your customers or fans will lead to attitudinal loyalty. In turn, attitudinal loyalty will also help us achieve the seventh and final communication goal. Advocacy. Ottman (2011) has a very valid point in that she suggests that the best way to promote a brand is to let the consumers of the brand be co-creators and “stoke the conversation” (p. 123) about how great the brand is. As we know, positive word of mouth (WOM) is the best advertising an organization can have. When friends and family recommend a brand or product, most of us are considerably more likely to purchase it if we are in the market for such a product or service. Marketers who have developed good relationships with their customers or fans and have engendered loyalty in those fans are inherently likely to experience their fans spreading the good word about the team. As advocates of the team, these loyal fans will be able to encourage their family and friends to become fans of the team. These advocates will be much more successful than the team will be getting others who may not be quite as interested in the team to start interacting more with the team. We know this based on the socialization chapter. As we have discussed and as we have shown in Figure 5.7 (several pages prior), these seven communication goals are related to, and help achieve, marketing and business goals. In addition, all of these communication goals are applicable to all of the business goals whether they are economic, environmental, or social, and thus influence the Triple Bottom Line. However, it is not possible to achieve all of the communication or marketing goals at once. It is necessary to be able to prioritize goals and that’s what we will discuss below.

Prioritizing Goals After reading the above information, some people might think that achieving all those goals would be feasible. However, that is rarely possible. It is necessary to prioritize goals because in most cases marketers or the organization will not have the budget to try to achieve multiple goals. So how can marketers determine which goal or goals to prioritize? Glad you asked. ☺ Marketers need to organize communication and marketing goals by their ability to drive business goals (Young, 2014). If the business goal is to increase revenue, then which marketing and communication goals are most likely to assist in accomplishing that business goal?


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Internal Organizational Environment Young (2014) suggests that only one goal should be chosen because if there are too many objectives then there may be a lack of clarity. He thinks that organizations should focus on one goal so that it can be fully achieved. However, I disagree. I think that sometimes there should be multiple goals. For example, if the economic goal was to increase profits, an environmental goal could be to minimize environmental impact (cutting energy expenditures), and an associated social goal that easily could be related to the environmental impact would be to enhance the quality of life of those in the community as the environmental impact is being reduced. Marketers could then have multiple relevant marketing and communication goals. In Figure 5.8, we have tried to show how all of this works, continuing with our fictitious minor league basketball team (Renton Roadrunners). One of their business goals was to increase profits by 10%. They determined that by meeting the marketing goals of increasing season ticket (ST) sales by 5%, reacquiring 10% of lapsed season ticket holders (STH), and achieving a 90% STH renewal rate, they would meet that business goal. Each marketing goal had associated communication goals. I have only listed two for each. For example, to achieve the marketing goal of increasing season ticket sales by 5%, if 20% of single game purchasers were interested in upgrading, and 5% did upgrade to season tickets, that would help meet that goal. In addition, if the Roadrunners were able to improve their ‘resistance to change’ scores (meaning that consumers were less likely to choose another entertainment option), that might help achieve the marketing goal as well. Figure 5.8

Increase Profits 10%

Business Goal

Marketing Goals

Communication Goals


Increase ST sales 5%

20% of SG upgrade

Increase Resistance to Change

Reacquire 10% of STH

Improve relationship building

Increase satisfaction

90% STH renewal rate

Improve RB scores

Improve satisfaction levels

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Internal Organizational Environment Hopefully you can see how the communication goals fit with the marketing goals, and how both are foundational for achieving the business goal. Now the next step is to examine how the business, marketing, and communications goals have impacted prior content (refer back to Figure 5.2). Content Content is all of the communications that the organization has previously created to inform, educate, and build relationships with their customers/clients. As Heidi Cohen from G+ has pointed out: Content is high quality, useful information that conveys a story presented in a contextually relevant manner with the goal of soliciting an emotion or engagement. Delivered live or asynchronously content can be expressed using a variety of formats including text, images, video, audio and/or presentations. When used for marketing purposes, content should incorporate the organization’s branding, be void of any form of promotion, and use a call-to-action to be trackable. Preexisting content should have been developed based on the business, marketing, and communications goals. However, it may not have been, so marketers need to make sure that they take this into account when creating new marketing plans and campaigns. The new plan or campaign should not conflict with existing content. If it looks like it may, then the campaign needs either to change, or the existing content needs to be eliminated or modified to be copacetic with the new campaign. As Scott (2015) noted, the function of content is to draw “visitors into the sales-consideration cycle and then funnel them toward the place where the action occurs” (Scott, 2015, p. 51). It helps buyers understand that the organization really is interested in creating and maintaining a long-term relationship with them. Thus, each piece of content that marketers produce and promote “must have a purpose” and be level-of-funnel specific. That is, “you should be creating content for each point in the funnel” (Simply Measured, August 25th, 2017). Sarah Garnsey, head of marketing and web communications at Textron Inc. said that “we make sure that the voice of the customer is captured and built into all of our electronic communications. We work on how to draw users into the content and use the site to form a relationship with them,” cited in (Scott, 2015, p. 133). They view that process as one of the primary functions of content creation. Specifically, Garnsey noted that content is “only as good as the management of the content and the processes” (Scott, 2015, p. 132). Scott followed up on Garnsey’s thoughts by noting that great content is not about the marketer’s company or products, great content will solve buyer problems or answer questions that shows that the organization is worth doing business with. Therefore, to create great content, marketers need to understand their audience segments, “consider what market problems their buyer personas are faced with and develop topics that appeal to


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Internal Organizational Environment them” (Scott, 2015, p. 225). This is why market research is so important; it is not possible to do these things if you don’t have the data and don’t know how to use the data to understand the audience. In addition, Scott says that marketers need to write specifically for each audience segment, using examples and stories that make the communication personal and interesting. He also suggests that marketers should use titles and subtitles that include keywords and phrases that their buyers are using to search on internet search engines. If we continue with our example of the fictitious Renton RoadRunners, their existing content primarily exists on their website and on their social media channels (Twitter, Facebook, Instagram, Pinterest, Snapchat, and LinkedIn). Unfortunately, most of their content is the same or very similar across all of their channels and is not updated very frequently during the offseason. However, their content does include information about the schedule, how to purchase tickets, stats about the team, information about the players and staff, promotions, and info for fans. Relatively basic content, very little of which would really motivate fans and/or spectators to progress along the consumer pathway. In addition, the RoadRunners send out email blasts, which are usually focused on promotions for upcoming games. Internal Organizational Insights Table Let’s put all of this information that we have discussed in this chapter together into a table, once again using the Renton RoadRunners as an example. This example is only going to include a small amount of the material that a marketer would actually need to collect, but I hope that it will give a broad idea of how it all fits together. Recall that this is a fictitious professional (minor league) basketball team playing in Renton, Washington, U.S.A., a suburb, just south of Seattle. They play in a venue that is owned by the city. I have created all of the material from existing data from other teams and have adjusted it to fit this scenario. Some of the information I had to make up so that it would make this example work. In addition, I am going to use tables to depict the information. However, there are many different ways of organizing the information. In addition, marketers need to assess all of this information succinctly in the SWOT analysis, or more accurately in the Strengths and Weaknesses (SW) part of the SWOT since we already did the OT part in Chapter 3. Strengths and Weaknesses are specific to the organization itself. The Strengths identify what the organization is doing well and obviously, the Weaknesses identify areas in which the organization


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Internal Organizational Environment needs to improve substantially. We have done this for the RoadRunners on the last two sections of Table 5.1 below. Table 5.1 – Internal Organizational Insights (Renton RoadRunners) Mission/Vision of RoadRunners Organizational Structure Brand Associations (this section is not complete yet because we need to discuss it in the next chapter). Price Points

Sales Trends

Business Goals Marketing Goals

Communication Goals

Internal Organizational Insights – Provide affordable family entertainment, – Deliver high-caliber professional basketball – Make a dynamic contribution to the Renton (and surrounding) community. – Flat, all managers report to the team president – Sales and marketing combined, no Director of Marketing Brand Associations Product Related Attributes Non-Product Related Attributes Product Benefits Organizational Constraints $14.00 - North Basket - Regular Price (1/2 price on Tuesdays) $14.00 - South Basket - Regular Price (1/2 price on Tuesdays) $24.00 - Premium - Regular Price (1/2 price on Tuesdays) $30.00 - Floor - Regular Price $40.00 - Club - Regular Price $60.00 - Suite Suite sales have trended lower over the last three years. Club ticket sales have increased and are maxed out currently. Floor tickets are sold out and have been over the last three years. Premium seats are down slightly over the last three years. South and North end seat tickets behind the baskets are down by about 10% over the last three years. – Increase profitability by 10% – Increase revenue by $400,000. – Trial: Distribute 100 ticket coupons per game to local elementary schools. – Obtain new customers: Increase new season ticket sales by 5%. – Reacquire 10% of lapsed season ticket holders. – Consumer participation: Hit STH Renewal rate of 90%, – Increase purchases: Increase single game sales by 10% – Increase consumption: Increase # of games STH attend by 10% – Repatronage: Increase repatronage by 3% – Improve brand image: Provide ultimate fan experience; -- Awareness: Improve Awareness to 80% of the market. -- Interest: Generate interest in 20% of the local market -- Active Consideration: 20% of SG ticket purchasers intend to upgrade to ST -- Relationship Building (RB): Improve scores on RB scale (trust, commitment, self-

connection, intimacy, reciprocity, empathy) to 4s and 5s out of 7.

Content

--Customer Satisfaction: Make sure all satisfaction scores are above the line on the Satisfaction/Importance graph; Fix parking issues (fan complaints) --Attitudinal Loyalty: Increase by 20% on Resistance to Change scale --Advocacy: Increase number of positive social media mentions by 20% – Schedule, – How to purchase tickets, – Stats about the team, – Information about the players and staff, – Promotions, – Info for fans – Pictures, videos


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Internal Organizational Environment Strengths

Weaknesses

– Previous success of the team on the court – Generates a decent attendance figure considering all of the issues – Players are viewed positively as good role models – Games are viewed as having good entertainment value – Club ticket sales are maxed out – Floor tickets sold out – Need better vision as to where the franchise should be headed and how to get there. – Organizational structure needs to be improved to improve effectiveness and efficiency of marketing and communications. – Fans and spectators do not think that the games are that exciting – Ticket sales are down in some areas. – Brand Image is losing luster – Concessions are a major problem – Venue aesthetics is poor – Parking is a problem

Now that you know how most of the Internal Organizational Environment affects the consumer environment and the consumer pathway specifically, we can really focus on the Brand Associations related to the sport organization and how they impact the consumer and progress along the pathway. On to the next chapter!


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Brand Associations & Organizational Constraints

Chapter 6 Brand Associations & Organizational Constraints

The University of Washington Huskies had a promotional campaign in 2016 that ran throughout the football season. It was a great campaign with multiple videos (almost weekly) posted on the team’s website, on YouTube, on Twitter, on Facebook, and on Instagram as well. The tagline was “Let Purple Reign Again” and it was genius! It incorporated many of the brand associations (product attributes and product benefits of the team) throughout its messaging. For example, the reference to “Again” in the tagline was a homage to the National Championship team of 1991. The “Reign” in the tagline had several meanings, primarily the goal of returning to greatness and staying there, but also a play on words relating to the rainy fall weather in Seattle. The “Purple” of course refers to the school/team colors and logo. There were dramatic plays shown in the video, shots of fans delirious with happiness of rivalry wins, and phenomenal athletic skill. Stories of hard work and ‘doing things the right way’ by players. Stories about the head coach and his philosophy. Talk about team personality. Beautiful pictures of the venue, its location, and general aesthetics. All of these brand associations were in many of the videos, and some were in other advertisements and promotions. Many were in specific communications to the fanbase. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints The Philadelphia Eagles teamed up with Pulte Homes to give away a new home to one lucky fan. Fans were able to sign up at the Pulte Homes showroom and enter the contest. Three people were chosen to compete during halftime of an Eagles game, where they had one chance to throw a football 25 yards and hit a designated target to win the house. Unfortunately, no one hit the target so there was no winner of a new house. Each of the three contestants did, however, win a gift package which included an Eagles hat, Eagles Yearbook and an autographed Eagles football. This promotional campaign was supposed to get people who may not have been Eagles fans to be introduced to the team through the sponsorship, but also to provide Pulte homes with an opportunity to get ROI for its sponsorship with the Eagles. They probably don’t need this partnership, now that they won the Super Bowl. The Los Angeles Dodgers have a stateof-the-art transportation center on the Club Level at Dodger Stadium. The Dodger Transportation Center uses numerous cameras to monitor the entire 255 acres of Dodger Stadium parking lots. The transportation center is manned by a transportation manager and parking manager who are in constant communication with 200 parking and security officers throughout the stadium property. In addition, more than 40 Department of Transportation engineers and traffic officers provide updates of parking and traffic outside the stadium. Dodger fans surrounding the stadium property can get continuous parking and traffic updates as they are entering and leaving Dodger Stadium by tuning in to 1610 AM on the radio station dial. These three examples above all deal with components of Brand Associations. In the last chapter we discussed all the different ways that marketers, managers, and their organizations can impact the customer environment, by developing relationships, and moving consumers along the consumer pathway. In addition, we discussed a little bit about how the product itself and content played a part in all of that. Now we are going to dive a little deeper into those areas which are call brand associations. As we discussed last chapter, the content is previous communications, messaging, advertising and promotions. These are things marketers can control. We will talk about how those things impact fans more in depth in a little bit. We will also discuss, the main product of the sport organization, which, obviously, is the game itself, but we will also discuss ancillary aspects as well. These are things that managers can control, at least to some extent. That is, owners, managers, and coaches can control what players are signed and the type of offense and defense the team runs. They can also control the service aspects in the venue. Yes, the game itself and the outcome are not controllable, but the rest is. Regardless, controllable or not, all of these things come under what is known as the organization’s brand. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Brand As noted in the sidebar, the definition of a brand is primarily focused on the name, design, symbol, logo, etc. However, in sport, the brand is quite a bit more than that. It is also all of those things that come under “other features” in the definition. In addition, within sport, the conception of what encapsulates a brand varies across levels of sports, types of sport, etc. For example, Duke is a brand, and when most people in sport think of Duke, they typically think of men’s basketball. Football, soccer, softball, or volleyball most likely don’t come to mind, when you are Brand. A brand is a "Name, talking about Duke as a brand. Yet, all of those sports are term, design, symbol, or any still part of the Duke brand. Similarly, if you talk about FC other feature that identifies Barcelona as a brand, most people outside of Catalonia one seller's good or service as will only think about the football (soccer) team. Yet, Barca distinct from those of other has many other sports within the organization including sellers." basketball, handball, futsal, and roller hockey, and that doesn’t even include all of the amateur sports that the club manages. In the U.S. though, most professional teams are the brand. The Green Bay Packers are the football team and they are the brand. There are no other teams or other things that are part of the Packers brand. The team and brand are pretty much the same thing on the surface. A marketer for the Packers knows that, and when they market the team they are marketing the brand, and when they market the brand they are marketing the team. That isn’t the case in other sport organizations that may have multiple sports and teams. In those cases, there are multiple products (basketball games, soccer games, football games, etc., and in some sports, both men’s and women’s games) and the marketer has to make a distinction between the products and the brand. However, even if the marketer has only one team, there are many aspects that are part of the brand that they need to consider, including brand associations, brand or product attributes, non-product related attributes, product benefits, and constraints. So, let’s talk about all of these things and how marketers and managers need to take these things into account. Brand Associations According to Keller (1993), “brand associations are the other informational nodes linked to the brand node in memory and contain the meaning of the brand for consumers” (p. 3). In plain English though, brand associations are the thoughts that come to mind when a consumer thinks about a particular brand (Aaker, 1991). Keller proposed that “brand associations can be classified into three major categories of increasing scope: attributes, benefits, and attitudes” (p. 4). Attributes are typically ascribed to products or services, rather than brands, and are the features that define or describe the product or service. Attributes can be split into product related attributes and non-product related attributes according to Keller. Product attributes are the “product's physical composition or a service's requirements” (Keller, 1993, p. 4). The AMA defines product attributes a little differently, noting that the attributes are the “characteristics by which products are identified and differentiated. Product attributes usually comprise features, functions, benefits, and uses.” Previous sport research has proposed a varied list of product attributes: Team Success (Gladden & Funk, 2001; Ross, Russell, & Bang, 2008), Star Player (Gladden & Funk, 2001), Quality Management (Kunkel, Funk, & Lock, 2017), Nonplayer Personnel (includes coaches, management, ownership; Kunkel et al., 2017); Team Personality (called Team Play in Ross et al., 2008; Biscaia et al., 2013); and Competitiveness of the Opposing Team (Rivalry; Ross et al., 2008; Biscaia et al., 2013). I am adding the following to the product attribute category: Aesthetic Play, Aggressive Play, Dramatic/Style of Play, Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Athlete Skill, and Competition (Rivalry) (see Table 6.1). These that I have added, have typically been called motives and historically have been looked at from the consumer perspective (Trail & James, 2015; Wann, 1995; Funk, Mahony, & Ridinger, 2002). However, in reassessing all of the potential motives, I have come to the conclusion that these that are mentioned above, are also product attributes because they are controlled to some extent by the organization itself, that is, by coaches/managers/owners, and how they construct the product. General managers obtain players and coaches that fit within their vision of what they want the end-product to look like. Coaches create systems for the players. Obviously, this may change over time. For example, when the Houston Rockets from the NBA had Hakeem Olajuwon and then Yao Ming at the center position, they ran an entirely different type of offense and defense to take advantage of those players strengths and built the team around them (Style of Play). However, recently, with the hiring of coach Mike D’Antoni a couple of years ago and the development of James Harden and the addition of Chris Paul, the team’s offense is now an up-tempo, three-point shooting, fast break, offense, with not a lot of defense (Aesthetic Play). This is a totally different product than the product in prior decades, and the attributes are entirely different as well. We will discuss all of these product attributes in more depth later in the chapter. You might be thinking: “Wait a second. Those are motives for fans to watch the game.” And you would be right, because those things (e.g., aggressive play) might motivate someone to watch a hockey game for instance. However, and this is the key point here, these things are product attributes as long as they are still in the realm of how the managers have designed and built the product, and how the marketers are marketing the product. Once the product attributes are communicated to the consumers and the consumers are aware of the product, then the perceptions of the consumers for each attribute become the potential motivators. Let’s try an example. A couple years ago, the Seattle Sounders (MLS), signed a bunch of very skilled players, some international and some not. The Sounders marketed those players and their skills. Although these players were very skilled individually and were perceived to be skilled on their previous teams, when added to the Sounders’ roster, they did not mesh well with existing players or each other. Uneducated Sounders fans perceived these players as not being as skilled as they were purported to be by the Sounders marketers. So even though the management thought that they were adding the product attribute of skilled players, and the marketers promoted the players in the media as very skilled, their lack of skillful play on the pitch when playing for the Sounders countered what the Sounders FC as an organization was trying to show and promote. Fan perception was totally opposite of the proposed and promoted attributes. In sum, product attributes as designed or communicated by the organization may differ considerably from the perceptions of fans. So, to be clear, what we are discussing in this chapter, are the product attributes as seen from the organization’s perspective. Now let’s get back to the other type of attributes. Non-product related attributes are aspects that are external to the product itself but are associated with the purchase or consumption of the product or service. Keller (1993) suggests that there are four primary non-product related attributes: “1) price information, 2) packaging or product appearance information, 3) user imagery (i.e., what type of person uses the product or service), and 4) usage imagery (i.e., where and in what types of situations the product or service is used)” Keller, 1993, p. 4). However, some of these are not adequately explained as we will see in a little bit. Second, there are others that Keller didn’t take into account, and third, not all of these apply to sport. Keller (1993) said that price is not a product attribute because it “does not relate directly to the product performance or service function” (p. 4). This is somewhat obvious, because with a sporting event, unlike other products, many consumers pay a wide range of prices for the same product. For example, I bought a ticket for $8 from a scalper on the sidewalk a couple minutes before a Seattle Mariners’ game started. Other people who attended that same game paid as much as several hundred dollars for a ticket. Did they have a better seat than I did? Sure, but that didn’t change the product on Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints the field. The game was the same. The performance of the players did not change because I paid $8 and someone else paid $500. Other than tickets, the prices of concessions, parking, merchandise, and media could be included in this category. Packaging, according to Keller (1993), does not “directly relate to product performance” (p. 4). Relative to most products, this is true. If your Coke comes in a can or in a bottle (different packaging), the Coke still is the same product assuming the same bottling company and ingredients for both. However, with sport, there might be exceptions to the this. If we consider packaging to include the venue in which the team plays, then sometimes the packaging might impact the product. Let’s say we have a stadium that has a retractable roof like some do in North America. Many of these stadiums are hypothesized to influence the game depending on whether the roof is open or closed. If true, then that means that the “packaging” (roof open or closed) could have an impact on the product. For example, at Rogers Centre in Toronto where the Blue Jays play, an analysis was done relative to home runs hit when the roof was closed versus. when it was open. It was found that about 67% of the longest home runs hit in that ballpark during the 2013 season were when the roof was closed. In general, however, the “packaging” for sporting events doesn’t usually have an impact on the product itself, although people’s perceptions of, or experiences with, the “packaging” might influence their perceptions of the product. Some of the “packaging” aspects that have been investigated by sport researchers include: Brand Mark/Logo (Gladden & Funk, 2001; Ross, James, & Vargas, 2006, 2008; Biscaia et al., 2013), Venue Aesthetics (Gladden & Funk, 2001; called stadium community in Ross et al, 2008), Venue Location (Ross et al., 2006), and Quality of Concessions (Ross et al., 2008; Biscaia et al., 2013). I have added Venue Cleanliness, Concession Service, Parking, Employee Service, and Seating to this category (see Table 6.1). Although these have not been investigated as non-product attributes per se, they have been examined within other perspectives (e.g., satisfaction, service quality, etc.). Keller’s (1993) third category of non-product attributes includes the imagery attributes communicated in “brand advertising or by some other source of information (e.g., word of mouth)” (p. 4). This would include promotions generated by the sport organization, any marketing messaging or advertising by the organization, and in general, any communications that attempt to develop relationships with the consumer. As far as I can tell, none of these have been investigated as nonproduct attributes except perhaps something that Ross et al. (2008) called Organizational Attributes but seem to represent relationship building. Other than that particular aspect, some of these have been researched under other perspectives, so we will include them here. The final category by Keller (1993) is the personality of the brand or the “character of the brand itself” (p. 4). Brand personalities can be as varied as human personalities and could include rugged, sophisticated, competent, sincere, and exciting (Aaker, 1997). However, there hasn’t been a lot of support for these brand personality measures either in sport or other products (Braunstein & Ross, 2010). However, some people have proposed that different teams have different personalities, and just from a totally unscientific investigation, that is probably true. Obviously, the Oakland Raiders personality Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints (outcasts/bad boys) is different than the LA. Rams personality (white hats/good guys). The Pittsburgh Steelers personality of blue collar differs from the Dallas Cowboys white collar personality. We talked a little bit about this in the chapter on external environment, where the teams take on characteristics of the cities that they play in. We will discuss all of these non-product attributes in much greater detail individually later in the chapter, but now we will turn our attention to the other major dimension of brand associations that Keller (1993) identified and that is product benefits. Benefits are what consumers think the product or service can do for them according to Keller (1993). According to Park, Jaworski, and Maclnnis (1986), benefits can be grouped into three categories: functional benefits, experiential benefits, and symbolic benefits. These benefit categories differ by the underlying motivations people ascribed to them. Keller noted that functional benefits are “often are linked to fairly basic motivations, such as physiological and safety needs” (p. 4) as per Maslow (1970). In addition, Keller also suggested that product or service consumption could correspond to product-related attributes mentioned above. This is a rather limiting definition of functional benefits and would be hard to apply to sport. Gladden and Funk (2001) listed Escape as a benefit in this category, but I disagree because escape is not a physiological or safety need. However, it probably fits better in the experiential benefits category. I have added Social Interaction Opportunities as a construct in this category, because, although it doesn’t fit within physiological and safety needs, it does fit within Maslow’s belongingness needs, which is the next step up on his pyramid of needs. Keller (1993) suggests that “experiential benefits relate to what it feels like to use the product or service and also usually correspond to the product-related attributes” (p. 4). He notes that these benefits meet needs such as “sensory pleasure, variety, and cognitive stimulation” (p.4). If we go with this definition, then Gladden and Funk’s (2001) concept of escape, which I am calling Diversion similar to Kunkel et al. (2017) to differentiate it from the motive of escape, would go in this category because it gets at the variety provided by sport to create diversion. Gladden and Funk also suggested that nostalgia and pride-in-place were two constructs that should be included in this category. Nostalgia makes sense because it fits within the sensory pleasure definition, but pride-in-place is a little more difficult to fit into this category because it doesn’t necessarily meet sensory pleasure, variety or cognitive stimulation needs, but it does fit into the symbolic category better. In addition, I am adding two more categories. The first, called Information Provision, represents the information provided by the organization, which is the cognitive stimulation that Keller notes. The second is Athlete Attractiveness, which represents the physical attractiveness of the athletes and certainly fits into the ‘sensory pleasure’ noted above by Keller. The final category of benefits is labeled symbolic benefits by Keller (1993). He suggests that “they usually correspond to nonproduct-related attributes and relate to underlying needs for social approval or personal expression and outerdirected self-esteem” (p.4). Keller, citing Solomon (1983), proposes that it may be the prestige, exclusivity, or popularity (he said ‘fashionability’) of the brand that helps increase the consumers’ self-concept. Gladden and Funk (2001) redefined the symbolic category and suggested that it satisfied “needs for improvement, status, group affiliation, and ego enhancement” (p. 74). They suggested that fan identification and peer group acceptance were two constructs that were contained by this category. To me it makes sense that peer-group acceptance fits into this category because that represents an aspect of popularity and exclusivity, which is why I am calling it Popularity instead. I’m having difficulty distinguishing fan identification from peer-group acceptance based on Gladden and Funk’s definition and the items in the constructs. They differ only in perspective. For example, an item in peer-group acceptance is “I follow my favorite team because my friends like the same team,” which is very similar to the item “It is important that my friends see me as a fan of my favorite team” from the Fan Identification construct. The first is inner-directed and the latter is outerdirected. I don’t think that both constructs are necessary. In addition, I am adding several other Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints constructs that I feel are symbolic: Pride in Place (which I mentioned above needed to be moved here), Supporting a Cause, and Players as Role Models. Supporting a Cause, such as supporting women’s sports, is a personal expression, and thus fits into Keller’s definition of this category. Players as Role Models also fits because it is a non-product related attribute and might fit as a symbolic benefit (Table 6.1). I am also adding another category to brand associations because marketers and managers need to be able to understand that there are constraints associated with the brand. Many of these constraints are just the opposite of either the attributes or benefits, but often are not sufficiently identified or dealt with. I have included Lack of Communication, Price (too high), Ticket Scarcity, Poor Player/Employee Behavior, Lack of Access, Losing Team, Poor Ticketing Processes, Games being on TV/Streamed, and Games being on the Radio. We need to view all of these constraints from the perspective of how the organization needs to address and deal with these potential barriers to consuming the product. So now let’s talk about each of these constructs in each of these categories much more in depth. Table 6.1 Attributes Product related Non-product related Team Success Price (Game, Concessions, Merchandise, etc) Star Player Brand Mark/Logo Quality Venue Aesthetics Management/ Front Office Quality Head Venue Location Coach Aesthetic Play

Dramatic/Style of Play Athlete Skill

Cleanliness of the Venue Quality of Concessions Concession Service Parking

Team Personality

Employee Service

Competition (Rivalry)

Seating

Aggressive Play

Brand Associations Benefits Functional Experiential

Symbolic

Social Interaction Opportunities

Nostalgia (Team History/ Tradition) Diversion Information Provision

Popularity (Commitment) Pride in Place Supporting a Cause

Price (too high) Ticket Scarcity

Athlete Attractiveness

Players as Role Models

Poor Player/Employee Behavior Lack of Access

Constraints Internal Organizational Lack of communication

Losing Team Poor Ticketing Processes Game being on TV/Streamed Games being on Radio

Relationship Building Promotions Advertising


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Brand Associations & Organizational Constraints Product-related Attributes Team Success Team success was defined by Gladden and Funk (2001) as winning, making playoffs and competing for championships. Gladden and Funk (2001) and Ross et al. (2008) found that as people’s perceptions of team success increased they were more likely to be a fan of the team and be loyal to it. Biscaia et al., 2013 replicated those findings to some extent but found that Team Success predicted Satisfaction better. In a reanalysis of the Kim and Trail (2010) women’s college basketball team data, success didn’t predict team identification (being a fan) or intentions to attend women’s college basketball games at all for either fans or non-fans. There was, however, an interesting finding from that data. Among the people that rarely came to a game, we found that if they perceived the team to be successful, then they would overcome the perceived constraint of having no one to attend with. Those responding reported they would attend games if the team were successful, even if no one attended with them, so they could be associated with a successful team. However, because the women’s team was not perceived as successful by this group of people they went to the men’s basketball games instead because the men’s team had the product attribute of being successful. Star Player Gladden and Funk (2001) defined this construct as the “presence of a player that is outstanding. Often defined by allstar appearances” (p. 73). That is a limiting definition because not all leagues have an all-star game. So, I am eliminating that aspect of the definition and relying on the consumers to decide who is outstanding or not. Gladden and Funk didn’t find that star player had much of an impact of being fan and Kunkel et al. (2017) found that it had no impact on attendance, TV, or merchandise consumption. Neither Ross et al. (2008) or Biscaia et al. (2013) included star player. In a reanalysis of the women’s college basketball data I mentioned above, I found that if fans of the team and non-fans perceived that the team had a star player, their level of fandom would increase. Unfortunately, neither thought that the team had a star player. In addition, if fans thought that the team had a star player, then they were more likely to attend games, but this did not impact non-fans. Quality Management/Front Office Gladden and Funk (2001) called this construct Management and defined it as the “ability of the organization to garner trust from consumers in that consumers believe management is doing its best to satisfy consumer needs” (p. 73). They found that this had a very limited impact on being a fan or fan loyalty. Kunkel et al. (2017) found it had even less impact and no impact on game attendance, TV consumption or merchandise consumption. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Quality Head Coach Gladden and Funk (2001) defined this concept as the “presence of a head coach that has a track record for success and/or possesses significant charisma” (p. 73). This is a bad definition because it includes to concepts that are not necessarily correlated with each other. Yes, sometimes charismatic coaches can be very successful, and sometimes not. In addition, coaches that have no charisma (e.g., Bill Belichick the head coach for the New England Patriots) can be very successful. Gladden and Funk found that the quality of the head coach was only minimally associated with team identification or team loyalty. Ross et al. (2008) combined the two above dimensions (Management & Head Coach) and called it Nonplayer Personnel. Biscaia et al. (2013) modified it again and called it Management, focusing solely on managers and whether the fans liked them or not. Both Ross et al. and Biscaia et al. found that this concept was related to being a fan and Biscaia et al. found that it was related to customer satisfaction and to some extent related to behavioral intentions. Aesthetic Play Aesthetic play is defined as the beauty and artistry exhibited in competitive sport (Guttman, 1986; Wann, 1995). For example, a beautiful fast break in basketball, a long touchdown pass in football, a spiral or a Camel spin in figure skating, all can be considered aesthetically pleasing. Milne and McDonald (1999) suggested it was the beauty, grace, or other artistic characteristic of sports that were attributes that attracted people to sport. Madrigal (2006) concurred, suggesting that aesthetics refers to the “consumer’s appreciation for the grace and beauty of the sport itself” (p. 271), and aesthetic quality of play has long been associated with spectatorship (Sloan, 1989; Wann, 1995). In some data I have from a university in the northwest, all groups (students, faculty/staff, alumni, and general public) thought that the team’s play was not aesthetically pleasing at all. In addition, there was no relationship between aesthetic play and attendance intentions. This was contradictory to earlier research that showed that aesthetic play was positively related to being a fan of the team and to a smaller effect to attendance (Kim & Trail, 2010). In that data, we also found that fans of the team definitely thought that the team had aesthetically pleasing play, versus non-fans who thought that the team’s play was not aesthetically pleasing at all. Robinson and Trail (2005) suggested that if a team is not successful, which was the case in their data, marketers should use a marketing communication strategy that focused on the aesthetic qualities of the sport itself, rather than the specific team. Aggressive Play Aggressive play is defined as the perception that players and the team in general, is very violent or hostile, in their style of play (McDonald & Milne, Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints 1999). Initial support for this idea was reported by Bryant, Comisky, and Zillmann (1981) who found that as people watched clips of increasingly rough play from NFL games, their enjoyment increased. In other words, as the play depicted in the clips got rougher, ratings of enjoyment increased. Obviously, hockey, in the United States and Canada especially, is known for its fighting and aggressive play. In fact, the NHL has been known to market fighting and hard hits. Olympic hockey, however, is almost an entirely different sport with very little fighting and significantly fewer hits. In some research by Lee, Trail, and Anderson (2009), they asked both season ticket holders (assumed to be fans) and single game attendees (assumed to be spectators) whether they were motivated by the fighting and rough play during the hockey games. Both fans and spectators alike were motivated by the aggressive behavior of the players, however, season ticket holders were motivated considerably less by aggressive play than were single-game ticket purchasers. It seemed that true fans of the team were motivated by things other than player aggression. Dramatic Play or Style of Play People typically prefer games to be dramatic and exciting, where the outcome is uncertain until the end. Kim and Trail (2010) found that once other product attributes (they called them external motivators) were taken into account, drama did not predict attendance very much at all. In addition, this was true for both fans and non-fans. So, a supposition in a previous edition of this book is not true: people are not more interested in watching games that are dramatic when they do not have a favorite team involved. If my favorite team is playing, however, I just want them to win! It doesn’t need to be dramatic at all. These results were also reflected in the data from the Northwestern university: people who were fans of the team wanted drama, but apparently only if their team was on the winning side. Style of Play is defined as the perception that a team has a particular style of play, maybe fastpaced or defensively intense, which can make it dramatic. I gave an example earlier in the chapter about the Houston Rockets and their current coach Mike D’Antoni, so let’s continue with that example here. D’Antoni coached the Phoenix Suns back in early 2000’s and instituted a run-and-gun offense. Many Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints people became Suns fans because of that style of basketball, enjoying the fast, up-tempo offense, with lots of fast-breaks, dunks, and 3-point shots because they found it dramatic. However, it wasn’t successful in the playoffs and eventually he was fired. The New York Knicks hired him, but his style of play wasn’t successful there, nor in Los Angeles after he was fired from the Knicks. So, an interesting question would be whether people perceived that style of play to be interesting and entertaining even if the team was losing. However, now that he is with the Rockets and winning, are people attracted to the Rockets? Unfortunately, very little research has been done on style of play as a product attribute, although Zhang and his colleagues have looked at it as a market demand variable (Braunstein, Zhang, Trail, & Gibson, 2005). Athlete Skill Athlete skill is defined as the perception that certain athletes may have exceptional physical skills for performing that athlete’s sport or event. For example, a 350-pound offensive lineman blocking a defender that allows the running back to make yardage up the middle during a football game could be a physical skill that some people might appreciate. Another example is a basketball player setting a back-pick for a teammate so that the teammate gets an easy lay-in. Both fans and spectators alike can perceive quality physical skills in athletic events. People may be fans of a particular team because the players, or a player, executed certain skills very well. Both Fink, Trail, and Anderson (2002) and Robinson and Trail (2005) showed that this product attribute was related to being a fan. In a reanalysis of some women’s college basketball data, we found that people that had attended a game that season perceived that the players had greater athletic skills than those who hadn’t attended ever or hadn’t attended recently. It is possible that spectators may be motivated to watch a particular game in which they have no specific interest in either team because of expectations about the physical skills of certain athletes who may be playing in the game. Team Personality Ross et al. (2008) called this concept two different things in the same paper (team play; team characteristics), so one of them is mislabeled and I think it probably is Team Play. Regardless, they measured it by items referring to team personality, qualities, and characteristics. Team personality fits more closely with Keller’s (1993) framework so we will go with that one. Ross et al. found that team personality (characteristics) was related to being a fan fairly highly. Obviously, this is something that marketers can advertise, and many do. In addition, it is less risky than trying to market success. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Competition Ross et al (2008) defined this concept as the “competition among teams that are known to be historically significant competitors” (p. 326) and called it Rivalry. However, only one item, maybe two, out of the four, deals with “rivalry”, whereas all four deal with the competitiveness of the opposing team. So, I renamed it Competition. Although Biscaia et al. (2013) used the same scale, they deleted this construct. In the Ross et al. data, it was only moderately related to being a fan or not. Marketers however often do market the opposing team, especially if it is a rival, or is supposed to be a competitive game. Thus, even though researchers haven’t dealt with this concept sufficiently well, marketers know of its inherent value. Non-product Related Attributes Price Price is a non-product related attribute as I noted above, but it is not just the price of a ticket to the game that marketers must consider; it is the total cost of going to the game, including the price of concessions, parking, merchandise, ancillary products, and possibly travel to the game as well. Although price is a non-product related attribute, it rarely has a positive influence on attendance or on being a fan of a team. Usually if it has any impact it will be negative, and if people think that ticket costs are high or going to the game is too expensive, they won’t go. So, we will examine this more within the section on constraints. However, we do know that people have different perceptions of both season ticket prices and single game ticket prices. Using the women’s college basketball data again, I found that those who attended during the current season, perceived both single-game and season tickets as pricier than those who never attended or who had attended before (but not during that season). An interesting thing that the Oakland Athletics just did to address the price issue was to eliminate their typical season-ticket format for the 2019 and create something called A’s Access. People buy memberships and get general-admission access to every game, half-price concessions, 25% off merchandise, a reserved seat plan, and upgrade credits. Plus, those fans that buy now, get first shot at 2018 post-season tickets if they make it. They need to do something creative because they are 3rd last in attendance so far in 2018 and have typically been at the bottom of the league. Brand Mark/Logo Gladden and Funk (2001) called this concept Logo Design and defined it as the “use of corporate logo and marks to establish and reinforce and image” (p. 73). Ross et al. (2008) call it Team Mark (or Logo) and defined it “as the identifying marks associated with a specific sport team” (p. 326). Gladden and Funk, Ross et al., and Biscaia et al. (2013) found that it had a small association with being a fan, but Kunkel et al. (2017) did not. Biscaia et al. found that it was related to customer satisfaction to some extent, but not behavioral intentions. I am very surprised that Kunkel et al. didn’t find a relationship between it and merchandise purchasing. I would think that it would show up there more than elsewhere. Marketers certainly do understand the value of the logo and typically brand all communications with it, which makes sense, even if it only has a small impact. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Venue Aesthetics This concept has been called several different things, but it really is supposed to represent the venue enhancing the spectating experience. Gladden and Funk (2001) call it Stadium/Arena and Stadium Community, and Kunkel et al. (2017) term it Stadium Atmosphere. Regardless of what you call it, it seems to have little impact on being a fan of the team, team loyalty, or game attendance (Biscaia et al., 2013; Gladden & Funk; Kunkel et al.; Ross et al., 2008). However, Biscaia et al. did find that there was a

relationship between the venue aesthetics and customer satisfaction though. I’m sure that you have seen marketing communications that tout the stadium, arena, or whatever venue. It makes senses that marketers promote a good venue experience if they have it, but even if the stadium is great, fans don’t really care, as long as it meets a minimum level of comfort and aesthetics. Spectators might care more and might come solely to see the venue, especially if it is a new one, but that is due to the novelty effect and doesn’t last. That said, if the stadium is really bad, for example the Oakland Athletics stadium, it might serve as a constraint if it negatively impacts the fan experience. Location of the Venue The location of a venue can be a nonproduct attribute that can be perceived as a positive or a negative. Some newer venues have been built in very nice locations, with water (San Francisco Giants) or city skyline (design drawings of proposed remodel of Key Arena in Seattle) views. Some have been remodeled to take advantage of historic landmarks in the city, for example Camden yards in Baltimore. However, oftentimes the location of the venue can be perceived as a constraint if the venue is in a bad part of town, not easily accessible because of traffic patterns, or if there is a perception of difficulty of getting there due to distance. For example, Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Carmichael, Millington, and Simmons (1999) found that the farther people had to travel to the venue, the less likely they were to go. Trail, Robinson, and Kim (2008) found that there were no differences between those who attended the football games and those who did not based on perceptions of whether the location of the venue was a constraint. From the reanalysis of the women’s college basketball data, location of the venue was a positive. We separated distance from the other location items to examine it as a distinct item and found that the non-attendee groups did perceive it as a slight constraint whereas those that attended did not. Similar results were evident specific to the amount of time it took to get to the venue. Obviously, the location of the venue is not something that can be controlled if it is an existing venue, but the perception of the location can be modified by marketing if it is perceived as a constraint or reinforced if it is perceived to be a positive. Spectator concerns can be addressed through marketing the positives of the location. However, in some instances the venue owners will need to get assistance from the community to improve the area around the venue. Cleanliness of the Venue Cleanliness of the venue includes cleanliness of restrooms, seating areas, concession areas, concourse areas, etc. As I noted above, this is a new construct that I added to non-product related attributes. In a reanalysis of some data that I had from both men’s and women’s college basketball game, which were played in the same venue, I found that both men’s game and women’s game attendees thought it was very important that the venue should be clean (5.5 and 5.7 on a 7-point importance scale). However, whether it was clean or not made no difference as to whether they were a fan or not and did not impact their attendance in the slightest. Similar to the venue location though, as long as the attendees perceive the venue to be clean, then there are no issues, but if the spectators think that the venue is nasty-dirty, then there will be complaints and dissatisfaction. So, managers need to be aware that this non-product attribute can be an issue if it is bad enough. Marketers don’t typically need to worry about this too much unless it was an issue that was fixed and now needs to be communicated to the fans. Quality of Concessions Ross et al. (2008) labeled this as Concessions, but the items measured the quality of the food sold at concessions. Concessions can be more than just food though, it can also include service, cost, and cleanliness. Regardless, similar to other aspects in the venue, concessions have little impact on anything (Biscaia et al., 2013; Ross et al.), not even customer satisfaction. I found similar results when I reanalyzed the men’s and women’s college basketball data. It did not impact fandom or attendance. What was interesting though, was that attendees at women’s games thought that concessions were more important than those at the men’s games. All aspects were more important, the quality, the cost, the selection, the courteousness of the servers, and the overall concession experience. Again though, unless there is a total disaster with concessions continuously, it probably will not matter to the fans or spectators. However, last year, the Atlanta Falcons decided to cut costs on all of their concessions by about half. The fans loved it and were extremely happy to consume more than normal. The Falcons ended up making


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Brand Associations & Organizational Constraints 16% more than they did the prior year on concessions net sales. It would be interesting to know whether this made more people Falcons fans or come to more games, but I doubt it. Concession Service I mentioned concession service above in passing, but this is another non-product attribute that does impact the stadium experience. What was interesting in the men’s and women’s college basketball data was fans thought that good service was just as important as the quality of the food and more important than the cost. Granted, in this particular situation, the cost wasn’t that high for concession food and the quality definitely was not great. Marketers typically don’t market concessions much at all, unless there is a unique food being offered, but I don’t think I have ever heard of someone marketing good concession service. Let me know if you have and I’ll put it in the chapter. 😊 Parking Parking is a non-product attribute that can potentially be a positive if the experience is a good one, but more frequently it can be an external constraint for many people if it is difficult to access, not very close to the venue, or if it people think the fee for parking is too high. Apparently, some of the closest lots at the Dallas Cowboys’ stadium cost up to $60 per game for parking. As we indicated at the beginning of the chapter, some franchises such as the Dodgers spend quite a bit trying to make parking easier so that it is not perceived as a potential constraint and prevents anyone from coming to games. Trail et al. (2002) found that the availability of parking, the time it takes to park, and distance from parking area to venue were all important to the spectators and suggested that parking concerns might reduce attendance. Specifically, even though parking was free and fairly close to the venue, the difficulty of egress from the parking lot caused the attendees a fair amount of dissatisfaction. There were only three exits from the lot and the event staff was not trained well enough in directing traffic. This caused people to spend up to an hour waiting to get out of the lot. From the reassessment of the college women’s basketball data, I found that the perceptions of parking differed for the three groups with current attendees perceiving it to be easier to access and more plentiful than the other two groups of non-attendees and lapsed-attendees. However, none of the groups felt that any aspect of parking in this instance was a constraint. This was probably since parking was free, close to the venue, and egress and ingress were easy because attendance at the games was very low, thus decreasing the crowding. Trail, Robinson, and Kim (2008) found similar results with a Division II football team. Parking was not a constraint because of cheap, close, and easy parking. Employee Service Yoshida and James (2011) called this construct Frontline Employees and looked at how these people “in the trenches” responded to attendees’ needs. Biscaia, Correia, Yoshida, Rosado, and Maroco (2013) used the same scale and found that Frontline Employees had no impact on either satisfaction or behavioral intention. Looking at the data from the men’s and women’s college basketball games, all attendees thought employee service was important, but it didn’t impact their fandom or their attendance at all. That said, the same caveat applies. As long as the employee service is at least at a certain level, it won’t impact people and their game experience. However, if it gets really bad then managers need to get involved. It would be rare that marketers would need to deal with these types of issues unless it was a recovery campaign due to some catastrophically bad service. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Seating Yoshida and James (2011) also examined the impact of seating on the customer experience. They found that it had very little impact on convenience, organization identification, or the entertainment experience. Biscaia et al. (2013) found similar results in that seating had almost no impact on satisfaction and less on attendance.

Relationship Building Ross, James, and Vargas (2006) titled this construct organizational attributes and defined it as “thoughts regarding specific attributes that characterize the sport organization as a whole; organization’s loyalty to fans, management actions, and brand personality” (p. 270). The items in Ross et al. (2008) research representing this construct measure relationships with fans more than organizational attributes so we will call it Relationship Building. They found a moderate relationship between this construct and team identification, but Biscaia et al. (2013) found a larger correlation with being a fan and a substantial relationship with customer satisfaction. The relationship with behavioral intentions was considerably smaller though. We discussed the importance of building solid fan relationships in the previous chapter and this shows that this is an area that marketers and managers need to spend time on because it does impact people developing into fans, being satisfied with their game experience, and to some extent, increasing their consumption behavior. Promotions At the beginning of the chapter I included the example of the Philadelphia Eagles halftime promotion along with a sponsorship opportunity for Pulte Homes. Many sport organizations rely heavily on promotions based on the assumption that they will increase attendance. Previous research has found a positive impact of promotions on attendance (Hansen & Gauthier, 1989; Zhang, Pease, Hui, and Michaud, 1995) which served as a justification for McDonald and Rascher’s (2000) suggestion that marketers should focus on promotions that are influential and controllable. I beg to differ. Sometimes promotions are not controllable, for example disco demolition night at Comiskey Park in 1979. Also, we are really Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints more concerned with the influence of promotions rather than the idea of “controllability.” Although promotions may generate a slight uptick at the gate for a single game if the promotion is a good one, promotions typically cost a fair amount and there is little evidence to show that there is any return on investment over the long run. From the information in Figure 6.1, it is apparent that promotions such as giveaways, pre-game events, and post-game events are in the midpoint area of the scale, which means they have little to no positive impact on attendance, but you can see the three groups do differ on their perceptions of how impactful some of the different promotions would be on their attendance. Kim and Trail (2010) found that even though there was a significant impact of promotions on attendance, only 4% of the variance was explained, leaving 96% of attendance explained by other things. With respect to influence, that is not a good return on investment. What typically happens is a team may find a promotion that attracts people to the game, but only for that one game. Many people come to the one game so that they can collect the promotional items or experience the promotional event, but never return. Or, some were coming to the game anyway, regardless of the promotion, so they picked up the promotional t-shirt or whatever, but it didn’t have any impact on them coming to additional games because they already were coming to additional games anyway. Most promotions that have been used in the past do not create attachment to the team or generate future attendance.

Promotions

Figure 6.1 6.00 5.00

MEAN SCORES

4.00 3.00 2.00 1.00 0.00 Special promotions

Giveaways during the game

Halftime events

Pre-game events

Post-game events

Never Attended

4.42

4.32

4.09

4.06

3.97

Didn't attend this season

4.70

4.44

4.23

4.12

4.02

Attended this season

5.27

4.97

4.59

4.48

4.24

Advertising Many teams think that advertising the team through newspaper ads, TV commercials, billboards, radio ads, the internet and media in general is a valuable use of funds. The information in Figure 6.2 indicates this idea may be misguided. In the data depicted, media advertising was shown to be neutral as a motivator for attending. In addition, Kim and Trail (2010) found that advertising did not predict attendance at all. Fink et al. (2002) found that men and women did not differ on the effect of advertising on attendance; both males and females indicated that advertising had no effect. Fink et al. also found that although advertising through the media had a greater impact on getting potential spectators to attend women's games than to attend men's games; it was not very effective for either. They suggested that their results were similar to Mawson and Coan’s (1994) concerning the effectiveness of advertising on sporting events that are highly attended but suggested that advertising Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints might be more effective for sporting events that have low attendance. Figure 6.2

Advertising 6.00

MEAN SCORES

5.00 4.00 3.00 2.00 1.00 0.00

Newspaper ads for XX Women’s Basketball games

XX Women’s Basketball television commercials

XX Women’s Basketball billboard ads

Radio ads for XX Women’s Basketball games

Media publicity about XX Women’s Basketball games

Never Attended

4.20

4.02

3.88

3.90

4.17

Didn't attend this season

4.47

4.30

3.95

4.14

4.41

Attended this season

4.88

4.43

4.27

4.41

4.73

Trail, Kwon, and Anderson (2009) found that in situations “where there was a positive outcome (i.e., the home team won), advertising had no effect on either the satisfaction with the decision to attend the game or the satisfaction with the outcome of the game for low-behavioral-involvement spectators” (p. 120). That is, for those spectators who typically did not come to games, advertising had no effect on satisfaction level with attending the game or satisfaction with the outcome of the game. Furthermore, similar results were found with low-involvement spectators who came to a game in which the home team lost. There may be a small glimmer of hope for those that believe that advertising has a positive impact on getting people to attend games. In the Trail et al. (2009) research, they also determined that when the team lost, advertising seemed to have a slight mitigating effect on future attendance. That is, if people were exposed to advertising before attending the game, if the team lost, these people were more likely to indicate that they would attend future games than people who indicated that advertising was not influential. According to Trail et al. because “there was an increase in the perception of advertising as a motivating factor to attend games, the likelihood of future game attendance, merchandise purchasing, and team support increased. This relationship was much more apparent in the spectators who watched the team lose (a potentially negative consumption experience) than in the spectators who watched the team win (a potentially positive consumption experience)” (p. 120). Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Benefits Social Interaction Opportunities Social Interaction opportunities refers to the frequently fortuitous social interaction that is freely entered into by two or more parties watching a particular sports event (Guttmann, 1986; Melnick, 1993). Both fans and spectators see the benefits to watching sporting events because of the opportunity to interact with other people (other attendees, friends, acquaintances, etc.). For fans of the team, research has shown that there is a small, but direct, relationship between social interaction opportunities and being a fan of the team in some instances (Biscaia et al., 2013; Fink et al., 2002, Robinson & Trail, 2005; Ross et al., 2008; Wann, 1995), but surprisingly in other instances there seems to only be an indirect relationship through attachment to the organization associated with the team. Specifically, I found that for fans of the team, the opportunity for social interaction was associated with attachment to the university, which was related to team identification. However, there was no direct effect between social interaction and team identification. For spectators though, this relationship did not exist. What did exist for spectators was a relationship between social interaction and being a general sports fan. Other research has shown that a relationship exists between social interaction and television viewing (Shamir & Ruskin, 1984), attendance (Kahle, Kambara, & Rose, 1996; Pan, Gabert, McGaugh, & Branvold, 1997), written media consumption (Shamir & Ruskin, 1984), and spectatorship (Milne & McDonald, 1999). However, Funk, Mahony, and Ridinger (2002) found no significant relationship with level of support of the team. Nostalgia Nostalgia refers to the perceived benefit that the individual has due to fond or idealized memories associated with sports events that took place in the past (Holt, 1995). This can be a desire of fans and spectators alike. Fans may feel nostalgic about the good old days when the team was winning national championships or world championships consistently; fans may feel nostalgic for an old ballpark. This was one of the reasons that so many new baseball ballparks in the 1990s were designed to be retro, for example Camden Yards in Baltimore. Spectators can perceive the product benefits of nostalgia also. That is why leagues and teams celebrate and advertise historical achievements and events. Gladden and Funk (2001) found that nostalgia as a product benefit was associated with being a fan of the team and being loyal to the team. Ross et al. (2008) didn’t include it in their analysis, but they did have a construct called History, which dealt with past success of the team. It is not the same as nostalgia, but it is similar, and both Ross et al. and Biscaia et al. (2013) found Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints that it was related to being a fan. Biscaia et al. also found that it was related to satisfaction and behavioral intentions, at least to some extent. Kunkel et al. (2017) determined that nostalgia was the most important predictor of the team brand and of game attendance, TV consumption, and merchandise consumption. Diversion Some people perceive that a benefit of watching or attending games is because the games provide a diversion from everyday life. Previously we (Trail and colleagues) have listed this as the motive of escape, but really it is a perceived benefit of consumer sports. From a brand association perspective, Kunkle et al. (2017) call it diversion rather than Gladden and Funk’s (2001) original term of escape. Regardless, the ideas are similar because if the individual is a fan of the sport or just a spectator (or anywhere in between), the individual has the opportunity to focus on the game and not any problems that may be causing angst in his or her everyday life. Diversion (escape) has been shown to be either directly (Fink et al., 2002; Gladden & Funk, 2001; Robinson & Trail, 2005; Wann, 1995; Wann, Schrader, & Wilson, 1999) or indirectly related to team identification and attendance (Wann, 1995) and fan behaviors (Milne & McDonald, 1999; Trail & James, 2001). The exception seems to be Kunkle et al. (2017), where the relationship between diversion and game attendance or TV consumption is negligible (less than 2% of the variance in each). Despite Kunkle at al.’s statistically meaningless findings, marketers do need to take diversion as a product benefit into account. Both fans and spectators do watch and go to games because they perceive that it is a benefit. In addition, it is an easily marketable concept and is not overly contingent on the success of the team. Information Provision Information Provision is a benefit gained by the consumer through the sport organization providing information about a sport, team, or players, etc., through some type of medium, through attendance, or indirectly through word of mouth (WOM). Historically this has been called acquisition of knowledge and has been evaluated as a motive (Trail, Anderson, & Fink, 2000; Wann & Branscombe, 1995). However, although it may be motivating, it is definitely a benefit of interacting with the sport organization in a direct or indirect way. Obviously, the sport organization needs to provide their consumers with information to Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints develop relationships and move them along the consumer pathway. Without information from the organization, a consumer can never become aware of the team, and ultimately become a fan of the team. Not surprisingly, this concept has been associated with media consumption (Gantz & Wenner, 1991), ticket purchasing (Madrigal, 2006), spectatorship (Milne & McDonald, 1999), and being a fan (Fink, Trail, & Anderson, 2002; Robinson & Trail, 2005; Wann & Branscombe, 1995). Reanalyzing the college women’s basketball data showed that all of the groups were almost exactly the same on this concept. Regardless if they had attended during the current year or had never attended, they all were aware of the team and the sport, indicating that one way or another, for this sample, the athletic department had at least done a sufficient job making people at least aware of the team. Unfortunately, they hadn’t been able to do more than that because approximately 60% of the sample still had no interest in the team. Athlete Attractiveness Athlete attractiveness, as a product benefit, refers to the physical attractiveness of the participants playing the sport. For some people, if they perceive that the athletes are sexually appealing they may be more likely to watch the team or game (Duncan & Brummett, 1989; Guttman, 1986; Hofacre, 1992). Guttman noted that historically people have recognized and celebrated the sensual appeal of athletes competing in sporting events and Madrigal (2006) noted that modern fans frequently engage in a form of voyeurism while consuming sport. Sport spectators and fans alike may perceive that certain sports and/or teams are more sensually appealing than others. Some heterosexual males think that women’s beach volleyball and women’s golf have this product benefit and not that long ago the director (male) of the LPGA tour was actively marketing the sex appeal of the female golfers. There has also been anecdotal evidence that gay men watch both wrestling and football because of the sex appeal of the athletes. Women have been noted to watch football for the same reason. Trail et al. (2000) suggested, and Trail and James (2001) found, that perceiving that certain players were sexually appealing was related to being a fan of a particular team. Madrigal (2006) found similar results, showing it was associated with ticket purchase intentions, going to games, and fan behavior in general. In addition, Trail and James also found that it was related to attendance. Popularity This category was originally call Peer Group Acceptance by Gladden and Funk (2001) and was defined as “the ability of a team to provide a vehicle which generates broad social approval when followed” (p. 73). Ross et al. (2008) has a similar concept called Commitment which has items like “The team has many loyal fans supporting them” (p. 329). Both of these constructs get at the underlying popularity of the team, so I changed the name. Interestingly, most research has not found that this construct is related to fandom or consumer behavior (Gladden & Funk; 2001; Kunkel et al., 2017; Ross et Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints al., 2008). The exception is Biscaia et al. (2013a) who found a small positive relationship between the popularity of the team and being a fan and an even smaller positive relationship with behavioral intentions. They did find a fairly substantial relationship with customer satisfaction though. Obviously, marketers want their team to be popular because that means that more people know about the team and like it. However, one would expect that if people perceived it as popular, more people would be fans, attend games and consume the product. For whatever reason, the research doesn’t show that to the extent that one would expect. More research needs to be done to figure this out. Pride in Place Gladden and Funk (2001) noted that pride in place was a benefit for some people and defined it as the ability of the team to provide a rallying point for civic pride. Kunkel et al. (2017) called the same concept Community Pride. I have typically called this concept as Community Attachment. Regardless,

pride in place, typically hasn’t predicted attendance behavior, or anything else, very well. Gladden and Funk found that it was not a significant predictor of loyalty and Kunkel et al. found that it did not predict a positive attitude toward a league brand. In a wide variety of data sets that I have collected, and that my students have collected, Community Attachment has not been correlated with any behavioral intention, attitude, loyalty, or behavior. The mean scores are typically fairly high in all data sets, indicating that people are attached to their community; that is, they have pride in place. However, it doesn’t predict anything the do, or feel about, the team. That said, marketers need to be aware that fans (and spectators) are typically very proud of where they live, so even though it does not predict whether they go to games or not, it doesn’t hurt to note the connection that the team has to the community. Supporting a Cause Supporting a cause is perceived as a benefit because the individual believes that supporting the team will further a cause beyond the financial or competitive success of the entity. Originally, Armstrong (1999) developed this idea specific to supporting professional women’s sports and indicated that some individuals went to women’s professional basketball games because they believed in supporting sports opportunities for women. However, Funk et al. (2002) extended it to other specific sports and I have also applied it to women’s college sport. Although all the applications that I am aware of focus on Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints women’s sport, it certainly does not have to be limited to that arena. In many men’s non-revenue sports in intercollegiate athletics in the U.S., support for those teams may be cause-related, as some people feel that those teams are being unfairly cut when athletic departments are concerned about budgets. In some of my work I have found that supporting a cause was positively related to being a fan of the team and to supporting women’s intercollegiate basketball. Players as Role Models Despite Charles Barkley’s claim that athletes are not role models for kids, many people think they are and this can be a highly rated potential benefit of watching sport. Many people believe, especially in women’s sports, that the players provide inspiration for kids and should be emulated by them. Although the ratings of importance were high for this benefit in our reanalysis of the women’s college basketball data, it did not predict attendance directly. We did, however, find an indirect effect. We found that people who went to the games because of their attachment to the players were more likely to perceive that the players were role models. These results did not support previous research that showed a negative relationship between role model and spectator support level (Funk, Mahony, & Ridinger, 2002), nor did it support our results with the men’s basketball team from the Northwest. Organizational Constraints As I noted much earlier in the chapter, Organizational Constraints are ones that are specifically associated with the brand or product and they may need to be controlled for or fixed by the organization. Like most constraints, they may be the opposite end of the continuum from attributes or benefits, but sometimes are not perceived as negative attributes until they are examined more closely. Lack of Communications This construct is a compilation of the negative parts of several attributes and benefits. Lack of communication is defined as the organization not providing sufficient communication 1) to develop good relationships with their consumers, 2) to educate and inform their consumers or potential consumers about their product, and 3) to advertise or promote sufficiently well to create awareness and interest in the product. As far as I know, there has not been any research to examine this issue specifically. Marketers need to provide information so that they can accomplish all of the above things in a positive manner, so obviously, not providing sufficient communication will have deleterious effects for moving people up the consumer pathway. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Price (too high) High prices are probably one of most challenging potential constraints sport consumers face. The price of tickets has outpaced inflation considerably as we pointed out in Chapter 1. The overall financial outlay for attendance has also increased dramatically. Overall price includes the ticket price, concessions, parking, programs, merchandise, travel expenses, and other miscellaneous expenses. The impact of economic variables on attendance has been well documented. For example, ticket prices have been shown to have a negative relationship with attendance (Fink, Trail, & Anderson, 2002; Hansen & Gauthier, 1989; Mauricio & Armstrong, 2004; Zhang et al., 1997). Typically, as the price goes up, more people perceive it as a constraint. The attendees of the women’s basketball games actually reported the price of attending was not a constraint at all; in fact, it tended toward being a motivator because of the low price of a ticket and free parking. Although there were differences between the attendees and the other two groups, the latter still did not view this as a potential constraint. Although high prices typically are constraints, sometimes if the price of the ticket is too low, that might be perceived as a constraint as well. The Cincinnati Reds figured this out the hard way during the last year in their old ballpark. Attendance was down because the team was not doing well, and the new stadium was yet to be finished. The Reds decided that they would try to get people in the stands and at least earn some parking and concession revenue. The team decided to price the outfield bleacher seats at $1. The tactic backfired. People perceived that the Reds must be really bad and desperate if tickets could be purchased for only one dollar. The results were that no one bought the dollar tickets and attendance dropped further. The difficult economic environment during 2008-2010 resulted in many teams dropping ticket prices in order to maintain attendance levels. Spectators were appreciative as many families and individuals were dealing with lower income and higher prices. However, some people still perceived the prices to be prohibitive and definitely viewed them as potential constraints. Thus, marketers and managers need to be very aware about how attendance costs impact fans and spectators or do a better job of showing added value or creating added value if they maintain ticket price levels. Ticket Scarcity Implicitly, ticket scarcity (i.e., tickets not being available) makes sense as a constraint. If no ticket is available then obviously the person wishing to attend can’t, ergo, scarcity prevents some people from attending. However, it is not quite that simple. First, it is very rare that absolutely no tickets are available. Even during the NFL Super Bowl, tickets are available on the secondary market. It just happens that typically those tickets are extremely expensive, which precludes many people from purchasing them. So, for our purposes, scarcity applies when the team no longer has tickets available to be sold, Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints consistently across the season. For example, the Boston Red Sox had an 820-game streak where their games were sold out. This streak is the longest in major league baseball, and any other league as far as I know. This is mainly due to the number of games in a baseball season compared to most other leagues. For example, the Green Bay Packers would have to sell out for over 100 years to eclipse that mark. They hold the longest streak in the NFL, having sold out since 1959, but are not even halfway there. Scarcity exists in both places, among lots of others. In addition, scarcity is impacted by the level of team fandom and the cost of the tickets. Wann, Bayens, and Driver (2004) found that people who were not fans, were willing to pay more than twice as much for tickets when they thought they tickets were scarce, than when they thought tickets were easy to get. However, fans of the team were willing to pay 33% more than non-fans, even when they thought that tickets were not scarce, and non-fans thought they were scarce. When fans thought tickets were scarce, then they were willing to pay almost double what non-fans were willing to pay. Many NFL stadiums, as I noted in Chapter 1, are almost at capacity, so tickets may seem scarce to many fans. Major League Baseball, which averages around 60% capacity as a league, has consistently built smaller stadiums recently, to create demand and perceived scarcity. As scarcity increases ticket prices go up, thus, marketers and managers want to market scarcity if possible. The Seattle Storm understand that as I received several emails over the last week imploring me to buy tickets before there were no tickets left (in the lower bowl). Didn’t work. I didn’t buy any 😊. Poor Player/Employee Behavior Another potential constraint is poor behavior of the players both on and off the court/field. This construct also focuses on the specific personality of the players and how they interact with the community. Data from the college women’s basketball team indicated that all three groups thought that this was important, and those that attended the games felt player behavior was more important than the other two groups. Perceptions of negative player behavior decreased intentions to attend. However, in the Kim and Trail (2010) analysis, player behavior did not predict attendance once the other variables were included in the model. However, Tiger Woods experienced a dramatic drop in his approval rating (Qscore) due to his infidelity back in 2009. He lost sponsors, as well as fans due to his actions. More recently, many players’ and management level employees’ sexual harassment behavior and abusive behavior have turned off many fans. The #MeToo movement has finally given more traction to illuminating all of the sexual harassment behavior that is prevalent, not only in sports, but pretty much everywhere. Unfortunately, there still is a segment of fans that doesn’t really care about bad behavior of players or employees as long as they help the team win. Managers/coaches/owners should not tolerate poor behavior by players and there should be repercussions for such. Ownership should not tolerate it in their employees and the leagues and society should not tolerate it in owners. Recently, public backlash and pressure from the NFL forced the Carolina Panthers’ owner Jerry Richardson to sell the team. However, there doesn’t seem to be many consequences for the Seattle Mariners’ executives. Marketers need to have plans in place when instances of poor behavior take place and be able to communicate the stance of the organization to the public. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints Lack of Access Grady and James (2013) noted that sport organizations need not only to provide a “physically accessible environment, including ramps and accessible restrooms, but also an accessible sport service environment, one that is inviting and accommodating to people with disabilities” (p. 49). Although Lack of Access could refer to many possible things, I’m focusing on making attending a sporting event accessible to all. Grady and James identified physical needs such as: seating, companion seats, parking, line of sight, restrooms, elevators/ramps, signage, and heat-related protection. They also found that people with disabilities had service needs such as: staff responsiveness, staff awareness, and staff knowledge. Finally, there were also blended needs where both physical (venue related aspects) and service (staff related aspects) came into play, and could be potential constraints: crowds, accidents, emergency evacuation, accessible policies, and role of companion. All of these aspects, or any of these aspects, could easily be a constraint for a person with a disability (and for those who don’t have disabilities as well, in some cases) and prevent attendance. Some things that were not identified would be communications to those who might be visually impaired or auditorily impaired. Webpages need to be created so that those with visual impairments still get the same experience. There are many other aspects, but the above listed ones are a few. Regardless, managers and marketers need to deal with these aspects to make the game enjoyable and welcoming for all. Managers need to deal with solving the issues and marketers need to make sure that all communications are clear and accessible, in addition to creating feelings of inclusivity. Losing Team A constraint that is well known both anecdotally and empirically is losing. If a team has a losing record, attendance is negatively impacted. Denaux and Denaux (2011) found that for MLB teams that had a record below .500 (losing more games than they won), attendance decreased by 4175 people per game on average, all else accounted for. At the minor league baseball level in the U.S., Gitter and Rhoads (2010) found that an increase in losing by 10% equated to a 1.9% decrease in attendance at the A level and a 2.3% decrease at the AA level. However, losing had no effect on attendance at the AAA level at all. Cebula (2013) found similar results with his minor league baseball data, but a 10% decrease in winning percentage equated to a 4.1% decrease in attendance. Furthermore, Anthony et al. (2014) showed that losing percentage had a negative impact in two minor leagues, but not a third and Paul and Weinbach (2011) found losing predicted a decrease in attendance in minor league hockey in Canada. Most of this information is not surprising; however, what is surprising to me at least, is that losing doesn’t have a greater negative impact on attendance. Poor Ticketing Processes Poor ticketing practices can prevent some people from attending games, or at least prevent them from repatronage. Poor service from ticket service/sales personnel is an obvious issue, but most people buy their tickets online now, have the tickets sent to their mobile phone, and then the ticket ‘takers’ at the venue scan their phone. However, there are many ways that process can go wrong. Perhaps the e-ticket is lost. Maybe the person can’t pull up the e-ticket on their phone. Maybe the scanner can’t read the screen on the phone, etc. Recently, the Minnesota Vikings (among others) totally did away with paper tickets. You must use digital tickets and if you don’t want to, then you have to pay $35 extra (e.g., Seattle Seahawks). For some teams, their ticket selection process online is very poor. How many webpages must I click before I can actually buy a ticket, and then it ends up not being available because the team didn’t update the system or something. In most cases all of these things aren’t an issue for most fans; however, for some they could be constraints that prevent people from attending a game because ticketing is too much of a hassle. Managers need to make sure that everyone can easily purchase tickets, Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints receive the tickets, easily produce the tickets at the venue, and the entry process is quick and worryfree. Games being Televised/Streamed Many leagues used to have black-out rules where if the team did not sell out a home game then the game could not be televised in the home geographical area. The assumption was that if the game was televised, people would stay home instead of going to the game. This was definitely the case with the collegiate women’s basketball sample: if the game was televised, people that typically would go to a game would stay home instead and watch it on TV. The same situation was apparent with the professional women’s basketball team, but even more so. Thus, the leagues are correct; televising the games will decrease attendance. However, team executives need to assess whether more revenue can be gained through television revenue than can be gained by having people in the seats. In general, people in the seats are more valuable for less popular sports which typically don’t have good TV contracts. Games Being on the Radio Although there has always been concern that televising the games might decrease attendance, the influence of having the game on the radio has not been raised. In Major League Baseball, many fans take a radio to the game so that they can listen to the analysis and play by play at the game. However, apparently if the game is on the radio it can have a negative effect on attendance similar to televising the game, albeit not as much. The fans of the professional women’s basketball team indicated that if the game was on the radio they were less likely to attend games in the future. The accuracy of this finding is debatable as these same fans indicated that the game being on the radio had not influenced their attendance so far that season. Even if this research was replicated across multiple sports the impact is probably minimal. The collegiate women’s basketball fans did not indicate that the game being on the radio was a very large negative influence. Copyright 2018 Galen Trail

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Brand Associations & Organizational Constraints So, let’s put all of this together with our Renton RoadRunners example. In Table 6.1 I have listed all of the brand associations that we’ve discussed. In addition, I have included the marketers’ perceptions of each, and where data was available, consumers’ perceptions of each. In some categories there are no perceptions for either because it was not collected. Table 6.1 – Product Attributes, Benefits, and Constraints Brand Associations Attributes Product Related Team Success

Star Player

Quality Management/ Front Office Quality Head Coach

Aesthetic Play Aggressive Play Dramatic/Style of Play

Athlete Skill Team Personality Competition (Rivalry)

Non-product Related Price (Game, Concessions, Merchandise) Brand Mark/Logo

Venue Aesthetics Venue Location Cleanliness of the Venue Quality of Concessions Concession service Parking


Marketer perception: Very successful last year, made playoffs; new coach and some new players so don’t know for this year, but high hopes Consumer perception: 82% thought the team was successful Marketer perception: Had one last year, but he was called up to NBA; don’t really think we have one this year Consumer perception: 90% don’t think there is a star player this coming year. Marketer perception: We’re great! Consumer perception: Don’t have a feeling one way or the other on this one. Marketer perception: Last year good coach, successful, but boring; new coach this year, have high hopes, but don’t know yet, should be more exciting play on the court Consumer perception: 30% have concerns that they got rid of a good coach. 25% pleased with new head coach hire. 45% don’t know. Marketer perception: Horrible last year, should be better this year Consumer perception: 55% thought that the RoadRunners played an aesthetic game. Marketer perception: Should be much better this year under new coach Consumer perception: 37% thought the RoadRunners played aggressively last year. Marketer perception: Last year stunk, this year should be much better Consumer perception: 56% thought that the games were dramatic and exciting, but 60% thought the play was too slow. Marketer perception: Good skills, but new players this year so tough to tell Consumer perception: Only 62% thought players had good skills (averaged) Marketer perception: Don’t know Consumer perception: 48% thought they were hard workers Marketer perception: Competition is good overall, but no strong rivalries Consumer perception: No data

Marketer perception: Great prices Consumer perception: 64% thought that the price of season tickets was affordable 81% thought the price of single game tickets was affordable Marketer perception: Great logo Consumer perception: Only 58% thought the logo was good. Comments by those who didn’t like it indicated that it was too cartoonish. Marketer perception: Poor, looks like a warehouse inside and out. Consumer perception: 43% liked the venue aesthetics Marketer perception: Easy access, good location, Consumer perception: 80% liked the location Marketer perception: Venue is relatively clean Consumer perception: 64% thought the venue was sufficiently clean Marketer perception: Need to improve quality Consumer perception: 42% thought the concessions were of good quality Marketer perception: Good service Consumer perception: Only 48% thought concession service was good. Marketer perception: Free, what can be better? Consumer perception: 76% were satisfied with parking, those that weren’t didn’t like having to wait for trains after leaving the game.

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Brand Associations & Organizational Constraints Employee service Seating Relationship Building Promotions

Advertising

Benefits Social Interaction Opportunities Nostalgia (Team History/ Tradition) Diversion

Information Provision

Athlete Attractiveness Popularity (Commitment)

Pride in Place

Supporting a Cause Players as Role Models

Constraints Lack of communication

Price (too high)

Ticket Scarcity


Marketer perception: Great service Consumer perception: 56% were satisfied with the service by personnel at the venue Marketer perception: Not bad, but can’t do anything about it Consumer perception: 68% were satisfied with seating at the venue Marketer perception: Great relationships with our fans Consumer perception: No data Marketer perception: Need to do lots more promotions of all kinds Consumer perception: 74% said that giveaways during the game positively impacted their attendance 48% said pre-game events positively impacted attendance 45% said post-game events positively impacted attendance Correlations showed that no promotions had a positive impact on attending multiple games. Marketer perception: Need to do more social media Consumer perception: 20% said Twitter ads had at least some impact 46% said Facebook ads had some impact 42% said the Mobile App had an impact Fewer than 8% said Pinterest had an impact 46% said TV ads had an impact 24% said newspaper ads had an impact Correlations showed no impact on multiple game attendance

Marketer perception: Great opportunities in clubs and seats Consumer perception: 86% thought that the games provided opportunities to interact socially Marketer perception: None – team not that old Consumer perception: No data Marketer perception: The game provides a great entertainment experience. Consumer perception: 80% thought that attending the games provided a good diversion from everyday life Marketer perception: We do more than enough communications Consumer perception: 79% thought that the organization provided sufficient and valuable information Marketer perception: Not interested in marketing this Consumer perception: No data. Marketer perception: Not very popular in the community Consumer perception: 83% of those in the database like the sport; 46% like the league; 88% like the team Marketer perception: There is a lot of pride in Renton because of the RoadRunners because we are the only professional team in the city. Consumer perception: No data Marketer perception: Not applicable Consumer perception: No data Marketer perception: Good role models Consumer perception: 93% thought that the athletes were good role models

Marketer perception: None Consumer perception: 22% thought that the team did not provide sufficient information, primarily due to a poor website (based on comments). Marketer perception: Good prices Consumer perception: 20% thought that ticket prices were too high 72% thought the concessions prices were too high Marketer perception: Not a problem Consumer perception: 92% thought tickets were always available when they wanted them.

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Brand Associations & Organizational Constraints Poor Player/Employee Behavior Lack of Access Losing Team Poor Ticketing Processes

Game on TV/Streamed

Losing Team

Marketer perception: Haven’t had any problems for several years now. Consumer perception: Only 7% thought that there were player issues. None noted for employees. Marketer perception: We are ADA compliant Consumer perception: No data Marketer perception: We haven’t had a losing season since Year 1 Consumer perception: Only 3% thought the team was a losing team. Marketer perception: Very few complaints about tickets. Old school though. All paper. Consumer perception: 2% indicated that they hadn’t been satisfied with getting their tickets. Marketer perception: We haven’t ever been televised. Thinking about streaming next year. Consumer perception: N/A Marketer perception: We haven’t ever been on the radio Consumer perception: N/A.

Summary In sum, marketers need to have done their market research so that they know the positive impact that all of the brand associations (product attributes, non-product related attributes, and benefits) may have on fans and spectators. In addition, they also need to know the potential negative impacts that constraints might have. As shown in the example, sometimes the marketers’ perceptions are pretty accurate, and other times not at all. This is why market research is so important. Once marketers have identified all of the aspects that impact their specific team, then they can start to develop the marketing plan and communication campaigns that take all of this information into account. Even if marketers have all of this information, and it would be a great start, it still is not sufficient. Marketers also need to understand the customer environment much better than they typically do. So, on to the next chapter!


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Personal Needs and Values


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Chapter 7 Personal Needs and Values

Many cause-related marketing schemes are based on marketers’ beliefs that sport fans are benevolent (a value) and are willing to donate to worthy causes. For example, every year the Portland Sea Dogs (what is a Sea Dog anyway?), a minor league baseball team, teams up with the Rotary District in Portland, Maine, to fight homelessness. During a four-year period, fans of the Sea Dogs donated more than $50,000. These donations were used to fund food pantries, homeless shelters, soup kitchens, and shelters for battered women and children. Another example is Major League Baseball and its member clubs teaming up with fans to raise money for the Susan G. Komen for the Cure foundation. Since 2004, fans, teams, and MLB have contributed over US$1.4M. A final example; the Kansas City Chiefs hold a fundraiser that is called Red Friday which happens before every home opener. Chiefs’ fans are supposed to ‘paint’ the town red by wearing Chiefs’ paraphernalia and by donating to several local causes. In 2016 they raised over $250,000 in one day for the Ronald McDonald House Charities of Kansas City. Teams also use promotions to try and tie in to needs and values that their fans may have. For example, a while back, the Oakland Raiders teamed up with the California State Parks and the California State Parks Foundation to promote family fitness in a natural environment. Through this promotion, the Raiders are hoping to attract people who have an interest in fitness and the environment to come to their games and the Parks department hopes to encourage city-based families to get out to the state parks and hike or bike to get fit. The Raiders participate in the State Park Foundation’s annual Earth Day event and the State Parks display information in the stadium and on the Raiders web site. Copyright 2018 Galen Trail

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In all of the above examples, teams and leagues are trying to connect with people through their needs and values. The teams are hoping that these connections will bloom and increase sport consumer behavior. Overview of the chapter As we noted in the previous chapters, the external environment and the internal organizational environment impact the customer environment and the consumer pathway (Figure 7.1). In this section of the book, we are starting to focus on the customer environment and the insights Figure 7.1

that marketers can glean from understanding how internal motivation, constraints, satisfaction, and advocacy can affect people on the consumer pathway (Figure 7.2, below). in this chapter, we will focus solely on the motivational aspects of personal needs and values, and how each of them individually, and in concert, can influence behavior and movement along the consumer pathway. In the last chapter, we listed the product attributes and product benefits that might attract people to come to a game or be a fan of the team. Those attributes and benefits can be perceptions that people have about the game/event/team or whatever. However, just because a person perceives that a product has a certain attribute, it doesn’t necessarily mean that the product is appealing or of interest to that person. Let me give you a personal example. I recognize that Tom Brady, the quarterback for the New England Patriots (NFL), is a star player (he was named NFL MVP for the 2017 season) and will Copyright 2018 Galen Trail

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probably be in the NFL Hall of Fame. He has really great skills and I can appreciate the aesthetics of his play and the play of his team. However, I don’t like him, I can’t stand the Patriots, I don’t like their Figure 7.2

coach, and I typically don’t watch their games unless they are playing a team I like or are in the Super Bowl (and get beat! Yay Eagles!). So, even though I can see all of these attributes of the Patriots and acknowledge them, they don’t motivate me to watch Patriot games or be a fan. Thus, this means that there has to be something else that prevents me from watching their games and be a fan of the Patriots. Something that is more important than those attributes. One possibility is a combination of needs and values. However, needs and values, by themselves are not sufficient either. They require a fulfillment agent (i.e., the team or fandom in this case). Here’s another example. If one of my values is competitiveness, and it is a guiding principle in my life, then most of the things I do in my life will have something to do with being competitive. Regarding sport, what this probably means is that I’m only interested in competitive teams and competitive games. If I don’t perceive that a game is going to be competitive (dramatic) or if I don’t perceive that a team is going to be successful, then the game or team won’t fulfill that value for me and I won’t be interested in watching the game or following that team. The argument I’m making is that the person’s perceptions of the product attributes and benefits need to be paired with the person’s unfulfilled needs or values. If the person doesn’t have both, most likely, that person will not watch the game or follow the team. In this chapter, we will examine the needs and values that might be applicable to the various product attributes and benefits that we have already covered, and hopefully show relationships between the external product attributes/benefits and the internal motives (needs/values). Copyright 2018 Galen Trail

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Personal Needs A need has been defined as a "deficit state of the organism that recurs periodically and that has a specific requirement for its satisfaction" (Gordon, 1975, p.8). Gordon suggested that needs are divided into two categories: those that are psychologically based and those that are physiologically based. Physiological needs consist of the needs for food, shelter, sex, etc., and psychological needs consist of the needs for coordinated social interaction and group welfare and survival. Abraham Maslow is probably considered the father of ‘needs-based’ research; he identified five general categories of basic needs: physiological, safety, love, esteem, and self-actualization (see Figure 7.3 below; Maslow, 1943). Maslow proposed the existence of a needs-based hierarchy. Within the hierarchy, lower level needs must be satisfied first; as lower level needs are fulfilled, an Figure 7.3 Selfactualization

Esteem

Belongingness

Safety

Physiological

• Maximizing one’s potential

• Respect

• Acceptance & affection

• Nurturance & money

• Food, water, & oxygen

individual then proceeds to focus on or attend to higher level needs. However, he also noted that the lower level need does not have to be 100% satisfied before an individual ‘moves on’ to satisfy a higherlevel need. For example, physiological needs could be satisfied 90% of the time, safety needs satisfied 75% of the time, love needs 50% of the time, esteem needs 35% and self-actualization only 10% of the time. As lower-level needs gradually become more satisfied, the next level need(s) emerges. Furthermore, in extreme circumstances, lower level needs may not be satisfied at all, but higher-level needs will be. However, these situations are not typical. Maslow also suggested that individuals are not necessarily consciously aware of most of these basic needs. For example, typically most people are not mentally appraising their esteem needs. An individual does not think “Hmm, I’m feeling a little low on self-esteem today. I think I’ll go find something that will improve how I feel about myself.” However, sometimes people will unconsciously perform a certain act that will improve their esteem in the eyes of others or themselves, without really understanding the reasoning behind why they acted that way. For Copyright 2018 Galen Trail

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example, why are certain girls ‘Jersey Chasers’? These girls are groupies who like to follow male athletes around to gain attention and achieve vicariously. For some reason, by associating with the athletes, they feel better about themselves. Similarly, why do some guys feel the need to always ‘one-up’ everyone else? Someone will relate a story and the ‘one-up’ guy has to always out-do that, whether it is a bigger fish, a sportier car, more points in a middle school basketball game, or whatever. This seems to make them feel better about themselves. Most behaviors are motivated by more than one need, if not many needs. Thus, a behavior could be motivated by esteem, belongingness, and physiological needs, all at the same time. For example, Joe Fan might take his friends to the game because he feels better when he is part of the incrowd (belongingness). He also may think that by doing this his friends will respect him more (esteem). A third need that might be satisfied is hunger (physiological) because it is ‘Dollar Dog’ night. Based on Maslow’s (1943) hierarchy, I have identified some needs that I felt might be related to fan behavior. I incorporated them into Maslow’s hierarchy and compared sport fans to non-sport fans (see Figure 7.4). Figure 7.4


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SELF-ACTUALIZATION NEEDS Need for Inner Peace. The need for inner peace is a self-actualization need that focuses on being at peace with one’s self and one’s life. It is also needing the feeling of being free of conflict within one’s self and needing the satisfaction of a life well-lived. This latter part is why it fits within the selfactualization category because it gets at maximizing one’s potential. It is closely related to Wisdom and Personal Growth needs. It is also correlated with Openness and Benevolence values among others. Specific to sport, sport fans and non-fans did not differ significantly on Inner Peace needs (Figure 7.4). In other words, there was no correlation between Inner Peace and being a sport fan in a national sample of individuals. I have no evidence yet that it is connected to any of the product attributes or benefits related to being a fan of a particular team or to attending/watching sporting events. However, we have found that communications to donors that focus on inner peace will indirectly increase donations. Obviously, this need still needs further investigation. Need for Personal Growth. The need for personal growth is defined as the need to always be developing emotionally, intellectually, and spiritually, in order to become a better person. This need fits well into the self-actualization category and maximizing one’s potential. It is related to Wisdom, Curiosity, and Inner Peace needs, as noted above. It is also related to the values of Work Ethic, Openness, Benevolence, and Pragmatism, among others. Sport fans and non-fans did not differ on personal growth needs at all (Figure 7.4). We have found it to be associated with product attributes (drama) and benefits (supporting a cause), but not directly with being a fan, or attending a game, with one exception, and that is receiving communications from the team, but it was very lowly correlated. In our donor data set, we did find that people who were high in personal growth needs wanted to be kept informed and up-to-date about what the organization was doing. This makes sense in light of the correlation with the email communications that fans looked slightly favorably on in the sport-fan data set. This need, relative to sport, should have more investigation focusing on the information provision product benefit by the team. Need for Aesthetics. This was defined as the need for beauty (Maslow, 1954) and I have extended it here, defining it as the need for natural beauty inherent in the world or the aesthetical aspects of life. Selmer and Littrell (2010) noted that Maslow and Lowery (1998) placed aesthetic within the self-actualization category. I have found that it is more similar to the other needs in the selfactualization category than those in other categories. Lee and Trail (2007) found that the need for aesthetics seem to be somewhat antithetical to values of patriotism and spiritualism but related to values of tolerance and pragmatism. In addition, in Lee and Trail found it was related to environmentalism, which they called a goal. My recent U.S. national-data supported some of Trail and Lee’s findings. I found that aesthetic needs were relatively highly correlated with environmentalism values, which makes sense. Those who have a need for natural beauty would be more likely to want to protect the environment. We know that people who have aesthetic needs are typically those who enjoy scenic views in the country, a hike into the mountains, naturalistic paintings or sculptures, etc. Specific to sport, Lee and Trail (2007) found aesthetics had a slightly negative relationship with watching televised sport. In other words, someone who feels that aesthetics is an important need in their life would be less likely to watch sports on television. Lee and Trail also found that aesthetics was not significantly related to being a sport fan, a fan of a specific team, attending games, buying sportlicensed merchandise, listening to the radio, or consumption of sport on the internet. The recent national research I just did also found that the need for aesthetics was very slightly negatively related to being a sport fan, and as Figure 7.4 shows, non-fans scored higher on aesthetic needs than sport fans Copyright 2018 Galen Trail

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did. Similarly, in a sample of Americans and Taiwanese, Gau (2007) also found that aesthetics was negatively associated with watching sports on television. The negative association, however, was primarily among females. However, unlike Lee and Trail’s (2007) research, Gau’s research showed that aesthetics was also negatively related to attending sport events, reading about sports in newspapers or magazines, and talking to others about sports. For aesthetics to have a negative relationship with fan attitudes and behaviors makes sense to some extent. If people who have high aesthetic values are more likely to enjoy the outdoors, communing with nature, or perhaps going to art galleries and/or museums to enjoy paintings or sculptures, then these activities may be choices preferred over sport. For example, a person that has limited time for entertainment would be more inclined to choose going to the opera than attending a basketball game. However, in some recent research with fans of a minor league hockey team, we found that aesthetics was lowly, but positively, correlated with BIRGing (Basking in Reflected Glory), intention to buy tickets, and being a fan of the team. Not surprisingly, the need for aesthetics was also related to the perception of the hockey games being aesthetically pleasing and acquiring knowledge about the team. Thus, marketers need to keep this need in mind. Need for Wisdom. Although Rokeach (Rokeach & Ball-Rokeach, 1989) suggested that wisdom is a value, I am following Maslow’s (1954) lead here and placing wisdom in the needs area instead because he places it within the cognitive sub-dimension of self-actualization, which Selmer and Littrell (2010) note consists of knowing and understanding. This makes sense to me because becoming wise certainly is part of maximizing one’s potential. I’m defining the need for wisdom as needing to accumulate a mature understanding of life through experience and study (a compilation of Rokeach’s definition and components of Maslow). In addition, I have found that the need for wisdom is associated with the other self-actualization needs such as curiosity, and personal growth, as noted above. It is also correlated with the values of work ethic and pragmatism, which make sense to some extent. To become wise, one certainly needs to work at it through study or through a cognitive appraisal of experiences. In addition, wisdom is typically a pragmatic approach to knowledge. The need for wisdom does not seem to be related very much to product attributes or benefits within the sport world. Some students of mine working with a professional women’s soccer team (Nicefaro & Goobes, 2017), found that it was very lowly correlated with supporting a cause (product benefit) and also very lowly correlated with perceiving that the games were drama-filled (product attribute). In addition, need for wisdom was not associated with attending games, level of interest in the team or team identification. In the national data set, sport fans and non-fans did not significantly differ on need for wisdom. This makes sense, why would wisdom be associated with being a fan or not, although one might question how wise it is for some people to support certain teams at all if there are no redeeming qualities relative to the team or organization. Copyright 2018 Galen Trail

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Need for Curiosity. Curiosity is the need to explore, garner new knowledge, and learn new and interesting things. This need is a part of the cognitive needs listed by Maslow (1954) and Selmer and Littrell (2010) and fits well within the self-actualization category. I have found that it is related to the other self-actualization needs of personal growth, aesthetics, and wisdom. Not surprisingly, it is also related to the values of work ethic and tolerance. Interestingly, non-fans are more curious than sport fans (Figure 7.4). In one data set that we have about a minor league hockey team, the need for curiosity was not related to being a fan of the team, sport, or league, and also not related to any team consumption behaviors, but it was related to future behavior intentions slightly. Curiosity was related to the product benefit of information provision and two product attributes (drama and aesthetic play). The first was expected, but the latter two were not. It makes sense that if people had a need to have their curiosity fulfilled, and they perceived that the sport organization provided information about the team, then that need would be met. Need for Stimulation. Need for stimulation was defined as the need for variety and stimulation. People who are high in this need are daring, courageous, and adventuresome. They need challenges and will easily take risks. I put this construct in the self-actualization level because I felt that for many people to maximize their potential, they need stimulation and variety. They need to be challenged. Rokeach (1979) lists “an exciting life – stimulating active life” as a terminal value and Schwartz and Bilsky(1987) also lists it as a value, but it is more of a need in my view. Lee and Trail (2007) found that the need for stimulation was related to the achievement, power, and physical well-being needs. In the recent collection of national data need for stimulation was associated with the other self-actualization constructs, but not as closely as the others were to each other. Need for stimulation was also related to the values of competition and hedonism. Related to sports, Lee and Trail (2007) found that stimulation needs were predictive of being a sport fan or being a fan of a specific team, but not of any particular sport behavior. Ko, Chang, Jang, Sagas, and Spengler (2017) found that need for arousal (similar to stimulation) was correlated with spectating involvement and spectating intention. In the hockey team research stimulation was correlated with BIRGing to a small extent (r = .236) and even less with intending to go to more games or be a fan of the team. In the U.S. national study and shown in Figure 7.4, sport fans had higher stimulation needs that non-sport fans. Gau’s (2007) findings contradicted this in terms of his excitement value (the same as stimulation). He found that people who rated excitement as a value highly were more likely to attend sporting events than people who did not. He also found that as males rated excitement higher, they watched more sports on TV, but as females rated excitement higher, they read less print media about sports. However, both genders were likely to talk about sports more with others. In the women’s soccer data (Nicefaro & Goobes, 2017), we found that need for stimulation was related to the product attribute of aggressive play. This latter association makes some sense, but we were Copyright 2018 Galen Trail

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expecting it to have a higher relationship with drama than the very small one that it did. However, what we did find is that the interaction between need for stimulation and drama predicted level of interest in the team to some extent, but even more so, it predicted identification with the sport of women’s soccer. What this means is that people who had a high need for stimulation and perceived women’s soccer to have the product attribute of being dramatic, were more likely to have a higher level of interest in the sport than those that didn’t. ESTEEM NEEDS Need for Prosperity. I defined the need for prosperity as the desire to attain economic well-being; that is, the need for wealth, valuable things, and possessions. This is a redefinition of Lee and Trail’s (2007) goal of materialism, defined as the acquisition of worldly possessions and material well-being. People who have a high need for prosperity enjoy the process of getting things and then showing off their possessions. According to Roberts and Pirog (2004), “materialists tend to over-idealize wealth and possessions” (p. 64). This need fits well into Maslow’s (1954) esteem needs category that focuses on the need for respect. People who have a high need for prosperity think that material possessions and wealth will give them respect. Need for prosperity, not surprisingly, is related to power and achievement needs. It is antithetical to the self-actualization needs. This makes sense if the individual needs to obtain things just to obtain things, then that person is probably not too concerned with the selfdevelopment. Furthermore, if the focus is on objects and not on people, it makes sense that prosperity and belonginess needs may not be related. In addition, too much materialism probably has deleterious effects on an individual’s financial security. In addition, need for prosperity is highly correlated with the values of competitiveness and hedonism. Lee and Trail (2007) found that materialism (prosperity) did not predict any type of sport consumption behavior, nor did it predict whether or not people would be sport fans in general or fans of a specific team. This means that materialistic people were just as likely to attend games, watch sports on TV, etc., as non-materialistic people. In addition, non-materialistic people were just as likely to be fans as materialistic people. No differences existed. This was contradicted by the U.S. national study I did where I found that sport fans had much higher needs for prosperity than non-fans. However, in the women’s soccer data (Nicefaro & Goobes, 2017) we collected, there were no relationships between need for prosperity and any product attribute or benefit, or with any interest with or attachment to the team. Nor was it related to any consumption behavioral intentions. It seems likely that this need is unrelated to product attributes, benefits, attitudes or behaviors. Need for Achievement. I have adapted Lee and Trail (2007) modification of Schwartz’s (1992) definition of achievement and listed it as a need rather than a value. Historically it has almost always been referred to as a need, so I am moving back into this category. I am defining it as the need to accomplish personal success according to social standards. Lee and Trail suggested that people who had a need for achievement wanted to attain success, recognition, prestige, and personal status. These individuals also wanted to be able to have a good reputation. Achievement needs are associated most closely with prosperity needs and power needs. Achievement needs seem to be slightly more correlated Copyright 2018 Galen Trail

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with other needs such as stimulation needs, and social acceptance needs than prosperity and power needs are. They are also correlated with competitiveness and hedonism values. Specific to sport, Gau (2007) found several relationships between variables similar to the need for achievement and different sport behaviors. Preserving public image was related to watching sports in person and reading about sports in newspapers and magazines. Sense of accomplishment was associated with watching sports on television and spectator sport consumers rated it as more important than non-consumers. Success was lowly related to attending games, watching sports on TV, and talking about sports with other people. Again, sport consumers rated it as being more important than non-consumers of sport. This was supported in my collection of the U.S. national data in which I found that sport fans had higher need for achievement than non-fans did. Lee and Trail (2007) found that need for achievement did the best job of predicting most cognitive measures of sport fandom. Achievement predicted general sport fandom well (explaining about 16% of the variance – Figure 7.5). As need for achievement increased, so did the likelihood that the individual would be a sport fan. Achievement also predicted being a fan of VARIANCE a specific team (13% of the variance). It however did not predict sport consumption behaviors well. It was slightly Variance refers to the amount or related to sport merchandise consumption (2.5% of the percentage that one or more variables variance), but not at all related to game attendance, explain in another variable. For example, watching sport on television, reading print media about in Figure 7.5, Need for Achievement sport, listening to sport on the radio, or getting explains 16% of the variance inFigure sport 7.5 information about sport on the internet.

Variance in Sport Fandom 16% Need for Achievement Unknown 84%

Ko et al. (2017) showed that need for achievement was correlated with the importance of sport spectating (9%) and continuing to watch sports in the Copyright 2018 Galen Trail

fandom. What this is saying is that need for achievement will predict 16% of why people are fans. However, you will also note that 84% is unknown. The unknown portion could be other needs or could be product attributes or benefits, or all of the above, or anything else that might be the reason that people are fans. In addition, some of that unknown is also error. Error can be people not answering the question accurately on the survey, the item on the survey not being written well so people misunderstand it, or several other types of error.

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future (9%). In some hockey research I did, need for achievement did not predict any behaviors and was only lowly correlated with BIRGing (r = .213; 4% of the variance). Similarly, in the women’s soccer data (Nicefaro & Goobes, 2017), need for achievement was unrelated to any product attributes or benefits. It was also not related to any team-based attitudes or behaviors. We expected it to be related to the product attribute of team success. That is, those people who had a high need for achievement and perceived the team to have a high level of success, would have a more positive attitude and consume the product more. The latter was not the case at all, but those who were high in the need for achievement and perceived the team to be successful, were more likely to be fans of the team and of the coach. In addition, they were more likely to BIRG. However, all of these correlations were smaller than the product attribute of success by itself, that is, the interaction had less predictive ability than success by itself. Future research still needs to investigate the interaction effects. Need for Power. Based on Lee and Trail’s (2007) work, I have defined the need for power as the need to establish social control or dominance over people and resources. People who have this need want to be able to influence other people so that they can control the situation. This need is related to the other self-esteem needs: prosperity and achievement. Not surprisingly, it seems to be antithetical to inner peace among other needs. It is also correlated with the values of competition and hedonism. Specific to sport, Gau (2007) found that sport spectators liked to be seen as being influential (similar to the need for power) more than non-spectators. Gau also found that social power was negatively associated with attending games for females, but not for males. In other words, as women wanted an increase in social power, the less likely they were to attend games. For both men and women, as the need for social power increased the more likely they were to read about sports in the print media. In my U.S. national data sample, I found that sport fans were higher in the need for power than non-fans. Lee and Trail (2007), on the other hand, found no significant relationship between power and any type of sport consumption behavior or cognition (i.e., being a fan). BELONGINGNESS NEEDS Need for Family Togetherness. Need for family togetherness is the first of the four belonginess needs we will discuss and I have defined it as the need for a close-knit family; that is, a need for a family that enjoys each other’s company and cares for each other. It is associated with the other belonginess needs of companionship, mature love, and social acceptance. It is not associated with the needs of stimulation or power. It is associated with the values of commitment, openness, and benevolence. This makes sense as those values would help fulfill the need for family togetherness. Relative to sport, sport fans are slightly higher in this need than non-fans in the U.S. national survey. In the professional women’s soccer team data (Nicefaro & Goobes, 2017), the need for family togetherness was not associated with any sport consumption behaviors or attitudes. However, it was slightly associated with the product benefit of social interaction opportunities. This makes sense as the more that one perceives that attending a game provides social interaction opportunities, then the more likely family togetherness needs could be fulfilled if the family goes to the match. However, what was Copyright 2018 Galen Trail

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more interesting was that those who perceived that the players played aggressively (product attribute), seemed to feel that their needs for family togetherness would be met by going to a match. I’m not sure why this would be the case unless the whole family enjoys aggressive play. Obviously more work needs to be done in this area to be able to either replicate and explain these relationships or refute them. Need for Mature Love. I have defined mature love as the need for a romantic relationship of deep and lasting affection. This need can be met through monogamous relationships that are deeply emotional, loving, and intimate. This is based on Rokeach’s (1973) value of mature love. Need for mature love is associated with the other needs in Maslow’s (1954) belongingness category (family togetherness, companionship, and social acceptance – to some extent). However, it is also related to the need for personal safety. This might be due to feelings of being safe if one knows that there is someone looking out for you all of the time. Pure conjecture on my part though. Not surprisingly, it is highly correlated with the value of commitment, and somewhat correlated with openness and benevolence. Sport fans seem to have higher needs for mature love than non-fans (Figure 7.4), although I don’t know why that would be the case. Maybe it is because sport fans need to increase their selfesteem needs and can do it through unconditional love 😊. As far as I can tell there hasn’t been any other research done on the relationship between need for mature love and sport. There doesn’t seem to be any inherently obvious reason why there should be either. If you think of one or know of one, let me know! Need for Companionship. I have defined need for companionship as the need for establishing and maintaining social relationships based on Lee and Trail’s (2007) definition of companionship. People who have high companionship needs think that true friendship, camaraderie, and fellowship are very important. Companionship needs are associated with the other belonginess needs (family togetherness, mature love, and social acceptance, to some extent). Similar to those needs, it is also related to the values of commitment, benevolence, and openness. Gau (2007) found that there were no gender differences on sense of belonging (related to companionship) between American males and females. However, there were gender differences between Taiwanese males and females. A cultural difference may exist here, but relative to American society, it seems that men and women both think that this is important. Specific to sport, Lee and Trail (2007) found that people who rated companionship highly also tended to be fans of a specific team (6.7% of the variance). However, companionship was not significantly related to being a sport fan or to any sport behaviors. In the hockey team research, companionship explained about 7% of BIRGing behavior, but less than 5.5% of being a fan of the team, talking about the team (WOM), or intending to go to more games. In the professional women’s soccer data (Nicefaro & Goobes, 2017), need for companionship was slightly related to the perception that going to the game Copyright 2018 Galen Trail

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provided social interaction opportunities. This was replicated in a professional men’s soccer team data set that some of my students collected (Gallo, Yu, & Sharma, 2017); but need for companionship was also associated with the idea of supporting soccer in the U.S. (supporting a cause). The former makes sense as if you perceive that there are social interaction opportunities at the game and you are high in the need for companionship, then you might go to fulfill those needs. However, if you perceive that going to a game shows that you are supporting a cause, I’m not sure how that fulfills meeting the need for companionship unless you feel that other people are going for the same reason and you can associate with them to meet the companionship needs. Gau (2007) found that sense of belonging was unrelated to being a fan or any sport consumption behaviors for Americans. In the Taiwanese sample there was a small negative correlation with talking about sports, but this was probably driven by the females in the sample, as there was a large difference between men and women on this aspect as noted above.

Need for Social Acceptance. I have defined this as the need to belong in a social group where there is a feeling of inter-connectedness. Although this need is in Maslow’s (1954) belonginess-needs level, it is not as closely related to the other three as they are to each other. This need is somewhat related to the values of benevolence and commitment, but not nearly as highly as the other belonginess needs. Sport fans did not significantly differ from non-fans on the need for social acceptance in the U.S. national study. I thought that they would be significantly higher on this need because sport fans seem to need to be accepted by the fan community. Ko et al. (2017) found that need for affiliation was correlated with spectating involvement and spectating intention. In the women’s soccer data (Nicefaro & Goobes, 2017), need for social acceptance was associated with the perception that the match provided opportunities for social interaction. That is, the more people perceived social interaction opportunities at the match and the more they had a need for social acceptance that they though could be filled at the match, the more likely they were to go. Similar results were found in the hockey data. However, in addition, those high in the need for social acceptance were also more likely to BIRG, to perceive that the game offered the product benefit of diversion and the product attributes of aesthetic and dramatic play. The fans of the men’s soccer team (Gallo, Yu, & Sharma, 2017) who were high in the need for social acceptance also perceived that the matches offered opportunities for social interaction. In addition, they were more likely to perceive the players to be role models and to be attractive. Copyright 2018 Galen Trail

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SAFETY NEEDS Need for Personal Financial Security. I modified Lee and Trail’s (2007) definition to work for a need: The need to manage money so that one is financially secure at all times. People who have this need are focused on achieving stability and security through managing their money well. This need was related to personal safety needs. In addition, financial security was related to achievement to some extent. Interestingly, it was not related to the goal of democracy, nor was it related to materialism. This need was related to the values of work-ethic, self-control, pragmatism, self-direction, and frugality. What this means is that those values will be used when trying to fulfill the financial security needs. So, someone high in the need for financial security will work hard, be very in control of their financial goals, work toward those goals independently, and in a financially conservative way (frugally). Within the sport realm, in the national data set, there were no differences between fans and non-fans (Figure 7.4). However, both in the Lee and Trail (2007) research and in the hockey team research, personal financial security was slightly negatively related to both being a sport fan in general and being a fan of a specific team. In other words, those individuals who focused on managing their money so that they would be financially secure were less likely to be fans than those who put less emphasis on financial stability. However, there were no significant relationships between this need and any sport consumer behavior, including attendance, watching sport on TV, consuming print media, listening to games on the radio, or internet consumption related to sport. Similar non-findings were evident in the men’s soccer data (Gallo, Yu, & Sharma, 2017). Need for Personal Safety. I modified the Lee and Trail (2007) definition for this construct as well as they considered it the internal goal of individual safety. I have defined it as the need to attain a high level of personal safety by feeling protected and secure. People who score highly on this need are unlikely to take risks that might put themselves in danger. These could be risks such as walking alone at night in a bad part of town, going sky-diving or eating blowfish. Personal safety refers solely to the physical entity and does not apply to financial risks. It is related to personal financial security needs though and seems to be antithetical to stimulation needs not surprisingly. It is also related to the values of self-control, commitment, and openness. Within the sport realm, little research has been done in this area. In the U.S. national study, I found no differences between sport fans and non-fans (Figure 7.4). Lee and Trail (2007) found that individual safety had absolutely no significant relationship to any sport attitude, cognition, or behavior. However, in some research that I did for an Arena League Football team, I found that some season ticket holders had not renewed due to issues of unruly fans in the stands around them. This could be evidence that these people were concerned for their own physical safety and the safety of their families. In the professional women’s soccer team data (Nicefaro & Goobes, 2017), need for personal safety was slightly related to the product benefit of supporting a cause (women’s sport in this instance). Similarly, in the men’s soccer data (Gallo, Yu, & Sharma, 2017), it was also related to supporting their cause (soccer in the U.S.), and perceiving that the players were role models. I can’t make sense out of this last one. Copyright 2018 Galen Trail

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PHYSIOLOGICAL NEEDS Need for Physical Well-Being. The last need within Maslow’s (1954) hierarchy is the only physiological need that is relevant and that is the need for physical well-being. I have defined that as the need to attain personal health and physical fitness based on Lee and Trail’s (2007) definition. People who have this need are physically active and lead a healthy lifestyle. The need for physical well-being should be correlated with the safety and security needs, which would support Lee and Trail’s findings, but it isn’t. It is slightly correlated with some of the self-actualization needs, which to some extent makes sense, but still, the focus of the items is on being healthy, fit, and in shape. In the national data set, sport fans were higher in the need for physical well-being than non-fans (Figure 7.4). Lee and Trail (2007) found that physical well-being was positively related to being a sport fan in general, similar to the national data set, but not related to being a fan of a specific team. There was no relationship between this need and any sport consumption behavior either. Thus, people who are physically fit are no more or less likely to go to games than those of us who are coach potatoes. Similar results were found in both the women’s and the men’s professional soccer data sets, with the exception that those high in this need in the women’s soccer data set (Nicefaro & Goobes, 2017) were more likely to perceive that going to games supported the cause of women’s sports, and those in the men’s soccer data set (Gallo, Yu, & Sharma, 2017) perceived that the male players were role models. In sum, many of the needs do not have a direct impact on sport consumption behavior intentions, attitudes, or product attributes or benefits. So why are we interested in them? Partially because we have just started investigating them and haven’t determined the extent that they are related to sport consumption aspects. However, primarily we are interested in them because although they may not directly impact sport consumption aspects, they may do so indirectly through values because as we have shown above, all needs are related to at least one value if not several. We also need to appreciate the difference between needs and values though, but first we need to consider what values are. Personal Values Gordon (1975), Rokeach (1973), and Schwartz and Bilsky (1987) all agreed that needs shape values. Specifically, Rokeach (1979) suggested that psychological needs and societal demands are the primary influencers of values (p.2). Gordon made the distinction between needs and values in this manner: Needs are satisfied by specific responses or goals and are defined by the responses or goals themselves. Values, on the other hand, may be satisfied by a variety of behaviors. Needs require satisfaction. The individual may not feel compelled to behave in a specific way to satisfy a particular value (Gordon, 1975 p.9). This distinction is critical to the hypothesis that the needs can influence value formation and value choice. For example, an individual may hold a value of honesty very highly during normal circumstances. However, if there was a food shortage, that individual may lie about his stash of food, to fulfill his own physiological need for food. In this case the need has influenced and changed the individual’s value system. Personal values have been defined and described in a variety of ways. Rokeach (1973) defined a value as "an enduring belief that a specific mode of conduct or end-state of existence is personally or socially preferable to an opposite or converse mode of conduct or end-state of existence" (p. 5). He also noted that "values are multifaceted standards that guide conduct" (p. 13). Gordon (1975) suggested that "values are constructs representing generalized behaviors or states of affairs that are considered by the individual to be important" (p.2). He also noted that although individual values can be modified, they Copyright 2018 Galen Trail

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tend to endure over time. Kahle (1983) suggested that values assist the individual's adaptation to the environment. This is accomplished through the guidance of the individual's decision about which situations to enter and how to act in those situations (Kahle, 1983). Keeney (1994) proposed that values are the "driving force of our decision making" (p. 33). Lachman, Figure 7.6 Nedd and Hinings (1994) proposed that Peripheral there is a value values hierarchy where core values exist as values that endure, and peripheral values are Peripheral Peripheral ones that can be values values modified (Figure 7.6). The former are held much more strongly than the latter, are more important, and typically cannot be modified. Lachman et al. suggested that when an individual Peripheral Peripheral must make a decision, values values multiple values come into play. The hierarchy of the values determines what decision is made. If honesty, as a value, is more highly held than concern for another’s feelings, then Jill Fan, when asked by her husband, may tell him “Yes, that horizontally striped shirt does make you look like Charlie Brown.” The organization and hierarchy of values that exist in most individuals has been labeled a "value system" by Rokeach (1973). He defined it as an "enduring organization of beliefs concerning preferable modes of conduct or end-states of existence along a continuum of relative importance" (p. 5). Schwartz (1992) synthesized the preceding information about values fairly concisely. He noted that values "(1) are concepts or beliefs, (2) pertain to desirable end states or behaviors, (3) transcend specific situations, (4) guide selection or evaluation of behavior and events, and (5) are ordered by relative importance" (p. 4). Trail and James (2015) defined values as beliefs that guide an individual’s evaluation and selection of goals, which fulfill needs, and the choice of behaviors or processes used to achieve those goals. In addition, individuals have values that are hierarchically ordered. Core values exist which transcend situations and peripheral values exist which may be situationally specific.

Core values

Influence of values on consumer behavior According to Rokeach (1973), values have three components: cognitive, affective and behavioral. An individual knows (cognition) the correct manner in which to behave based on his/her value system. The individual feels emotion (affect) about the value and the behavior that is a consequence of acting upon the value. Rokeach divided personal values into two categories: instrumental and terminal. He suggested that there are eighteen instrumental values that determine the Copyright 2018 Galen Trail

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mode of conduct of the individual. He further distinguished between moral values which are interpersonal (e.g. honest, broadminded, and forgiving) and competence values which are intrapersonal (e.g. ambitious, imaginative, and capable). The eighteen terminal values show the end-states of the conduct of the individual. Rokeach also breaks these into two categories: personal (e.g. wisdom, prosperous life, mature love) and social (e.g. world peace, equality, national security). The instrumental values determine the modes of behavior or guide the processes that are necessary for the attainment of all the values concerning the end-states. The terminal values "represent the supergoals" (Rokeach, 1973, p. 14) that the individual is attempting to attain. The underlying structure that Rokeach (1973) hypothesized has yet to be verified empirically. Other authors (Braithwaite & Law, 1985) have noted several inadequacies in the reliability and validity of the Rokeach’s Value Survey and suggested using alternatives when attempting to measure values. Schwartz (1992) suggested that there was little support for the terminal-instrumental distinction. However, Howard (1977) suggested that product category choice is better predicted by terminal values whereas brand choice is better predicted by instrumental values. Furthermore, Pitts, Wong, and Whalen (1991) suggested that instrumental values seem to be used primarily in situation-specific contexts. Another values measurement device, the List of Values (LOV), was created by Kahle (1983; Figure 7.7). The LOV was developed from a theoretical base of Feather's (1995), Maslow's (1954), and Rokeach's (1973) work on values in order to assess adaptation to various roles through value fulfillment" (Kahle, Beatty, & Homer, 1986, p.406). The authors claim that these values relate more closely to the values of life's major roles (i.e., marriage, parenting, work, leisure, daily consumption) than do the values in Rokeach’s Value Survey (Kahle et al., 1986). Both the LOV and the Values and Life Style (VALS) questionnaire (Mitchell, 1983) were developed to Externally focused Internally focused evaluate values within a consumer behavior values values framework. Kahle and his fellow researchers •Sense of Belonging •Self-fulfillment determined that the "LOV significantly predicts •Being Well Respected •A sense of Accomplishment consumer behavior trends more often than does the •Security •Excitement •Self-respect VALS scoring system and accounts for more variance •Fun and enjoyment in life in consumer behavior" (Kahle et al, 1986, p.409). The •Warm relationships with LOV consists of nine values. In addition, Homer and others Kahle (1988) determined that these values can be Figure 7.7 categorized as having either external or internal focus. Homer and Kahle (1988) found that values from the LOV predicted anywhere from 25-50% of the variance in attitudes, which then predicted about 31% of the variance in shopping behavior. However, values predicted less than 4% of shopping behavior directly, indicating that values are not good direct predictors of behavior; they seem to be mediated by attitudes. However, Kahle et al. (1986) found that the nine LOV values explained 23.7% of the variance in sports program watching on TV, 18.2% of reading behavior of general sport magazines like Sports Illustrated, almost 21% of reading of specific sport magazines (e.g. Golf Digest), and 22% of participating in sport activities like tennis or golf. Furthermore, Kahle, Duncan, Dalakas, and Aiken (2001) found that fans of women’s basketball differed from fans of men’s basketball on 4 of the 9 LOV items. More women’s team fans ranked self-respect and sense of belonging as a primary value than men’s team fans, and more men’s fans ranked self-fulfillment and security as a primary value than women’s fans. The fans did not differ from each other on the remaining values.

List of Values


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Although Kahle and his colleagues determined that the LOV scale as a whole did a decent job in predicting attitudes and behavior, Kamkura and Novak (1992) found that the LOV predicted only 2-4% of participating in physical activity or sport behavior and only 2-3% of reading sport magazines. They suggested that although values are central to decision making, they are fairly remote from each specific decision made by the consumer. There are many more immediate but less stable environmental influences such as the product attributes and benefits: price, sales promotions, and exposure to advertising that predict behavior better. Thus, they suggested that it would be improbable to segment markets by value system alone; however, values may influence preferences for product attributes, product benefits, and consumer preferences. Schwartz (1992), from his own previous work and research on Rokeach’s values, identified 10 motivational types which represented 56 values. Each motivational type was derived from some physiological or psychological need and each had a defining goal. From Schwartz’s work, Lee and Trail (2007) developed 17 value categories and defined values as beliefs that guide an individual’s evaluation and selection of personal goals. Schwartz (1992) proposed that values would be arranged in a circumplex where values that are next to each other are similar (highly correlated) and values that are opposite each other on the circumplex are antithetical to each other (negatively correlated). Furthermore, values that are at right angles to each other would have no relationship with each other (uncorrelated). Based on Schwartz (1992), Lee and Trail (2007) hypothesized that their 17 values would be related in the circumplex as is depicted in Figure 7.8.

Figure 7.8

Values Moderation

Conservatism Patriotism

Hedonism

Spiritualism Self-Control Ambition

Stimulation

Commitment

Pragmatism

Benevolence

Aesthetics Tolerance

Freedom Self-Direction


Openness

Work-ethic

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However, Lee and Trail (2007) found in their research that values were not arranged as Schwartz (1992) had hypothesized and had supported in some of his other studies as well. Using a statistical technique called multidimensional scaling, Lee and Trail found that values congregated in four areas and were not arranged in a circumplex at all. Since the Lee and Trail (2007) research I have been continuing to assess needs and values with multiple data collections, some of which I have mentioned above in the needs section. In addition, based on more recent literature and other research that I have found since the original Lee and Trail research, I have also moved some of the values constructs to the needs category, moved many of the goals to needs, and modified some of the existing value constructs. I then retested the circumplex model on values using a multidimensional scaling technique. These results again show that there is not a circumplex model of values as suggested by Schwartz. What it does show is that there are some clearcut categories and some values that don’t really fit well anywhere. To get a clearer idea about which values are associated with each other, I ran another statistical technique called an exploratory factor analysis (EFA) and it showed 6 categories of values. Using Schwartz’s (1992) “motivational types” categories for values, I labeled the six categories: universalism values, benevolence values, conformity values, self-direction values, hedonistic values, and security values (Figure 7.9). Each of the value constructs are placed in these categories based on the EFA. Figure 7.9


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So, most of the values discussed below are the same as those in Lee and Trail (2007), but there are some new ones as well. I will discuss each value, the values that it is related to, and the values that it is antithetical to. I will also discuss the relationship each value has with sport consumption cognitions (fan identification), attitudes, and sport consumption behaviors. In general, though, Lee and Trail (2007) found that personal values (as an entirety) were related to general sport fandom (23% variance explained) and loyalty to a particular team (22% variance explained). However, values predicted attitudes about sport a lot better than they predicted actual sport behavior lending support to the supposition that they are distal predictors which may be mediated by other factors. Although values were related to game attendance (10% of the variance), watching sport on TV (8%), listening to games on the radio (7%), and reading about sport on the Internet (6%), the relationships were not as strong as the cognitive/attitudinal aspects (fandom and loyalty). In addition, values were not significantly related to sport merchandise purchasing or reading print media about sport. UNIVERSALISM VALUES DIMENSION Let’s talk about each of the values now, starting with the category of universalism values, which includes democracy, tolerance, environmentalism, social justice, and global peace. Sagiv and Schwartz (2000) defined this category as “understanding, appreciation, tolerance and protection for the welfare of all people and for nature” (p. 179). Democracy. I have modified Lee and Trail’s (2007) definition of democracy to be: The belief in the exercise of majority rule directly or indirectly. People who believe in democracy think there should be governance by the people. They also think the ‘majority’ in the population should decide who should be elected, who should make the rules and policies, and should do the governing. Democracy is not closely related to the other universalism values and that is because it is also related to the Conformity Values category as well, just not as highly. It also seems to be somewhat antithetical to the hedonism values. Relative to sport, sport fans and non-fans were not significantly different on this value as both thought Democracy was fairly important (Figure 7.9). Lee and Trail (2007) found that as beliefs in democracy increased, the likelihood of being a general sports fan increased slightly (around 7% of the variance), as did the likelihood of being a fan of a specific team (9%). Watching sports on television also increased as the beliefs in democracy increased, but the relationship was small (less than 4% of the variance). There were no other significant relationships apparent. Tolerance. Tolerance values are the beliefs in accepting opinions and practices that differ from one’s own. People who have these values are open-minded, non-judgmental and accepting of differences. Tolerance values are related to global peace values and environmentalism values, but antithetical to the Conformity Values category (patriotism, conformity, etc.). Maybe this illuminates why there are so many problems in the world today. Those individuals who are highly loyal to their own country do not seem to be very tolerant of other viewpoints. Furthermore, those who see themselves as highly religious (spiritual) do not seem to be very broadminded or accepting of differing viewpoints. Non-fans in the national data sample were more tolerant than sport fans (Figure 7.9). In addition, Lee and Trail (2007) determined that those people high in tolerance were less likely to watch televised sports or to listen to sports on the radio. The flip side of this is that those people who are not very tolerant watched sports on TV and listened to them on the radio. Lee and Trail found no other relationships between tolerance and any sport related behavior or attitude.


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Environmentalism. I had to modify Lee and Trail’s (2007) definition of environmentalism slightly because I moved it from a goal to a value. I now define it as the belief in the preservation of the natural environment from destruction or pollution. People who score highly on environmentalism values believe that it is necessary to prevent the destruction of nature’s resources to protect the environment. They also believe that people should be in harmony with nature and preserve it as much as possible. Environmentalism values are related to tolerance values as noted above and are antithetical to the Conformity Values category. Non-fans score substantially higher than sport fans on environmentalism values in the national data set (see Figure 7.9). Lee and Trail (2007) found there were no significant relationships between environmentalism and any sport consumer behavior or attitude toward sport (being a fan, etc.). More recent research (see McCullough and colleagues) about environmental sustainability and sport may be able to show new attitudes are being developed by sport fans as more and more teams work on developing sustainability campaigns. Social Justice. I renamed Lee and Trail’s (2007) goal that they called social equality and called it social justice and put in the values category. The new definition is: the belief that all people should be treated similarly and fairly, with dignity. People who believe in social justice support human rights and human dignity. Social justice is correlated with global peace and tolerance, and to some extent, environmentalism. It is also somewhat related to the Benevolence Value category. It seems to be antithetical to hedonism and competitiveness. Within the sport realm, non-fans scored significantly higher than sport fans on social justice values (Figure 7.9). Similarly, Gau (2007) found that people who placed less importance on equality (similar to social justice values) were more likely to consume spectator sports than those who placed more emphasis on it. Lee and Trail (2007) had similar findings. They showed that as the importance of social equality increased, people were less likely to be fans of sport in general, less likely to be fans of a specific team, and less likely to watch sport on TV. Global Peace. I have defined global peace as the belief that peace is necessary throughout the world. This is similar to Rokeach’s (1973) value of a world at peace. People who believe that peace is necessary think that the world should be free from war and conflict, and nations should work together to help each other out. Global peace values are related to the other universalism values and also to some of the benevolence values. It is antithetical to patriotism and conformity values. In the national sample, non-fans score substantially higher than sport fans on global peace (Figure 7.9). Gau (2007) found that women who wanted world peace were less likely to watch sports on television and less likely to discuss sports with other people. As far as I know, that is the only research that has investigated global peace and its relation to sport. BENEVOLENCE VALUES DIMENSION According to Sagiv and Schwartz (2000), the benevolence value dimension represents the “preservation and enhancement of the welfare of people with whom one is in frequent personal contact” (p. 179). Based on the EFA, in my U.S. national data, this category includes Commitment values, Benevolence values, Openness values, and Work Ethic values. Copyright 2018 Galen Trail

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Openness. People who have openness values believe that people should be honest and candid. They are trustworthy, sincere, and truthful. Openness is related to the other values in the Benevolence category: work ethic, benevolence, and commitment. These values seem to be antithetical to conformity and hedonism values, supporting Lee and Trail’s (2007) results that showed that openness values were antithetical to patriotism values and conservatism values. The sport specific research has been somewhat contradictory as non-fans were slightly higher in openness values than sport fans, but Lee and Trail (2007) found there was no relationship between openness values and any sport attitude or sport behavior, implying that sport fans do not differ from non-sport fans on this value. However, Gau (2007) found that people who placed less importance on moral values, similar to the openness values of Lee and Trail, tended to watch sports on TV more than people who placed more importance on moral values. Similarly, Americans who were less moral also talked about sport more than those who were more moral. Benevolence. Benevolence is the belief that one should take care of, and be kind to, the people with whom one has personal relationships. Benevolence values are related to openness and commitment values, and to some extent work-ethic values, but also related to pragmatism and other values. Lee and Trail (2007) found that benevolence values were similar to work-ethic values also, but antithetical to values of hedonism. People who have benevolence as a guiding principle in their life are often viewed as being kind and considerate toward others. Benevolent people often are very generous and typically are supporters of, and donate to, various causes, as we noted in the introduction of this chapter. Although those examples seemed to indicate that sport fans are benevolent, in the national data sample (Figure 7.9) there were no differences between sport fans and non-fans. This supported Lee and Trail’s (2007) research. They did not find that benevolence was related to being a sport fan or being a fan of a specific team. Benevolent people did not attend more sporting events, watch more games on television, or do any of the other behaviors any more frequently than people who were not benevolent. This can be thought about in two ways: people who are fans are not any more or less likely to be benevolent than people who are not fans; or benevolent people are equally likely to be sport fans or to not be sport fans. However, in the women’s professional soccer data (Nicefaro & Goobes, 2017), we determined that those who were high in benevolence values, were more likely to perceive that they were supporting a cause when attend the women’s soccer matches. They were also more likely to perceive that the players were role models (product benefit), the matches were dramatic, and that the players had good skills; the latter two are product attributes. In the men’s soccer data (Gallo, Yu, & Sharma, 2017), benevolence values were only related to the perception that the players had good skills. Copyright 2018 Galen Trail

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Commitment. Commitment values are beliefs that if an individual is in a relationship with another person, the individual should be faithful to that person. People who are committed to each other are dedicated and devoted to each other. Commitment values were related to the other value constructs in the benevolence category and antithetical to hedonism values. This commitment value solely focused on the commitment to another person, which is different than psychological commitment that some researchers have used to explain an association with a sport team. In sport, sport fans were not significantly higher on commitment values than non-fans in the national sample (Figure 7.9). In addition, Gau (2007) found that female sport spectators placed more importance on commitment values than non-spectators, but that there was no relationship between commitment values and any type of sport behavior. Lee and Trail (2007) found the same thing. There was no relationship between commitment values and sport attitudes, nor was there a relationship between commitment and sport behaviors. Work Ethic. The values concerning work ethic are defined by beliefs in the virtues of hard work and diligence (Lee & Trail, 2007). People who score highly on these values persevere in the face of adversity and show gritty determination when attempting to achieve an objective. Even though workethic values are in the Benevolence Values category, they are also related to values of self-control, selfdirection and pragmatism among others. They are lowly correlated with many different types of values, but they seem to be antithetical to the values of hedonism based on this research and that of Lee and Trail (2007). In the national sample, sport fans and non-fans didn’t differ significantly (Figure 7.9), which contrasted with what Lee and Trail (2007) determined; that being a sport fan was predicted by work-ethic values, but no specific sport related behavior or other team specific attitudes was predicted. Now perhaps if Lee and Trail had surveyed fans of the Pittsburgh Steelers or of the Columbus Crew, two teams that are known for their hard-working mentality, they might have found a relationship between being a fan and seeing the importance of a good work-ethic because in the survey of the hockey team fans, a team known for their effort, there was a significant but not meaningful correlation between the value of work-ethic and intending to go to games (r = .227) and being a fan of the team (r =.162). In addition, those fans who valued work-ethic thought that the players were good role models for kids. Similarly, in the men’s soccer data (Gallo, Yu, & Sharma, 2017), those who were higher in work-ethic values thought that the soccer players’ skills were greater than those who were lower in work-ethic values. They were also more likely to perceive that the players were role models (product benefit) and perceive that going to a match was supporting a good cause (product benefit). However, in the women’s professional soccer data, there were no meaningful relationships between work ethic and any of the behaviors, attitudes, product attributes or benefits. Copyright 2018 Galen Trail

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CONFORMITY VALUES DIMENSION Sagiv and Schwartz (2000) defined conformity values as those focused on the restraint of “actions, inclinations, and impulses likely to upset or harm others and violate social expectations or norms” (p. 179). Based on the Euclidean Distance Model and the EFA, I determined that four value constructs fit within this dimension: self-control, spirituality, conformity, and patriotism. Self-Control. Self-control values represent the idea that controlling one’s emotions, desires, or actions by one’s own will is preferable. People whose actions are guided by these values show selfdiscipline and restraint in their behaviors. Self-control values were more closely related to the other conformity values in this dimension in the EFA than values in other dimensions, they are also, to some extent, related to other values as they were lowly correlated with several others. Similar to many of these values, they are antithetical to hedonism. In the national sample, non-fans were slightly higher in this value than sport fans, but not by much. Lee and Trail (2007) also found that self-control values did not have any relationship with any attitude about sport or sport related behavior. This indicates that there are no differences between people who think that self-control is important and those that do not, at least in terms of sport behavior. Spirituality. Spirituality is the belief in a religious and philosophical tendency to pursue meaning in life (Schwartz, 1992). People who are spiritual are devout in a religious way. However, these values are not specific to any particular religion, or necessarily referent even to religion in general, although they could be. Interestingly in the national sample, spirituality values were most closely related to patriotism values, replicating what Lee and Trail (2007) found. Spiritual values were antithetical to hedonistic values, but also dramatically different from social justice and tolerance values. Non-fans were slightly more “spiritual” than sport fans in my national sample, but not meaningfully so. Although Lee and Trail (2007) also did not find any relationship between spiritualism values and sport attitudes or behavior, but Gau (2007) did. He determined that individuals who held spiritual values highly were less likely to watch sports in person than those who did not. However, in a seeming contradiction, he also found that those who said that they were devout watched more sports in person than those who were not devout. Maybe they were devout fans, but not religiously devout? Gau also found that people who placed a lot of importance on inner harmony were less likely to watch sports on TV than those who placed less importance on it. Furthermore, women high in inner harmony were considerably less likely to consume spectator sports or talk about sports with others than women who indicated that inner harmony was not important to them. Conformity. Conformity values are beliefs that one should comply with traditional social norms, values, and local customs. They are similar to the other value constructs in this dimension, but they are also similar to competition to some extent. Lee and Trail (2007) determined that conformity values seemed to be antithetical to values of commitment and openness. People who are conformists are conventional, oftentimes compliant and conform to the majority of the society in which they live. Copyright 2018 Galen Trail

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Specific to sport, sport fans were not significantly different from non-fans in the national sample. Lee and Trail (2007) determined that people who held values of conformity (conservatism) were more likely to be general sport fans and to be fans of a specific team. Not surprisingly this translated into watching sports on TV more than those people who were not as conservative. Gau (2007) found similar results in that conservative people consumed more spectator sports than those who were less conservative. However, Lee and Trail did not find any relationship between conformity and merchandise purchasing, print media consumption, game attendance, listening to the radio, or sport consumption on the internet. Similarly, in the men’s soccer data (Gallo, Yu, & Sharma, 2017), there were no relationships between conformity and any attitude, behavior, product attribute or benefit. Patriotism. Patriotism values are defined as beliefs about being devoted to one’s country (Lee & Trail, 2007). People who are patriotic have a nationalistic sense of pride and are very loyal to their own country. Patriotism values were not highly related really to any other values in the Lee and Trail data and only slightly related to spirituality in the national data set. However, Lee and Trail found that they seemed to be antithetical to the values of openness, freedom, commitment, and tolerance, which was replicated in the national data set. Within sport, sport fans were considerably higher in patriotic values than non-fans. In addition, Lee and Trail (2007) found patriotism values did the best job of any value in predicting sport attitudes and behaviors. These values were fairly highly related to being a sport fan and a fan of a specific team. Patriotism also predicted TV watching behavior and radio listening behavior the best, as well as buying team merchandise and attending games. McDaniel (2002) also found that patriotism predicted interest in the Olympic Games, but the amount of variance it explained was low. Similarly, Gau (2007) determined that sport consumers indicated that general loyalty values were more important than non-consumers did. However, in the women’s soccer data set (Nicefaro & Goobes, 2017), patriotism was not related to any sport attitude, behavior, or perceived product attribute or behavior. SELF-DIRECTION VALUES DIMENSION The self-direction dimension was defined by Sagiv and Schwartz (2000) as values that represent independent thought and action: choosing, creating, exploring. Both statistical techniques on the U.S. Copyright 2018 Galen Trail

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national data set placed the values of pragmatism, freedom, and self-direction together in this dimension. Pragmatism. Pragmatism values represent beliefs in practical, matter-of-fact ways of approaching situations or of solving problems. People who live by these values are rational, reasonable, and practical. Pragmatism values were related to a variety of other values. However, pragmatism values seemed to be somewhat antithetical to spiritual values and hedonism values. Within sport-related research, non-fans and sport fans were not meaningfully different. Similarly, Lee and Trail (2007) also found that pragmatism values had little to do with any sport specific attitude and absolutely no significant relationship with any sport specific behavior. They were only moderately predictive of being a sport fan. This might be due to the necessity of having at least some sport knowledge to not look socially inept in many settings in the United States and other sport mad countries. An example of this is in the movie Click starring Adam Sandler. Sandler’s character, along with his boss, is in a business meeting with some Japanese clients. The clients and Sandler are discussing the relative merits of Ichiro versus Matsui. Sandler’s boss, played by David Hasselhoff, having no knowledge of baseball, did not realize that, at that time, Ichiro was a baseball player for the Seattle Mariners and that Matsui was a player for the New York Yankees. Hasselhoff’s character makes the incorrect assumption that everyone is discussing two types of Japanese food and indicates that he has not eaten either of them; obviously, a major faux pas on many levels. Freedom. Freedom values represent the belief that an individual has the opportunity to do as she or he chooses. People who have these values believe everyone should have the liberty to make their own choices; people should not be forced into decisions they do not like or forced into situations that make them feel uncomfortable. In the U.S. national data set, in addition to freedom being related to the other values in self-direction dimension, it was also related to global peace and tolerance. It seems to be antithetical to spirituality and hedonism. Sport fans and non-fans did not significantly differ on freedom values. In addition, freedom values were not significantly related to any of the attitudes about sport or any of the behaviors such as attendance, merchandise purchasing, or media consumption in either the Lee and Trail (2007), the Gau (2007) research, or the men’s soccer data set (Gallo, Yu, & Sharma, 2017). In the hockey team research the value of freedom was very lowly correlated with the product benefit of diversion. It seems that people who are sport fans vary across all levels of how important freedom values are. Copyright 2018 Galen Trail

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Self-Direction. According to Schwartz (1992), self-direction values are the beliefs that one should be independent in thought and action. People who think this value is important are decisive, independent, and self-reliant. In addition to being related to the other values in this dimension, selfdirection values are related to many other values, but they seem to be unrelated to spirituality and hedonism values. In the U.S. national sample, sport fans and non-fans did not differ on self-direction values (Figure 7.9). Gau (2007) determined that male sport consumers were more likely to think that selfconfidence values, somewhat similar to our self-direction values, were more important than for males who did not consume sport. However, Lee and Trail (2007) found that the only relationship that selfdirection values had with any sport behavior was negative. As people put greater emphasis on being self-reliant, the less likely they were to listen to sports on the radio. It seems as though these values have little to do with sport behaviors or attitudes, although they may have a lot to do with personal success in life. HEDONISTIC VALUES DIMENSION Sagiv and Schwartz (2000) defined the hedonism motivational-type dimension as focusing on pleasure and sensuous gratification for oneself. I have expanded that description and content to include competitiveness because competitiveness is sensually gratifying for some people: the objective is solely focused on beating others to get want one wants and is very inner focused and potentially deleterious to others. There only two values constructs in this dimension: Hedonism and Competitiveness. Competitiveness. I have defined competitiveness values as a strong belief or desire to achieve more than others. People who hold these values highly are very ambitious, assertive, and sometimes aggressive in trying to obtain what they want. In the U.S. national data set, competitiveness is related to hedonism, and is slightly related to work ethic. People who are ambitious often are very competitive both in work and in play. This value is rewarded in American society fairly frequently, both in schools, in the workplace, and certainly on the field or court. Specific to sport, sport fans were substantially higher in competitiveness values than non-fans, not surprisingly. This supports Lee and Trail’s (2007) findings that people who were guided by the value of ambition (competitiveness) were more likely to be sport fans and attached to a specific team. Furthermore, ambitious people were likely to attend games, watch a team on television, buy teamlicensed merchandise, read about the team in print media, and use the internet to find information about the team. Although Gau (2007) did not find a positive relationship between the value of ambition and various sport consumption behaviors, he did find that people who were ambitious were more likely to discuss sports with others than those who were not. This relationship was evident in males, but not in females. Furthermore, sport spectators rated ambition higher than people who were not sport spectators. In the men’s soccer data set (Gallo, Yu, & Sharma, 2017), those high in competitiveness Copyright 2018 Galen Trail

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values were more likely to be sport fans, supporting the previous research, but other than being related to nostalgia (product benefit), there were no other associations with any behavior, attitude, or product attribute or benefit. In the minor league hockey data set, people high in competitiveness values were more likely to think that the players were role models and to perceive that the play was very aggressive. I guess this makes sense because fans who are very competitive themselves, would see players who they perceive as being aggressive, as good role models. This obviously needs to be investigated further. Hedonism. Hedonism values represent beliefs in pleasure or sensuous gratification for oneself (Schwartz, 1992). People who are hedonistic are typically self-indulgent and seek pleasure in whatever they are doing. Hedonism values are antithetical and unrelated to most other values other than competitiveness. Hawkins, Best, and Coney (2004) noted that sensuous gratification is more acceptable today that it was in the past and that some consumers are indulging themselves more lavishly than ever before. That said, hedonism values were the least important of all the values (Figure 7.9) by a fair amount. However, it may be that people just aren’t willing to acknowledge how important hedonism values are to them, despite what Hawkins et al. think. Specific to sport, sport fans are considerably more hedonistic than non-fans, at least in the U.S. national study. Gau (2007) found that for females, hedonistic values could be fulfilled through watching sports on TV or reading about sports in the print media. Interestingly, there was no significant relationship between these values and sport related behaviors for men. Lee and Trail (2007) found similar results. Those individuals who were high in hedonism values were slightly more likely to be sport fans in general and fans of a specific team, than those individuals low in hedonism values. The highhedonism people were also more likely to watch sports on television. However, even though these relationships were significant, they were still fairly small. This was not replicated in the men’s soccer data (Gallo, Yu, & Sharma, 2017) or in the women’s soccer data though (Nicefaro & Goobes, 2017), with one small exception. As the men’s soccer fans increased in hedonism, they were more likely to perceive that attending games was the product benefit of diversion. SECURITY VALUES DIMENSION Sagiv and Schwartz (2000) defined this dimension as one that focused on safety and stability. However, most of the values that they listed here are actually needs and I moved them into the needs category. The only value that was left was frugality, so perhaps this dimension should be renamed in the future. Copyright 2018 Galen Trail

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Frugality. I’ve defined this value as the belief in managing money in a cautious and sensible way. People who are frugal spend money very cautiously and conservatively so that they do not risk their financial security. Frugality is not highly related to many other values but is lowly associated with some of the more centrally located values. In the U.S. national survey, sport fans did not score as highly on frugality values as non-fans did. In the men’s soccer data (Gallo, Yu, & Sharma, 2017), people that scored highly on frugality values were more likely to perceive the promotions that the team provided (non-product related attribute) as having a positive impact on their attendance. They also felt the team was successful and that the players were role models. I’m not sure why this would be the case. No rational explanation jumps out at me, so if you think of one, contact me and I’ll put it in this section. 😊 Relevance to Marketing Strategy and Applications It is evident from the preceding information on needs and values, that some of these aspects have an influence on sport consumer attitudes, perceived product attributes and benefits, and behavior. It is also evident some needs and values don’t. In addition, some of those that do have a significant relationship may not have a very meaningful relationship; in other words the amount of variance that they actually are able to predict in sport consumer behavior typically is small. However, according to several theorists, this is because there are many other things that mediate (come in between) the relationships between needs/values and consumer behavior, such as product attributes. For example, the need for achievement may predict sport merchandise consumption a little bit, but the price, the quality, and the brand of that merchandise may mediate that relationship. Thus, achievement may influence the perception of whether the merchandise is too pricey or of good quality. Those perceptions may have a bigger influence on whether the individual buys the product or not than the influence of the need for achievement. These mediated relationships are evidenced in the model depicted in Chapter 2. From a marketing standpoint, the marketer needs to realize that price may have a large impact on people purchasing season tickets. However, the marketer needs to go beyond that and determine why people feel that price has such an influence on purchases. If marketers can determine the “why” or the psychological reasoning behind what people feel or do, then marketers can either market their products to ameliorate negative psychological influences or accentuate positive psychological influencing agents. Let’s take an example all the way through though. This is based on the women’s professional soccer team data set, and we are going to look at it as if we were the marketers for that specific team. I have provided a pictorial representation of the significant correlations in that data set that would help the marketers determine how to generate a strategic marketing plan and create various communication campaigns (see Figure 7.10). Copyright 2018 Galen Trail

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Figure 7.10


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In this diagram, we have 9 personal needs that were relevant, 3 personal values, 4 product attributes, 3 product benefits, 2 attitudes relative to the team, and also attendance intentions. It is easiest if we work our way backwards through the model, starting at the bottom and moving up through the figure. We can see that attendance intentions are predicted by both level of interest in the team and team identification (cognitive loyalty to the team). Interest level (.420 * .420 = 17.6%) explains slightly more variance in attendance than team identification (.382 * .382 = 14.6%) does. In addition, the product attribute of player skills directly explains about 4% (not very much) as well. What this means is that as interest level, team identification, and perception that the players have good skills increase, so do attendance intentions. This is good information, but it really doesn’t help the marketer that much because the marketer doesn’t know why interest and team identification increase attendance intentions and there isn’t much to market other than athlete skills at the moment. So, the marketer needs to go up another level to see what impacts interest and team ID. By looking at the medium blue numbers that match the blue of the Interest box, you can see from the diagram what the impact of each of the product attributes and benefits has on interest. Based on the diagram (Figure 7.10), we can see the product attributes of Athlete Skills (.356) and Drama (.345) predict Interest the best, but the other attributes and benefits also predict Interest to a smaller extent. So how can the marketer use this information? The marketer now knows that to generate more interest in the team, she (marketer) needs to market the skill of the players and the drama of the game. Those two things will have the biggest impact on generating interest in the team. Secondarily, the marketer could market that attending the game is a good way to escape the daily grind of life (the product benefit of diversion) and increase the amount of information provided to potential consumers (information provision) through social media, emails, or whatever. Similarly, the marketer needs to identify how to build up cognitive loyalty (team identification). Not surprisingly there are similar product benefits and attributes that impact team ID (e.g. athlete skills and drama). But it is important to determine other ones that have an impact too, and perhaps a differential impact. Team success is a product attribute that falls in this category. However, marketing team success is not necessarily a good strategy, because if the team suddenly starts losing then that marketing campaign is doomed to fail. However, the marketer certainly can communicate the successes as they happen, posting them on social media and the website, emailing everyone in the database, making sure that there is good PR and assisting with stories coming from the print and broadcast media. Although marketing the product attributes and benefits is a great start, it is not sufficient. To develop deep relationships with the consumers (fans) and really increase interest and team identification, the marketer needs to understand how the needs and values impact the perceptions of the product attributes and benefits, as we have discussed in this whole chapter. In this example, we see that as the value of benevolence and the needs of wisdom and personal growth increase, the perception that coming to the games and supporting the team is supporting a cause, increases. Understanding this, the marketer can create messaging and communication campaigns that play up these aspects and help the fans fulfill these needs and these values. For example, the marketer can create communications that focus on how supporting women’s sports will help fulfill the need of personal growth and the value of benevolence. The communication (an email) would thank the fans for coming out to support women’s sport and the team. Thanking those that feel this way for their generous support (generosity is a part of benevolence) and for being role models for other fans (developing into a better person; a part of personal growth), helps fulfill those needs and values for those people. As the fans feel better about themselves and supporting the cause, they are more likely to develop greater interest and identify with the team more, thus moving along the consumer pathway. Copyright 2018 Galen Trail

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Renton RoadRunners Example Let’s go back to our fictitious Renton Roadrunners and look at how the needs and values of their attendees might be marketed. The data below is real, but it just happens to be from a team that is not (obviously) the fictitious RoadRunners. There are N/A’s where data was not collected on those needs or values unfortunately. Personal Motives Personal Needs Inner Peace Personal Growth Aesthetics

Wisdom

Curiosity

Stimulation

Prosperity Achievement

Power


N/A N/A 42.3% of all respondents indicated that this was a very important need (7+ on 9point scale). It explained 10% of the variance in the perception that the games were aesthetically pleasing. This indicates that there is a segment that marketers should be communicating how the aesthetic properties of the game fulfill aesthetic needs. 71.2% indicated that this was a very important (or higher) need. Although wisdom is important for a lot of people, it didn’t predict any attitudes, associations, opinions, or behaviors for the RR. 62.9% indicated that this was a very important (or higher) need. The higher the score on curiosity, the higher the score on information provision (i.e., those high on this need indicated that they thought the team was providing sufficient information about the team and its endeavors. In addition, those high on this need also thought that the games were dramatic and aesthetically pleasing. Thus, those aspects could be marketed for this group to fulfill those needs. 43.9% indicated that this was a very important (or higher) need. In addition, those higher on this need perceived that the players were role models, that the games provided opportunities to interact socially, and that the team had adequate information provision. Also, those high in the need for stimulation were more likely to BIRG (Bask In Reflected Glory). Thus, the team should provide this group with the opportunity to BIRG with other fans and players that they see as role models. The team needs to promote these opportunities via social media and other digital communications. N/A Only 30.4% indicated that this was a very important (or higher) need. Those high in this need perceived the games to be aesthetically pleasing and that the team did well in information provision. In addition, the higher the need for achievement, the more likely they were to BIRG. The team needs to provide opportunities to BIRG for this group, probably through social media, where they can comment on the style of play and the aesthetic highlights of the games. N/A

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Family Togetherness Mature Love Companionship

Social Acceptance

Financial Security

Personal Safety

Physical Well-Being Personal Values Democracy Tolerance Environmentalism Social Justice Global Peace Openness Work-Ethic

Benevolence Commitment Self-Control Spirituality Conformity Patriotism Pragmaticism


N/A N/A 73.5% indicated that this was a very important (or higher) need. Those high in this need perceived that the games provided good opportunities to interact socially, that the games provided opportunities to escape (diversion), and that the games were dramatic. This group also wanted to BIRG. Thus, the team should promote that the games are great places to get away, watch a dramatic game with other like-minded individuals, while showing off their team paraphernalia (BIRGing). 37.9% indicated that this was a very important (or higher) need. Those that scored high on this need fit in with those who scored high on companionship. All of the things noted above apply to this group as well. However, this group was much more concerned with the success of the team, than the Companionship group. If the team was losing, they may not come to games. 78.2% indicated that this was a very important (or higher) need. Those high on this need slightly perceived that the games were dramatic. Nothing else was relevant. Those high on this need did not see that the ticket prices or other costs were too high, which is good, indicating that these costs of going to the game are not constraints even for those who had a high need for financial security. 65.0% indicated that this was a very important (or higher) need. Those high in this need perceived that the games were a good place to get away (diversion) and interact socially. They also thought that the players were role models. All of these things indicate that they perceive the games to be safe for themselves and their family. N/A N/A N/A N/A N/A N/A N/A 73.1% of all respondents indicated that this was a very important (7+ on 9-point scale) value. Those that thought work-ethic values were important perceived the players to be role models and to work hard during the game and behave appropriately both on and off the court. They also thought that the games were a good place to interact socially. This indicated that this group felt that both other fans and the players also appreciated working hard and doing a good job. Marketers should promote these aspects to this group. N/A N/A N/A N/A N/A N/A N/A

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Freedom

Self-Direction Competitiveness

Hedonism Frugality

70.8% indicated that this was a very important (or higher) value. Even though this value was important to most people, it did not predict any aspect associated with the team. N/A 30.4% indicated that this was a very important (or higher) value. Interestingly, those high in this value felt that the players played aggressively and that they were role models. I guess this group likes tough, aggressive play, and wants their kids to do likewise. Marketers need to be careful with this information and may not want to market these ideas. N/A N/A

What we can learn from the RoadRunners example is that there seems to be different groupings of people and that the marketers for the Runners need to market (and communicate) different messages to the different groups because different needs and values will be fulfilled in different ways for different people. This is called segmentation, which we will discuss at the end of the book. However, what we also see is that, in general, needs and values impacted behaviors very little directly, but indirectly through the perceived product attributes and benefits, some needs and values could impactful. Summary Although I listed a large number of needs and values, there were not many that had a large influence on consumer attitudes, product associations, or behaviors directly. As I pointed out above, these relationships certainly might be mediated by other variables such as product attributes and benefits, as shown in Figure 7.10. Even if this is the case, it still is beneficial for marketers and managers to understand their fans’ needs and values. This comprehension in concert with other variables will help predict sport consumer behavior and help manage fan satisfaction better. Even so, there are still internal constraints that may prevent people from going to games or doing other types of sport related behaviors. Thus, I will examine those in the next chapter.


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Internal Constraints


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Chapter 8 Internal Constraints

The Stealth was a professional lacrosse team in the National Lacrosse League (NLL) that moved San Jose, California, up to Everett, Washington (30 miles north of Seattle), in 2010 and became the Washington Stealth. The NLL is a professional box lacrosse league, which means the teams play indoors in an arena on a hockey-rink sized field. Currently the NLL has 11 teams, as it did in 2010. The Stealth played in Comcast Arena in Everett, which seated 8,513 people. However, they rarely filled the arena, even though they won the NLL Champion’s Cup in 2010, their first year in Everett, and lost in the championship game in both 2011 and 2013. Obviously, they were very successful during that time, but even with the success, they had attendance problems. My students and I volunteered our services to try and help the Stealth improve their brand image, attendance, merchandise sales, etc., in 2011, the year after they won the championship. I happened to be doing another project at the time that looked at the Seattle Area Sport Fandom, so I added the Stealth to that research project that looked at the fandom levels of many of the teams, both professional and college, in the greater Seattle area. After adding the Stealth into the survey, we collected an additional 400+ randomly selected people in the greater Seattle area and asked them to respond to a simple question, rating each team on the following format: 1 = I’ve never heard of this team 2 = I’m aware of this team’s existence, but not interested in it 3 = I have a small amount of interest in this team 4 = I consider myself to be a low-level fan of this team 5 = I consider myself to be a moderate fan of this team 6 = I consider myself to be a loyal fan of this team 7 = I live and die with this team Copyright 2018 Galen Trail

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Internal Constraints What we found was not overly surprising, 54% of the people surveyed had never heard of the Washington Stealth (Figure 8.1). This was

Figure 8.1

not overly surprising because the Stealth were new to the area and didn’t have a very big marketing budget. They weren’t able to get the message out to the public (lack of communication – organizational constraint, Chapter 6). They didn’t get very much coverage from the regional media either, so no information was getting out that way either. Furthermore, as can be seen in Figure 8.1, there was a lot of direct competition (external environmental constraint, Chapter 3) with teams that had been in the area longer. There was even direct competition in the city of Everett, with the Everett Silvertips (minor league hockey) and the Everett AquaSox (minor league baseball). Furthermore, the Silvertips and the Stealth played in the same arena, and their seasons overlapped some. All of these issues, plus more, caused a lack of awareness in people. Lack of awareness is the first internal constraint of twelve that we will discuss in this chapter. The others are: lack of knowledge, lack of worth, lack of interest, lack of someone to attend with, perceived lack of success, lack of interest from others, lack of time, commitments to other, poor physical health, perceived costs, and perceived bad traffic. Many of these impacted the Stealth, and impact many other teams as well. So, let’s talk about all of the internal constraints, but first we need to backtrack a bit and refresh our memories about constraints in general. Copyright 2018 Galen Trail

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Internal Constraints If we go back to our model Figure 8.2 from Chapter 2 (Revised Structural Model of Sport Consumer Behavior – shown again here in Figure 8.2), we can see that internal constraints are part of the customer environment and are impacted by both the external environment and the organizational environment. In addition, they will impact the brand attitude, behavioral intentions, and consumption behavior, either directly or indirectly. Furthermore, as we can see from Figure 8.3 (the Customer Environmental Insights framework), internal constraints impact progression along the consumer pathway, not surprisingly. Let’s review some of the previous theory and research on constraints, some of which we have covered in earlier chapters, but it won’t hurt us to review a little. Lepisto and Hannaford (1980) suggested that there were five types of barriers (or constraints) to purchasing products or services: cultural constraints, social constraints, structural constraints, marketing constraints, and personal constraints. They suggested that cultural constraints are certain cultural norms and/or values that prevent or decrease patronage. Social constraints are negative influences of social expectations and reference groups. These first two Figure 8.3 may not be that different. Structural constraints, on the other hand, are due to physical, spatial, or temporal issues, all external to the individual. Lepisto and Hannaford suggested that marketing constraints are a failed fit between the product and consumer. Finally, the last set of constraints, personal constraints, are specific to the individual and their lifestyle. As you may recall, Crawford and Godbey (1987) proposed that leisure constraints could be divided into three main categories: intrapersonal, interpersonal, and Copyright 2018 Galen Trail

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Internal Constraints structural. The intrapersonal constraints equate to Lepisto and Hannaford’s (1980) personal constraints, and the interpersonal constraints are similar to the social constraints. Crawford and Godbey’s structural constraints include Lepisto and Hannaford’s structural constraints, but also seem to include some constraints beyond their dimension. However, Kim and Trail (2010) noted that the Crawford and Godbey’s intrapersonal and interpersonal categories were not distinct and combined them, calling the new category, internal constraints. They left the structural constraints the same but renamed the category external constraints. They then proposed and tested a new model of constraints and motivators. A New Model of Constraints and Motivators Kim and Trail (2010) believed the old constraints model could be improved, so they proposed a new model (see Figure 8.4 below). The new model included internal motivators, internal constraints, external motivators, and external constraints. They suggested that internal motivators would positively influence attendance behavior and represent the initial stage in the model. Here is how the process works. If you have sufficient internal motivation for interest to be activated (i.e., you have reasons to want to attend a game), internal constraints would then come into play. If the internal constraints are strong enough to overwhelm your internal motivators, then you would not go to the game. However, if you are able to negotiate around the potential internal constraints, you still might attend. One way this

Figure 8.4

might happen is if external motivators overcome the internal constraints. In the fourth stage you would be faced with external constraints. If these constraints are not overcome, then you would not attend the game either. However, if you do not perceive that there are any external constraints or if you are able to negotiate those constraints, then you would likely go to the game. An example would probably help here. According to this model, let’s say Jorge was motivated by a need for social acceptance (an internal motivator). He had just moved to Seattle and the U.S. to take a new job. He also knew that everyone in the office was always talking about the Seattle Seahawks. He thought that maybe if went to a game, he would learn what everyone was talking about and be able to fit in (social acceptance). However, he didn’t know how to get tickets, let alone know anything about American football or the Seahawks (internal constraint – lack of knowledge). He was much more knowledgeable about real football. In addition, he didn’t want to go by himself (internal constraint – lack of someone to attend with), so he didn’t go to a game. One day though, a nice fellow co-worker (Carlee) asked if he would like to join her and her two friends because they had an extra ticket to the Seahawks’ game (external motivator – free ticket!). He gladly accepted. Carlee told him to meet her at the north Copyright 2018 Galen Trail

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Internal Constraints end of the stadium an hour before the game was to start. Jorge agreed, and on Sunday caught the bus that would drop him off at the stadium. Unfortunately, there was a wreck in front of the bus on the freeway that blocked all traffic, preventing the bus from moving (external environmental constraint – bad traffic). He called Carlee and told her about the situation. She said that she would leave the ticket at the will-call window and he could join them whenever he got there. Eventually, most of the people on the bus semi-rioted because they were all Seahawk fans and wanted to get to the game. The driver relented and let them leave the bus even though it was against policy to open the doors on the freeway, even though all traffic was stopped. Jorge joined the mass exodus and all of them raced off the bus, climbed the fence to get off the freeway, commandeered a bunch of bike-share bikes, and rode the remainder of the way to the game. Jorge was able to get the ticket Carlee left for him and meet her and her friends inside the stadium during the second quarter. He had a great time at the game, learned lots about football and the Seahawks, and made some new friends. Not only that, but the next day at work, he had a great story to tell as he went with everyone to Starbucks for the third time that morning. So, as per the model, Jorge first was internally motivated, but because he had some internal constraints that he couldn’t overcome, he didn’t go to a game. However, Carlee provided some external motivators (free ticket and friendship) that overcame Jorge’s internal constraints. Unfortunately, Jorge was then faced with the external constraint of bad traffic that looked like it would prevent him from attending. Luckily, he followed the mob as they left the bus, or he probably would have missed the game. Thus, he overcame the external constraint. Obviously, most situations are not as extreme or unlikely as the above example, but you can come up with your own examples: this is the best I’ve got. The point being is that Kim and Trail (2010) thought that this was how motives and constraints interacted to determine whether someone would attend or not. They tested the model a couple times and it seemed to work (Kim & Trail, 2010; Trail & Kim, 2011). However, as we can see from Figure 8.2, the advertising and communications that the Seahawks and the media did, created word of mouth (WOM) in the office and elsewhere (an external motivator), which caused Jorge to consider that maybe going to a game might help him meet his need for social acceptance (the internal motive). If the WOM had never gotten to Jorge, that external constraint (poor communication by the Seahawks) may have occurred before his internal constraints. If so, then the model would not be correct. As Figure 8.2 depicts, external constraints may come into play at several different times in the model, so an internal motivator, then internal constraint, then external motivator, and finally an external constraint sequence, as depicted in the Kim and Trail (2010) model may not be correct, or rather, may not be the only potential sequence. Instead, as depicted in Figure 8.2, the external environment, the internal organizational environment, and the customer environment all interact, including all motivating and constraining aspects of each, and may come into play at different times, which is more similar to Lepisto and Hannaford’s (1980) proposal. I have covered the external environmental constraints and the internal organizational constraints in earlier chapters, so I will focus on the internal constraints here. However, you will notice that there may be similarities among them in that they only differ in perspective. That is, in some cases, internal constraints are the individual’s perception of an external or organizational constraint. We will discuss each of these below. Internal Constraints Internal constraints have been defined as internal psychological cognitions that deter behavior (Kim & Trail, 2010). The definition combines both the interpersonal and intrapersonal constraints proposed by Crawford and Godbey (1987) as noted above. Kim and Trail suggested that four internal constraints - lack of knowledge, lack of someone to attend with, lack of success by the team, and no interest from others – would have particular influence on consumers. Based on other peoples’ research and some more of my own, I have added 8 more to that list of 4 and the full 12 are here: 1. lack of Copyright 2018 Galen Trail

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Internal Constraints awareness, 2. lack of knowledge, 3. lack of worth, 4. lack of interest, 5. lack of someone to attend with, 6. perceived lack of success, 7. lack of interest from others, 8. lack of time, 9. commitments to other, 10. poor physical health, 11. Perceived cost, and 12. Perception of traffic. There are probably more internal constraints, but we have to start somewhere. Lack of Awareness. If we look at Figure 8.1, we can see that lack of awareness is only a problem for a few of the teams, mostly the minor league teams (Tacoma Rainiers – baseball, Seattle Thunderbirds – hockey, Everett AquaSox – baseball, and Everett Silvertips – hockey) and of course the Stealth. Part of the issue with all of these teams was that all of them were also in towns that were nearby cities around Seattle (the Thunderbirds play in Kent, WA). So, a lack of awareness by some people in this survey was probably due to the teams not spending a lot of money marketing their teams outside their local town and that the Seattle-area regional media not spending a lot of coverage on teams that don’t generate a lot of interest. Obviously, a lack of awareness will prevent attendance. You can’t attend a game that you don’t know about. Marketers need to generate awareness by getting their message out more, convincing regional media to increase coverage, etc., but even so, minor league teams are not going to draw regionally because the interest is not there for a lot of people, which we will talk about below. Social media and WOM can be an inexpensive way to get the message out to more people and make them aware of the team, but it may not have the same impact as mass media can. However, lack of awareness doesn’t only apply to being unaware of the team. It also can apply to being unaware of certain promotions, advertising campaigns, or team initiatives. For example, the Seattle Mariners run several different environmental sustainability initiatives in the stadium. The waste diversion initiative required all attendees to either recycle or compost their waste. If attendees only bought from the in-house concessionaires, this wouldn’t be a problem as all concessionaires were required to make sure all packaging and products can be either recycled or composted. However, attendees were allowed to bring outside food into the stadium. The packaging on the outside food may not meet the recycling or composting mandate. This caused a problem as there were no longer waste receptacles for garbage. Thus, there was a certain level of lack of awareness by a small portion of Mariners attendees of the waste diversion initiative that prevented the Mariners from achieving their goal of 100% waste diversion. Eventually they brought back garbage cans to deal with the garbage. As you can see, although lack of awareness applies to the existence of the team, and that is what some marketers are most concerned about, lack of awareness can also apply to other things that negatively impact the team’s bottom line as well.


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Internal Constraints Lack of Knowledge. Lack of knowledge focuses on the individual not understanding the technical aspects of the game or sport, or not understanding the strategy or rules of the game. For example, I’m not really a fan of hockey because I don’t understand the rules or the strategy of the sport very well (why can’t you pass the puck across two blue lines anyway?). Figure 8.5 depicts data from the Kim and Trail (2010) project. As it shows, lack of knowledge about basketball, did not seem to be a major constraint for most of the people surveyed (people who were in the football team’s booster club). This was not surprising, because the people who were surveyed were sport fans in general and were at least somewhat knowledgeable about various sports. However, what we did find was for people in Group 2 (those that hadn’t attended in 2005-2006), lack of knowledge indirectly influenced attendance at games, through being a fan of basketball. That is, a lack

Internal Constraints to Consumption Mean Scores (1-7)

7 6 5 4

Never Attended Didn't Attend 2005-2006

3

Attended in 2005-2006

2 1 0 Lack of Team Success

Lack of Knowledge Lack of Someone to No Interest from Significant Others Attend With of Basketball

Subscales

Figure 8.5

of knowledge about the game of basketball indicated that the individual was less likely to be a fan of basketball in general. This was not surprising. And of course, if a person was not a fan of basketball then they were less likely to attend any women’s basketball games. In a different data collection from a university in the Northwestern U.S., I found that alumni of the university who had little knowledge about basketball as a sport did not intend on going to the men’s basketball games. This certainly makes sense. Why would you go to a game about which you had no understanding? In addition, I found that for the general public (those who had no association with the university), a lack of knowledge about the sport predicted being Figure 8.6 more influenced by promotions (Figure 8.6). That is, the less people knew about the

sport, the more likely they were likely to go to one game because of the draw of the promotions. Of course, the flip side of that is that people, who were more knowledgeable about the sport, were negatively influenced by promotions, and were less likely to go to the game. Copyright 2018 Galen Trail

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Internal Constraints Several years ago, my students and I also collected some data from fans of a men’s professional soccer team (Gallo, Yu, & Sharma, 2017), lack of knowledge was not impactful except in one instance. In general, all segments (Socials, Bandwagoners, Loyals, and Aficionados) did rated lack of knowledge very low, indicating that it was not a constraint. Lack of knowledge also was not negatively correlated with future attendance, future support of the team, watching the team’s games on TV, or pretty much anything else. However, in the Aficionados segment, lack of knowledge did negatively predict team identification. That is, as people in this segment indicated a higher level of lack-of-knowledge, the less likely they were to be a fan of the team. Based on the above research, lack of knowledge doesn’t seem to preclude people from attending games I the future, watching the team on TV, or supporting the team in general. Thus, marketers don’t really need to worry about it, right? Not necessarily, the problem with most of the above research is that it assessed people who were well-educated about the sport in general in each case, and typically well-educated about the team. Unfortunately, I don’t have a data set that evaluates people who have very little knowledge about the sport of reference. So maybe, lack of knowledge is a constraint for those who truly have a lack of knowledge. What a concept! Thus, marketers need to determine whether lack of knowledge is applicable to people that they are trying to market to. Of course, getting these people to tell you that they actually have a lack of knowledge, would be extremely difficulty, because they probably don’t have enough interest in the sport to fill out the survey. Lack of Worth. Lack of worth represents the idea that the individual does not perceive the team as being worthwhile or fan related behaviors as being valuable. Trail and McCullough (in review) developed this concept based on information from Pritchard, Funk, and Alexandris (2009) who rated the sport on a scale from valuable to worthless. However, they did not find that internal constraints in general had an impact on attendance or media consumption. Trail and McCullough applied it to an environmental sustainability campaign, finding that as the perception of the worth of being environmentally friendly decreased, participating in the sustainability campaign decreased as well. Obviously though, the concept of lack of worth can also can apply to the team itself. For example, if I don’t think that going to a Dallas Cowboys game is worthwhile, then I won’t go. This concept needs to be further developed and assessed specific to fandom and attendance though. Lack of Personal Interest. Lack of personal interest is similar to lack of worth, but although the individual may be aware of the team and think that attending a sporting event in general or a specific type of sporting event (e.g., a baseball game) may be worthwhile, the individual may not have any interest in attending a specific team’s game. Similarly, although someone may be interested in a team, they may not be interested in going to that particular game on that particular day. In the first instance, personally I have no interest in attending a NY Yankees game to watch the Yankees. Even though I have no interest in watching the Yankees, and thus no interest in attending, there may be instances where I would go to a game, for example if the Dodgers were playing and I happened to be in New York. In the second instance, maybe I typically do go to Mariners games, however, I know that Felix Hernandez is going to pitch tonight. I also know that in the last four games he has pitched atrociously and overall his yearly ERA is above 6.00. This makes it painful to watch a once-dominant pitcher self-implode. Therefore, I decide not to go to this particular game because I have no interest in watching something that painful. Previous research by Alexandris and Stodolska (2004) found that lack of interest did negatively predict intentions to participate to a small extent but Carroll and Alexandris (1997) found that it was the most powerful predictor of sport participation, but still not that impactful. Trail and McCullough (in review) also found that lack of interest was an important predictor of participating in campaigns.


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Internal Constraints Marketers are well-aware that many people are not interested in their product, so it is up to them to figure out how to make them interested. That’s pretty much what the whole rest of the book is about. Lack of Someone to Attend with. The internal constraint of not having someone go to the game with you reflects the social aspects of spectatorship. Kim and Trail (2007) determined that when people do not have anyone that will attend a game or an event with them, they typically will not go. From Figure 8.5 it is apparent that those in Group1 (those who attended a game) were less influenced by not having somebody to go with. This indicates that for those people who had never attended or rarely attended, this was more of a constraint than for those people who had attended, although there was not much of a difference. I have also found that as people’s perception that they do not have anyone to go with them to the game increases, the more likely promotions will have a positive influence on them (Figure 8.7). However, this seems to be more of a one-time event, which we discussed earlier when we talked about promotions, because the more they perceive the team doing promotions, the less likely they are to attend in the future.

Figure 8.7

Interestingly, Fink, Trail and Anderson (2002) determined that the gender of the individual did not really impact this constraint either; both men and women were influenced similarly by whether friends and family would go to a game with them. However, Fink et al. did find that spectators at women’s basketball games were more likely to have attendance influenced by friends and family than spectators at men’s basketball games. Friends seemed to have the greater influence of the two. If a women’s game attendee did not have a friend to go with them, they were less likely to go. This constraint does seem to vary by segment as well. My students and I surveyed students of a university with a men’s basketball team (Savage, Walker, Ross, & Lail, 2017). Using a cluster analysis, we determined that four segments existed: Loyals, Bandwagoners, Football Fans (these were fans of the football team but not the basketball team), and Socials. We found that the Bandwagoners segment and the Socials segment were impacted, but in different ways, while the Loyals segment and the Football Fans segment were not. For those individuals in the Bandwagoners segment, if they did not have someone to attend with, they would not support the team in the future at all. This constraint had the largest impact of any variable on future support by far. Those in the Socials segment differed; they were less likely to attend fewer games the next season if they had no one to go with, but they would still support the team in the future in other ways. Loyals, would attend, regardless of whether or not they had someone to go with. The Football Fans segment didn’t intend on going to games in any case, so having someone to go with them didn’t matter. What marketers can do, if they find that people are not coming to the games because they don’t have anyone to come with, is to match people up with those who are already coming to the game and are willing to bring someone else along. We did this with the college women’s basketball team fans. We Copyright 2018 Galen Trail

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Internal Constraints identified people in the basketball team’s booster club who would be willing to help in this endeavor by bringing people who were interested in coming to the game but didn’t want to go alone. Once we identified who was willing to help, we organized both groups (boosters and those who needed someone to go with) by geographical region and then where possible, also tried to match them by other psychographic variables (such as motives and lifestyles) and demographics (such as gender). We invited all parties to a pre-game function hosted by the athletic department and team, had the boosters swing by and pick up their designated “new fan”, and bring them to the function and the game. During the function, we had many activities where people got to meet other people, get to know them a little bit, just to make the “new fans” feel as comfortable as possible and make new friends that they might attend future games with, before watching the game with their booster hosts. The first function worked so well (generating over 60 ticket purchases by “new fans” for future games) that the athletic department held another function a month later with other potential “new fans” and from there generated even more ticket sales. Lack of Interest from Others. A similar internal constraint to a lack of someone to attend with, although slightly different, is a lack of interest from others in the activity, whether it is watching a game on television, going to the game, or being a sport fan in general. This differs from the previous constraint in that even though someone might go to the game with you, they have no interest in the game itself. This lack of interest in the activity certainly dampens any enthusiasm for the activity. For example, if I asked my dad to come to a basketball game with me, he might go because he wanted to spend time with me, but he would have no interest in the game itself. Thus, he would spend the whole time telling me about his work, complaining about the neighbors, and discussing his latest ailment; none of which I would want to discuss when I am trying to watch the game. So, because of his lack of interest in the game, I would not want to take him and be distracted from enjoying a good game. Instead, if I wanted to spend time with my dad I would do something that we could both enjoy, and I would not end up going to the game. As is apparent from Figure 8.5, there was a significant difference between those who attended games and those who rarely or never attended. The lack of interest in the activity (women’s basketball) was a much greater constraint for the latter two groups. We also determined that a lack of interest from others had differential impacts on different segments. For example, in the women’s professional soccer data (Nicefaro & Goobes, 2017), the Casual Fans segment was meaningfully impacted in a negative way. If their friends were not interested, then they were much more likely to not attend any matches the following year. Women’s Soccer Supporters, on the other hand, were not only negative impacted Copyright 2018 Galen Trail

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Internal Constraints by friends, but also by significant others and/or family. In addition, for this segment, a lack of interest from others also decreased their match satisfaction, if they did end up going. Obviously not a good situation for the team. Not surprisingly, people in the Loyals segment were not impacted at all by a lack of interest from others. However, we were very surprised with the results from the Family segment. We expected that this segment would be negative impacted, especially if their family was not interested in going or the game or the team. This was not the case though. There were no significant relationships between lack of interest from others and any attendance intentions, future support of the team, match satisfaction, or anything. I don’t have an explanation for this. For the Bandwagoners segment, their positive mood after the game decreased if they couldn’t share the good feelings of a win with their friends. Furthermore, Bandwagoners were also slightly impacted if their family was not interested in that they would intend to attend one fewer match. Surprisingly, the Socials segment was not impacted by this at all, except that if their friends weren’t interested, then their satisfaction with the outcome of the match decreased somewhat. I can’t explain why this was the only significant result. I expected that the Socials segment would be dramatically impacted by a lack of interest from their friends, family, and significant others, but they weren’t. This one is harder for marketers to deal with. If family or a significant other is not interested in the team or game, and it is a constraint that prevents the individual from coming to the game, then that is pretty hard to change. First, change is difficult because the marketer may not have access. Second, because even if the marketer can get access then the marketer needs to be able to move the whole family (and/or significant other) up the consumer pathway. One would think that because the individual is already a fan to some extent, there would have already been an impetus for family to be a fan as well. If family members haven’t become fans already, it will be difficult to change those feelings. Perceived Lack of Team Success. Perceived lack of success differs from the organizational constraint of having a losing team. The internal constraint, perceived lack of success, although related to the winning percentage, is not perfectly correlated. For example, highly identified fans of the Golden State Warriors might expect that their team should win the NBA championship in 2018-2019 (partially because they have done so three out of the last four years). If they don’t, the fans might perceive that the team is not successful even though they have a great winning percentage. This happened the year that the Warriors set the record for number of wins in the regular season in 2015-2016 by winning 73 out of 82 games played. However, since they lost to the Cleveland Cavaliers in the NBA Championship, some fans thought that the Warriors didn’t have a successful season. Similarly, when the Seattle Seahawks lost to the New England Patriots in the 2015 Super Bowl, the Seahawks’ fans thought that the season was not successful because of the one bad play at the end of the game that prevented them from winning the Super Bowl. Kim and Trail (2007) suggested that lack of team success might prevent people from attending games. They hypothesized that if a team was perceived to be have a lack of success, people would be less likely to go to the team’s games. However, apparently that is not necessarily the case as the information in Figure 8.5 indicates. None of the groups seem to be overly influenced by the team’s lack of success. Mayer, Morse, Eddy, and Love (2017), however, found that lack of success did prevent people from coming to intercollegiate volleyball matches in a U.S. college setting. Even so, lack of


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Internal Constraints success, did not explain a large amount of variance. Other research using lack of success as a potential constraint also hasn’t found that it is a large determinant of attendance behavior. In addition, examples abound in the professional leagues of people continuing to support a team that does not have on-field or on-court success. Consider that some people still attend San Diego Padres games even though they typically are not very successful, at least not since the 1990s – go figure. There was, however, an interesting finding from the women’s basketball data from the Southeastern school. Among the people that rarely came to a game, we found that if they perceived the team to be successful, then they would overcome the perceived constraint of having no one to attend with. Those responding reported they would attend games if the team were successful, even if no one attended with them, so they could be associated with a successful team. However, because the women’s team was not perceived as successful by this group of people they went to the men’s basketball games instead to get this association. This might lead us to the supposition, that people who were bandwagoners might be most likely to perceive that lack of success was a constraint to attendance intentions. So, we checked this out on a sample of results from a men’s professional soccer team (Gallo, Yu, & Sharma, 2017). We ran a cluster analysis and found four different segments, one of which was Bandwagoners. Perceived lack of success did not predict future support for the team in the Bandwagoners’ segment, nor in the Socials’ segment or in the Aficionados’ segment, but it did predict it in the Loyals segment which is contrary to current theory, but this was with a very successful team. We also tested it out on women’s professional soccer fans (Nicefaro & Goobes, 2017). We found six different segments in this data set, one of which was Bandwagoners. We found that perceived lack of success was not a constraint for Bandwagoners, nor was it a constraint for any of the other segments in terms of predicting future attendance. However, the Bandwagoners’ segment did rate (mean score) lack of success the highest of the six segments, although the mean was still just below the neutral point of the scale, indicating that they still thought the team was at least somewhat successful. So, perhaps the expected results would show up if the team wasn’t successful.


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Internal Constraints To a small extent this was accurate. We surveyed students of a university with a very unsuccessful men’s basketball team (Savage, Walker, Ross, & Lail, 2017). We found that all four segments (Loyals, Bandwagoners, Football Fans – these were fans of the football team but not the basketball team, and Socials) perceived that the team was not successful. However, lack of success only slightly constrained Bandwagoners and Socials from supporting the team in the future. Lack of success did not however decrease the number of games that any segment intended to watch or attend in the future. So, even though lack of success has been proposed to be a constraint that will prevent people from attending games, there has been limited research that shows that it actually does, regardless of the type of sport, team, level of success of the team, or segment of potential attendee. Overall though marketers do need to assess whether lack of success does impact whether their own potential attendees may not attend because it is a potential constraint. Supposedly, loyal fans will not be influenced by this constraint and thus, getting as many people as possible to be loyal fans is a desired goal as it increases ROI. In addition, marketing motivations other than success, would be a better option as noted elsewhere in this book. Lack of Time. Alexandris and Stoldolska (2004) identified a lack of time as having an indirect effect on intentions to participate in sport and Forsythe and Shi (2003) described lack of time as a potential barrier to consumption. Trail and McCullough (in review) found that lack of time definitely impacted participation in a sustainability campaign put on by a sport organization. Furthermore, Casper, Kanters, and James (2009) claimed that lack of time was a major constraint (only behind cost) for people that attended NHL hockey games. Season ticket holders perceived time as a constraint significantly more than single game ticket purchasers. Casper et al. created four items to measure lack of time: lack of time because of work, because of family commitments, because of other social commitments, and game times conflict with my schedule. My colleagues and I have typically called the first three commitments and I’ll discuss what we found below. However, Casper et al. showed that lack of time due to work, was perceived as the largest time constraint by the NHL fans surveyed. Lack of time because of family commitments was second, closely followed by game times conflict with schedule. Marketers need to take into account that many people these days feel constrained by a lack of time. Most people are very busy, so marketers need to show how the time commitment is not going to be a problem by either showing how it can be reduced or by showing how the time is well spent, either through fulfilling a need for relaxation or need for a socialization, or perhaps a need to spend time with family. Marketers need to identify what is going to be most effective for each segment (or individual if possible) in reducing the feeling that they don’t have time to come to a game. Commitments to Others. Other potential constraints are commitments to other people and other organizations; for example, work commitments, school commitments, or commitments to friends. There was a slight negative relationship with work commitments and attending the basketball game for the professional women’s basketball fans; work did seem to prevent some from going to the game. These same fans also indicated that sometimes commitments to friends prevented them from going to a game, but this was not a large negative influence. For the collegiate women’s basketball set, these same issues were moderate constraints; however, school commitments were also a factor as well. However, in the data from the college students and the men’s basketball team (Savage, Walker, Ross, & Lail, 2017), commitments to friends were not constraints for three of the four segments (Loyals, Bandwagoners, or Socials). For Football Fans (who weren’t basketball fans), commitments to friends had a very slightly Copyright 2018 Galen Trail

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Internal Constraints negative correlation with the number of men’s basketball games intending to watch for the rest of the current season. In the men’s professional soccer data set (Gallo, Yu, & Sharma, 2017), no commitments (to work, to family, to friends), had any impact on intentions to attend, support the team, or watch games on TV for any of the segments (Socials, Bandwagoners, Aficionados, or Loyals). As noted above though, Casper et al. (2009) suggested that lack of time due to work, family, or social commitments, could be constraints, but Mayer et al. (2017) didn’t find that commitments really were constraints. Marketers can deal with some of these commitments to others by trying to convince them that the commitments can be fulfilled by attending a game. For example, sometimes family commitments can be met by bringing the family to a game. Obviously not always if the family commitment is to go camping; most sport organizations don’t allow camping on the field after a game, but maybe they should 😊. Similarly, sometimes commitments to friends can be fulfilled by going out to dinner and the game or even eating dinner at the game, assuming the food is good enough. Some teams have created spaces where people can get together with their friends a d drink and eat before, during, and after the game. Some of the other commitments can’t be ameliorated by marketers, such as work or school commitments, unless you can convince your boss that you are working while drinking a cold one at the game. Poor Physical Health. Alexandris and Carroll (1999) suggested that poor physical health could be a constraint for participating in sport or recreation. Theoretically poor physical health could also prevent people from attending games. Pritchard et al. (2009) confirmed that some people did list poor physical health or being tired as a constraint. However, the results did not support that these aspects actually had an impact on attendance or media use. I’m not aware that anyone else has shown that physical health has a negative impact on sport consumption behavior, but it is certainly possible, and it is logical. However, there isn’t much marketers can do if people have health problems that are preventing them from coming to games. I don’t mean disabilities that aren’t met with ADA compliance, as we discussed in the chapter about organizational constraints, those can and should be addressed. If sport organization employees are aware that a fan is not able to make a game (or games) because they are sick, then a nice get-well note would certainly help build a quality relationship. Traffic. As noted earlier, traffic is an external environmental constraint. However, the perception that traffic will be bad (whether it is or not) could potentially be an internal constraint because it is the perception and not necessarily reality. Traffic can often be a large constraint for attendance for some people. If they are driving to the game and perpetually get stuck in traffic, many people may decide not to go. For example, I was headed into a University of Washington volleyball game on a Friday night. It was the NCAA regionals with the first game at 5PM with the UW match to follow at 7PM. I left my office at 4PM for a trip that typically takes 45 minutes. At 7PM I was still stuck in traffic and Copyright 2018 Galen Trail

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Internal Constraints missed the start of the UW match. I was more than slightly peeved to say the least and will not try that again. Because of this experience, my perception is that traffic is always bad when I am trying to get to that venue at that time of day. Thus, I have quit going to volleyball matches there on weekdays because of it. We tried to determine if traffic had a negative impact on attendance for the women’s professional soccer team attendees (Nicefaro & Goobes, 2017). Across all of the segments (Casual Fans, Women’s Soccer Supporters, Loyals, Family Focused, Bandwagoners, and Socials), not a single segment indicated that traffic was a constraint. For this team, it might not be, as about half of their home games were played on a weekend that year. Traffic problems are typically outside of the sport organization’s control except in terms of modifying game scheduling to avoid problem traffic times. Many organizations have moved weekday games back a half hour to 7:30PM from 7:00PM to allow spectators to deal with potential traffic problems. Zhang (1998), however, found that although people prefer the 7:00PM time slot, moving the game to &:30pm did not affect the number of games that they intended to attend. It was not apparent whether these preferences had anything to do with traffic though. Again though, it is the perception that traffic is bad which is the issue that marketers need to deal with. Thus, suggesting alternatives that might reduce the impact of traffic would be a start. Many teams provide pages on their websites offering suggestions on how to get to the games as easily as possible. In addition, sport organizations also partner with transportation services, both public and private, to make accessing the game easier. Cost. In the internal organizational constraints section of Chapter 6, I discussed Price as an organizational constraint. Cost is the flip side of price. Price is determined by the organization. Cost is the perception of those prices by the consumer. When surveying consumers, they are focused on cost, whether you call it price or cost. So, the results will be similar. However, the perception of the price by the organization and the perception of the cost by the consumer will probably be quite different. Cost is probably one of most challenging potential constraints sport consumers face. The cost of tickets has outpaced inflation considerably as I pointed out in Chapter 1. The overall cost of attendance has also increased dramatically. Overall cost includes the ticket price, concessions, parking, programs, merchandise, travel expenses, and other miscellaneous expenses. The impact of economic variables on attendance has been well documented. For example, ticket costs have been shown to have a negative relationship with attendance (Fink, Trail, & Anderson, 2002; Hansen & Gauthier, 1989; Mauricio & Armstrong, 2004; Zhang et al., 1997). Typically, as the cost goes up, more people perceive it as a constraint. In the men’s professional soccer data though (Gallo, Yu, & Sharma, 2017), the cost of attending the game wasn’t a constraint for any segment, but the cost of transportation to the match was perceived as a constraint for all segments, to one degree or another, and decreased the number of matches they intended to attend during the upcoming season. In addition, the cost of concessions was also a constraint, but only for the Socials segment. For the women’s professional soccer attendees (Nicefaro & Goobes, 2017), the Casual Fans segment indicated that they were going to attend fewer matches in the next season due to attendance costs. However, none of the other segments thought that costs of attending the match were constraints. Costs of concessions or merchandise didn’t negatively impact attendance for any segment. The attendees of the college women’s basketball games actually reported the cost of attending was not a constraint at all; in fact, it tended toward being a motivator because of the low cost of a ticket and free parking. Although there were differences between the attendees and the other two groups, the latter still did not view this as a potential constraint. Similarly, for the Socials segment of the students going to the men’s college basketball games (Savage, Walker, Ross, & Lail, 2017), the cost of going to the game actually had a positive correlation with supporting the team in the future, but not with future attendance. Copyright 2018 Galen Trail

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Internal Constraints Although increased costs can be a constraint, sometimes if the cost of the ticket is too low, that might be perceived as a constraint as well. The Cincinnati Reds figured this out the hard way during the last year in their old ballpark. Attendance was down because the team was not doing well and the new stadium was yet to be finished. The Reds decided that they would try to get people in the stands and at least earn some parking and concession revenue. The team decided to price the outfield bleacher seats at $1. The tactic backfired. People perceived that the Reds must be really bad and desperate if tickets could be purchased for only one dollar. The results was that no one bought the dollar tickets and attendance dropped further. The difficult economic environment during 2008-2010 resulted in many teams dropping ticket prices in order to maintain attendance levels, but spectators didn’t necessarily perceive that the costs had decreased because many families and individuals had been dealing with lower income and higher living costs. Thus, even more people perceived the costs to be prohibitive and definitely viewed them as potential constraints. Marketers and managers need to either cut ticket prices even more or do a better job of showing added value or creating added value if they maintain the ticket price level.

Bringing it all together Kim and Trail (2007) examined the hierarchical model of motivators and constraints as I mentioned earlier in the chapter, in an attempt to determine whether people could be segmented by motivators and constraints. In addition, they wanted to see whether attendance intentions really differed by these aspects. In Figure 8.8, these relationships are shown. This data set included almost a thousand people, many of whom had never attended a collegiate women’s basketball game before. The top box indicates how many people were in the analysis (N=963) and the average number of games they expected to attend (M = 2.46). The average number of games to be attended was small considering that there were 16 home games in the up-coming season. Trail and Kim (2011) divided the group by the level of internal motivation. Those that scored above the midpoint on the scale were designated to be in the High Internal Motivator (IM) group (n = 394), whereas those that scored at the midpoint (neutral) or lower were put into the Low Internal Motivator group (n = 569). The average number of games each group expected to attend was compared. Those that were in the High IM group indicated that they would go to 3.26 games on average, whereas those in the Low IM group indicated that they would only go to 0.45 games on average; a significant difference. This shows if marketers know which people score highly on Internal Motivators, they can focus on those aspects when marketing. The next level of the diagram shows (on the left side) that we then split the High IM group into two groups by testing them on Internal Constrains (IC). The High IC group (n = 124) was then compared to the Low IC group (n = 270). When the number of games people expected to attend was compared, it Copyright 2018 Galen Trail

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Internal Constraints was apparent that people who felt internally constrained intended to attend fewer games (M = 2.18) than those who did not feel constrained (M = 3.75). Thus, even those these people were highly internally motivated to go to games, the perceived internal constraints would still have a negative effect on the number of games that they would go to. Marketers and managers need to determine what the perceived internal constraints are for the latter group and see if they can be ameliorated. On the same level of the diagram, but on the right-hand side, we split the Low IM group by their scores on Internal Constraints as well. Those in the High IC group (n = 335) intended to go to fewer games (M = .18) than the Low IC group (n = 234) expected to attend (M = .82). This shows that even when people are not motivated to go to games, if perceived constraints are removed or not present, people are more likely to come, but still not to very many games. These are segments that marketers should not put a lot of effort into trying to get to come to games. If we move to the next level where External Motivators (EM) are introduced, we can compare Group 7 (G7) to Group 8 (G8). G7 is the High IM-High IC and now High EM group (n = 60) in which the members intend to go to 2.88 games. Although this is higher than the 1.53 games that the G8 group (High IM-High IC, but Low EM; n = 64) intends to go to, it is not a significant difference because neither group is very large. This indicates that External Motivators such as advertising and promotions may not be influential enough to help overcome the perceived internal constraints for this set of people (G7) and certainly not for people that are not driven by External Motivators (G8). If we examine Groups G9 (n = 170) and G10 (n = 100), the effect of External Motivators is readily apparent. G9 is highly motivated by external motivators and intends to go to considerably more games (M = 5.26) than the G10s who scored lowly on External Motivators and only intend to go to 1.18 games. This shows that External Motivators can have a significant effect on top of high internal motivators and low internal constraints. Marketers need to determine how to incorporate both internal and external motivators to get people to go to games. By looking at Groups G13 and G14 it is apparent that when people are not motivated internally and perceive high internal constraints, the external motivators do not entice people to intend to come to more games. The 207 people in G13 who scored highly on external motivators, still only intend to come to 0.27 games. The 128 people in G14, who scored low on external motivators, intend to come to 0.18 games. This is not a significant difference and indicates that a lack of internal motivators and high perceived internal constraints trumps any external motivators. When Groups G11 and G12 are compared, however, there is evidence that external motivators can be successful in drawing people to the games, although not many games. People in G11, although they had no internal motivation to come to the game, also did not perceive any internal constraints to coming. Thus when presented with external motivators their intentions changed and they decided to come to at least one game. The people in G12, however, were not swayed at all by the external motivators and still have no interest in coming. The last row on the diagram depicts the influence of the external constraints. Each of the Groups G7 through G14 was split in two by their scores on perceived external constraints. In general there were no significant differences shown because of the small sample sizes. However, the variation is apparent just by examining the number of games that each group intends to attend across all of the groups. This indicates that people can be segmented by a combination of motivators and constraints, both internal and external. For example, the High Internal Motivator, Low Internal Constraint, High External Motivator, Low External Constraint group (G20) intends to go to 5.48 games, whereas G25 which is the Low Internal Motivator, High Internal Constraint, Low External Motivator, High External Constraint group, only intends on going to 0.16 games. Copyright 2018 Galen Trail

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Internal Constraints Figure 8.8

IM (Group 1) H (n: 394) M: 3.26

F(1,122) = 1.39 p= .24 EM (G8) L(n: 64) M: 1.53

IM (Group 2) L (n: 569) M: .45

F(1,961) = 88.01, p = .00

F(1,392) = 4.58, p= .03

IC (G3) H (n: 124) M: 2.18

EM (G7) H(n: 60) M: 2.88

IM = Internal Motivator IC = Internal Constraint EM = External Motivator EC = External Constraint H = High, L = Low M = Mean

All (N: 963) M: 2.46

F(1,567) = 16.30, p= .00

IC (G4) L (n: 270) M: 3.75

IC (G5) H (n: 335) M: .18

F(1,268) = 23.99, p= .00

F(1, 333) = 3.31, p= .07 EM (G13) EM (G14) H(n: 207) L(n: 128) M: .27 M: .13

EM (G9) H(n: 170) M: 5.26

EM (G10) L(n: 100) M: 1.18

IC (G6) L (n: 234) L: .82

F(1,232) = 6.89, p= .01 EM (G11) H(n: 104) M: 1.36

EM (G12) L(n: 130) M: .40

EC(G15) EC(G16) EC(G17) EC(G18) EC(G19) EC(G20) EC(G21) EC(G22) EC(G23) EC(G24) EC(G25) EC(G26) EC(G27) EC(G28) EC(G29) EC(G30) H(n: 16) L(n: 44) H(n: 18) L(n: 46) H(n: 15) L(n: 155) H(n: 23) L(n: 77) H(n: 39) L(n: 89) H(n: 49) L(n: 158) H(n: 22) L(n: 82) H(n: 35) L(n: 95) M: .88 M: 3.61 M: .46 M: 2.13 M: 2.93 M: 5.48 M: 1.73 M: 1.01 M: .21 M: .29 M: .16 M: .12 M: .73 M: 1.54 M: .94 M: .20 F(1,58) = 1.83, p= .18

F(1,62) = 1.80, p= .19


F(1,168) = 1.41, p= .24

F(1,98) = .91, p= .34

F(1,126) = .35, p= .58

F(1,205) = .26, p= .61 Page 250

F(1,102) = .72, p= .40

F(1,128) = 9.98, p= .00

Internal Constraints Marketers and managers need to realize that for the groups that intend on going to very few games, spending money trying to change their minds about coming is a waste of resources – temporal and financial. A better strategy is to focus on the groups that will be impacted by things that can be controlled; i.e., removing perceived internal constraints, increasing external motivators, and removing perceived external constraints. In addition, once the groups are segmented, then within each group the motivators and constraints that have the greatest impact can be identified and used to increase attendance within that specific group. However, none of this can be done without a considerable amount of research to identify each of these aspects and to track individuals when possible. In addition, as I noted at the beginning of the chapter, this model can work in some instances, but there are many instances where the interactions among motives and constraints are much more complex and this model would not suffice. In easy cases, this model works well and creates actionable information if each person is identified. That is, if marketers know who the 44 people are in Group 16, then external motivators might be identified and increased for those people. Unfortunately, most issues are not this simple, but doing market research will help marketers and managers to fill their arenas and stadiums. Renton RoadRunners Example Let’s look at our fictitious RoadRunners example and see what customer constraints were applicable and how their marketers might need to deal with them. Remember that the data below is real, it just happens to be from a different team (obviously) other than the made-up RoadRunners.

Lack of Awareness

Lack of Knowledge

Lack of Worth

Lack of Interest

Lack of Someone to Attend with Perceived Lack of Success


Customer (Internal) Constraints Slightly under 20% of the regional population were totally unaware of the Renton RoadRunners. 1 However, within the Renton area, only slightly more than 10% were unaware. Marketers still need to get the word out better. Unfortunately, there is limited marketing budget, so they need to use social media and the team website. In addition, they need to convince the Seattle Times (regional newspaper) and regional TV stations to give them better coverage. Difficult to do. Expand WOM. 26.2% of those surveyed indicated that they really didn’t understand the technical aspects of basketball, 21.6% said they didn’t understand basketball strategy and the same percentage said that they didn’t understand the rules of the game. 2 Team officials need to provide more and better information about the team, the game, the rules, the strategies, etc., on the website, through communications, on social media, etc. Only 4.3% thought that attending a RoadRunners game was not worthwhile, but 7% thought that the league was not worthwhile.2 Marketers don’t need to be overly concerned with this small percentage of people, but realize that these people are in their database, and thus have some connection already to the team. The percentage in the general public would be considerably greater. In addition to the 20% that were unaware, 40% more in the region had absolutely no interest in the team. 1 The lack of interest from people in the greater Seattle area is understandable because this is a minor league team and it is located in Renton, which is not a destination for pretty much anyone who doesn’t live there. Within Renton itself, the lack of interest level is much less with only an additional 25% beyond those who were unaware. Only 5% indicated that this was a constraint.2 This is such a low percentage, that the marketers decided not to worry about it unless it started increasing or if other constraints which had larger impacts were already dealt with. 10% perceived that the team was not successful, and an additional 8% weren’t sure. 2 With the new coach, this percentage was most likely going to change. Page 251


Lack of Interest from Others

Lack of Time

Commitments to Others

Poor Physical Health

Perceived Costs

Perceived Traffic

The problem was that the marketers had no idea which way it would go. Regardless, they were going to focus on other things other than success. Approximately 10% said that their family wasn’t interested in going to a game with them, and 13% said that their significant other wasn’t interested, but an additional 17% weren’t sure. 17% said that their friends weren’t interested and an additional 18% weren’t sure.2 These numbers weren’t that high and they are tough to fix, so this was down the list on things to address. Slightly over 19% indicated that lack of time was a constraint. 2 Management talked about moving the games back to 7:30pm from 7:00pm, but decided not to since almost 70% of attendees came with family and most of those had kids less than 13 years old. Games typically last 2.5 hours and the average commute time was 30 minutes. So moving the games to 7:30pm would put people getting home at 10:30pm on a school night. Not a good idea, so instead the marketers emphasized that coming to the game was a great opportunity to spend time with the family. Around 3% indicated that commitments to others prevented them from coming to games. 2 Marketers decided that this wasn’t a big enough constraint to really worry about. Less than 1% indicated that health reasons prevented them from coming to a game. 2 Again, not big enough to worry about, but management wanted to make sure that if anyone heard about a fan being sick, that they would receive a get-well message from the team. Only 19% thought that the cost of going to the game was not affordable. However, 36% thought that season tickets were not affordable, but only 20% thought single game tickets were not affordable. 2 Management was willing to live with the percentage of people that thought the game was unaffordable, but as you will recall from the Organizational Insight chapter, there were lots of empty seats that should have been going to season ticket holders. So, they decided to make a new seating price plan and adjust prices lower in some areas. Only 24% thought that traffic was a constraint. 2 The biggest part of this constraint, based on comments on the survey, was the trains causing delays both coming to the venue and leaving after the game. Considering that the tracks were just a block from the parking lot, this was a big issue. The city didn’t have money to build overpasses over the tracks, nor to build other roads that would provide alternatives. The team talked to the freight train company, but they couldn’t (wouldn’t) adjust train schedules just for a game. As of now, there is no solution, although it has been suggested that an alternate parking lot on the other side of the tracks with a footbridge across the tracks might be feasible. 1 Data gathered from a random survey of people in the region. 2 Data gathered from people in the team’s database.

What we can learn from the RoadRunners example, is that the team needs to pick and choose which internal constraints they might be able to ameliorate and which ones they can’t. In addition, they need to look at the ROI for each. If it is a cheap and simple fix, with a good return, then they would be stupid not to implement. On the other hand, if the fix is expensive, regardless, it may not get done just due to a lack of money. Those in the middle need to be ranked and then addressed as resources permit. Copyright 2018 Galen Trail

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Internal Constraints Summary A thought to close with is that the study of consumer behavior has focused to a great extent on understanding what consumers do and the variables that influence consumption (i.e., motivation). There has been much less effort to understand what prevents or constrains people from consuming a particular product. Ultimately what we need is to understand both motivators and constraints. As research continues and practitioners gain a better understanding of both topics, we will all better understand sport consumption behavior.


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Demographics


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Demographics

Chapter 9 Demographics

Introduction One way that sport organizations, especially professional sport franchises and leagues have tried to segment their market (Chapter 13) is by trying to access different subcultures, typically based on demographic profiles of potential attendees. Two prominent attempts are marketing to ethnic groups and marketing to women (who are typically underrepresented as fans in most sports). For example, the Texas Rangers (MLB) have had at least one Hispanic Heritage night a season for the last 15 years. In the past, the Rangers had a pregame concert by a local band Grupo Sueño and postgame fireworks. Hispanic Heritage Night has also included Mariachi Michoacan, a Latin Music Ensemble from a local high school, and Ballet Folklorico de Fort Worth performing on the concourse prior to the start of the game. In addition, Hispanic players and coaches Copyright 2018 Galen Trail

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Demographics from both the Rangers and the opposing team are honored before the game with a video, awards, and presentation of flags that represented the players' native countries. The first 25,000 through the gates that night typically receive a poster commemorating the event. The Philadelphia Phillies (MLB) do something similar and a little more politically correct by celebrating Latino(a) culture with one of the most popular heritage theme nights in Major League Baseball: Goya Latino, a Latino family celebration with food and entertainment. Both the Atlanta Hawks (NBA) and the Atlanta Braves (MLB) have Asian Heritage nights in which different Asian organizations are present in the venue to pass out information and to sponsor the events. In past Braves games, the Hung Sing Kung Fu Group of Georgia Tech presented a Chinese Dragon Dance and martial arts demonstration, and the Hiko-No-Kai Japanese Dance Troupe from the Onoe Institute performed traditional dances of Japan during pre-game festivities. Furthermore, the Braves held a silent auction that benefited the Asian-American Heritage Foundation. The San Francisco Giants (MLB) probably do the best job of trying to reach a multitude of ethnic groups. In 2015 they had the following Heritage Nights: Irish, Korean, Filipino, Japanese, Chinese, Portuguese, Italian, Native American, Jewish, Polynesian, and African American. One year they did a newly designed Irish Night themed Giants beanie. They also have had a Jewish Heritage Night with a limited-edition bobblehead, based on the famous "Rally-Rabbi." Joe Eskenazi reported that when Craig Solomon wore his Hebrew and Magen David-emblazoned Giants cap people were constantly coming up to him and asking how they could get one. Solomon, who is the season ticket manager for the San Francisco Giants, and Jewish, told everyone that the only way they could get a cap was to attend the Giants game against the Cincinnati Reds. A number of area synagogues and Jewish institutions set up ‘nights out’ at the game. The Giants also Copyright 2018 Galen Trail

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Demographics did a Native American Heritage Day in which they had music and dancers performing in Willie Mays Plaza and on the field before and after the game, and an African-American Heritage Night in which the ticket package included a specially designed African-American Giants themed shirt. However, not all of the focus is on ethnic groups, many leagues and teams have been targeting women as well. According to Nielsen's 2013 Year in Sports Media Report, most male professional leagues have a female fan TV viewing base of less than 37 percent. Over the last decade leagues have always claimed a higher percentage of their fans are women (we question that) but increasing the size of the female fan base is certainly important. Historically leagues have focused on educating women about their sport. Many teams have staged training sessions for a particular sport and billed such events as Football 101 or Baseball 101 and focused on women. A challenge with hosting these “fundamental courses” is they are sometimes seen as demeaning to women. If you were to read about a Football 101 course in your local newspaper advertised as Ladies Night, you might assume the course is for women that are stupid and have no knowledge of football. That is not the impressions teams want to make. In order to create interest in the “101” courses, teams have changed their marketing ploys. Some teams, for example, hold brunches and fashion parties, Girls Night Out ticket promotions, and tennis tournaments. One team in particular, the Baltimore Ravens an NFL franchise, has developed Club Purple. This is a girls-only affinity club that offers free memberships to women. Spanberg reported that women receive merchandise discounts, a newsletter, invitations to special events, and the opportunity to try offers and samples from sponsors whose target audience is women. The leagues and teams believe if they develop a larger fan base among women, they will experience an increase in revenues. The anticipated increase is based on the presumption that women will spend money to purchase team merchandise, tickets, etc., because women control 80 percent of household buying decisions, according to the CEO at The TrendSight Group, Marti Barletta. Women typically do all the household planning of family activities, especially when there are kids in the house. Thus, as Spanberg noted, if the teams and leagues can convince women to attend games, watch games on TV, and buy merchandise, it may have a generational impact on the kids. As I discussed in the fan socialization chapter, parents, and now perhaps moms as well as dads, may socialize children into the role of consumer of spectator sports.


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Demographics Subcultures as Demographics As is evident from the Figure 9.1 Revised Structural Model of Sport Consumer Behavior (Figure 9.1), the demographics of the customers are part of the customer environment. We discussed some demographics as an aspect within culture in the external environment back in Chapter 3. Here though, I am focusing on the demographics of the attendees and fans. In addition, I am treating the demographic aspects as subcultures, within the culture of being a fan or spectator. Many subcultures exist, and sport marketers and managers have segmented sport fans and spectators by some of the categories that are listed below (Figure 9.1) and discussed in the next sections. Although there may be an infinite number (not really, but sometimes it seems that way) of subcultures and categories within those subcultures I am limiting our discussion to the following: nationality, race, ethnicity, religion, geographic and region, age, sex, sexual orientation, household income, occupation, and education. A few of these are depicted in Figure 9.2 so that Figure 9.2 you can get an idea of how these subcultures can be further broken down into smaller categories. Nationality Although in previous research nationality has been used as an all-encompassing concept to incorporate variables such as ethnicity and culture (Sojka & Tansuhaj, 1995), using a strict definition, nationality refers to a Copyright 2018 Galen Trail

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Demographics membership with a specific nation and deals solely with the geographical locale of birthplace (Phinney, Horenczyk, Liebkind, & Vedder, 2001). Through four critical observations, Dann (1993) suggested the need for research to look beyond nationality and/or country of residence for the purpose of market segmentation. Due to (a) multiple nationalities, (b) disintegration of political order within countries (e.g., Yugoslavia, South Africa), (c) countries comprised of multiple waves of immigrants, and (d) multiple cultural experiences within one country, Dann argued nationality should not be used as the sole variable to account for behavior. He proposed alternative factors to account for the differences among tourists including culture, personality, roles, social class, and lifestyle. Conversely, Pizam and Sussman (1995) viewed nationality as one variable in determining an individual’s behavior and showed that perceived nationality traits differ in terms of behavioral characteristics among four nationalities. However, based on Dann’s argument that nationality typically is not sufficient in explaining why people consume products or goods, perhaps consumption behaviors are better determined by racial identity, ethnicity, culture, or ethnic identity. Very little research has been done examining differences between nationalities referent to sport consumption. Won and Kitamura (2007) found that Japanese fans differed from Korean fans on a variety of motives, with appreciation of physical skills, vicarious achievement, and entertainment being more important to Japanese fans and drama being more important to Korean fans. However, although these differences were significant, they were not large enough to market to these two groups differently. Summers and Morgan (2003) found that Americans directly and indirectly consumed more sport than either Malaysians or Australians. Malaysians had more indirect consumption than Australians, and Australians had more direct consumption than Malaysians. Again, although these differences were significant, because the amount of sport consumption they predicted was so small, the differences between nationalities were not meaningful. This indicates to marketers that segmenting based on nationality, and thus creating separate marketing plans, would probably not be a good idea unless marketers determine that specific to their own market, substantial and meaningful differences exist between nationalities. Race and Racial Identity Due to the historical context and discrimination that is associated with the concept of race, numerous and varied definitions, strong convictions, and a lack of detailed explanations have been connected with research on race or racial identity. According to Cokley (2005) and Phinney (2005), various researchers have used the terms racial identity and ethnic identity interchangeably with a disregard to their different meanings and implications. Tonkin, McDonald, and Chapman (1996) viewed biology as the primary concern when defining race, whereas Cokley defined race as a function of the racial identity resulting from minority status and ‘developmental challenges’ and argued that ethnicity and race should be studied as separate but related constructs. Alternatively, Phinney (1996) viewed race as a component of ethnicity: that is, ethnicity encompassed race. In terms of identity, and in particular ethnic identity, racial identity is a distinct concept “constructed on the basis of one’s appearance and the way in which others respond to that appearance, together with the history and meaning in society of the group associated with that appearance” (Phinney, 2005, p. 188). Distinctions between racial and ethnic identity exist and to some extent an understanding of culture may help understand the distinction. Copyright 2018 Galen Trail

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Demographics Little research in the sport Figure 9.3 consumer area has investigated the Race aspects of race on sport consumption behaviors, but most of it is Black White Hispanic descriptive. The Year in Sports Media Report (2013) does give TV viewing NHL 3% 92% 2% demographics by race for each of the NFL, NBA, NHL, and MLB (Figure 9.3), MLB 9% 83% 9% but totally ignores anyone who isn’t black, white or Hispanic. NBA 45% 40% 12% In addition, Scarborough Sports Marketing research has shown NFL 15% 77% 8% that African American men are more likely than the overall population in the U.S. to be avid fans of many sports. For example, although 41.6% of African American men are avid fans of the NFL (Figure 9.4), the “index” column tells us that they are 69% more likely to be avid fans than the rest of the population. Even though only 5.5% of African American men are avid fans of the WNBA (Figure 9.4), they are 244% more likely to be avid fans than the general U.S. population. The only sports that African American men are less likely to be avid fans of than the rest of the population are women’s professional golf (the LPGA), ice hockey (the NHL), auto racing, specifically NASCAR, and the Olympics. In the second table from Scarborough Research (Figure 9.5), we can see that only 8.6% of NFL fans are African American men, but 17.5% of WNBA fans are African American men. Figure 9.4 Figure 9.5


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Demographics Armstrong (2002) has examined racial components in her research and has found that sport consumption motivations differed for African American consumers compared to Anglo-American consumers. African Americans’ motives for sport consumption were explained by different constructs (i.e., group recreation) than Anglo-Americans (i.e., entertainment), but in terms of the meaningfulness of the differences, they were not great (i.e., the effect sizes were small). Zhang, Pease, Hui, and Michaud (1995) found that Blacks were affected by promotions the most, followed by Hispanics, and then Asians and Whites. Similar results were apparent for the effects of schedule convenience on attending games. No significant racial group differences were apparent on the influence of the home team appeal or visiting team appeal. Although differences were apparent of the influence of promotions and schedule convenience, again the differences were small. Furthermore, Armstrong (2002) found that no differences existed between Blacks and Whites on sport consumption variables. In the women’s professional soccer data (Nicefaro & Goobes, 2017), there were no differences on attendance, merchandise purchasing behavior, or on any brand associations, again indicating that segmenting the market based on race would typically be a waste of money. Ethnicity As with race and culture, ethnicity has numerous definitions of varying degrees of complexity (Phinney, 2000). Ethnicity is a term that can be used for groups of individuals within the minority status that are physically or culturally distinct from the dominant cultures in any given society. Moreover within the field of global marketing and advertising, ethnicity can be seen as an overall term that replaces or encompasses other identifiers such as race, religion, language and nationality as a method to establish the social identity of a group (de Mooij, 2004). Furthermore, ethnicity refers to a subgroup within a larger context, such as a nation or society, wherein the subgroup claims a common ancestry and a shared aspect of one’s culture or cultural origin (Naylor, 1997; Phinney et al., 2001). Ethnicity may also apply to a group or a subgroup of a nation that declare a common ancestry with “one or more of the following elements: culture, religion, language, kinship, and place of origin” (Phinney et al., 2001, p. 496). Ethnicity encompasses culture; furthermore, it contains the aspect of minority status and race. Individuals may feel part of an ethnic subgroup, i.e., feel that they identify with other members who have these shared experiences, feelings, and beliefs. An individual’s identification with an ethnic group or ethnicity is regarded as one’s ethnic identity (Bernal, Knight, Ocampo, Garza, & Cota, 1993; Isajiw, 1992; Phinney et al., 2001). Isajiw (1992) defined ethnic identity as the perceived relationship that an individual has with the ethnic group and both the inward and outward dynamics with that group. Furthermore, Bernal et al.’s (1993) definition of ethnic identity included both the sense of personal ownership and knowledge of one’s ethnic group. Researchers have stated the need for studies to investigate additional aspects of ethnic identity “including self-identification, feelings of belongingness and commitment to a group, a sense of shared values, and attitudes toward one’s own ethnic group” (Phinney et al., 2001, p. 496). In addition, previous authors have posited that ethnic identity incorporates “various aspects including a sense of belonging, positive evaluation of the group, preference for the group, ethnic interest and knowledge, and involvement in activities associated with the group” (Phinney, 1996, p. 923), sense of shared values, ethnic origin, (Naylor, 1997), and positive attitudes toward the group and commitment to the group Copyright 2018 Galen Trail

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Demographics (Phinney et al., 2001). Positive attitudes and a high level of commitment are necessary precursors for ethnic identity formation. I believe that ethnic identity is both the formation of, and cognitive commitment to, one’s ethnicity. As I have depicted in Figure 9.6, the process of ethnic identity development starts with the ethnic origin. This origin typically creates Figure 9.6 knowledge of one’s ethnicity and some level of interest in it. The individual may develop similar values to those that are common to the ethnic group, which may lead to positive attitudes toward the group. If the individual feels positively toward the group, it is likely to lead to positive evaluations of the group and a preference to maintain group membership. If the individual is accepted by the group, then feelings of belonging increase, which lead to cognitive and affective commitment to the group. As the individual becomes committed to group membership, involvement in group activities increase. It is possible for individuals to identify with more than one cultural group though (Tsai, Ying, & Lee, 2000) and thus have more than one group identity. For example, an individual born in the United States with a mother born in Mexico and a father born in Cuba may identify with both the Mexican culture and the Cuban culture, but also may identify with the dominant culture in the United States. In summary, interactions with numerous groups (e.g., family, social groups, environmental groups) play a role in transforming an individual’s self identity and ethnic identity. If an individual who is identified with an ethnic group is also identified with the dominant culture/societal group (e.g., the culture in the United States), this individual’s level of identification with

both groups may have an influence on identification as a sport fan in general and identification with certain sports (Pons, Laroche, Nyeck, & Perreault, 2001). Harrolle and Trail (2007) found that an individual’s level of ethnic identity had no affect on sport fandom for particular sports (American football, baseball, basketball, and soccer). However, they did find that for Latinos in the Southeastern part of the United States, ethnic identity had a significant and negative effect on their identification with hockey; as the level of ethnic identity increased, the level of identification with hockey significantly decreased. Copyright 2018 Galen Trail

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Demographics Sex as a Subculture Historically males differ from females on some types of sport consumption, for example, more males than females (in the general population) attend sporting events (Schurr, Ruble, & Ellen, 1985) and more men are season ticketholders than women (Pan, Gabert, McGaugh, & Branvold, 1997). Gantz and Wenner (1991) found that males had more interest in TV sports, more knowledge of TV sports, spent more hours watching TV about sport, more time reading newspaper sport pages, and spent more time watching sport telecasts in 1987. However, Kahle, Kambara, and Rose (1996) found Figure 9.7 no differences on the number of games attended between male and female fans. It seems though, that males tend to be more avid fans than females across a wider variety of sports, although there are exceptions. Based on research from Scarborough Sports Marketing reported in the SportsBusiness Journal back in 2007 (Figure 9.7), 60% of women are Olympic sports fans. The first column represents the percentage of women that are fans of that particular sport. The second column is an index that is a measurement of a consumer’s likelihood to engage in the target category. An index of 100 is par with the national average. Anything above 100 is above average and anything below 100 is below average, and the index is always comparing the target percentage to the base. In this example, the first row of data has an index of 98 (second column). This means that consumers who are women are 2% less likely to be fans of the Olympics than the population, which of course means that men are 2% more likely to be fans of the Olympics (in the Scarborough research they ignored any option that was not either man or woman). The third column indicates the percentage of women who are avid fans of the indicated sport. The fourth column is similar to the second column and relates the comparison of women being avid fans to the entire population. So again, relative to Olympic sports, 24.1% are avid fans, and that means that women are 5% more likely to be avid fans of the Olympics than the entire population, which means men are 5% less likely to be avid fans of the Olympics. Let’s use another example, the WNBA. Only 15.4% of women are fans of the WNBA, which is 2% less than the population norm. However, 1.8% of women are avid fans of the WNBA, which means that women are 11% more likely to be avid fans of the WNBA, compared to the total population. Looking at the NFL, almost 50% of women say that they are fans of the NFL. League research backs up that claim as of the league’s 50 million avid fans, 30 percent are women, and 52% percent of the NFL’s “mainstream” fans are women. In addition, according to the NFL, more than 45 million women watch NFL games on TV each weekend. Copyright 2018 Galen Trail

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Demographics The NFL may be Figure 9.8 ahead of the game in Sex terms of TV spectating behavior by women. Male Female According to the Year in Sports Media Report (2013), females made of 35% of the TV audience during the 2013 regular 30% 30% 32% 35% season, ahead of the NHL, NBA, and MLB (Figure 9.8). 70% 70% 68% 65% In addition, Wann, Dolan, McGeorge and Allison (1994) found NFL NBA MLB NHL that gender differences existed on team identification (being a fan of the team) and James and Ridinger (2002) found that significant differences did exist between male and female attendees at men’s and women’s basketball games as to degree of being a general sport fan. More males said they were strongly loyal fans and more females said that they were not fans at all. However, the differences were much smaller when a specific team was the focus, indicating that the differences were probably moderate at best (in terms of effect size, i.e., I would like to note the distinction meaningfulness). This was borne out through the findings of between sex and gender. Sex is a Gantz and Wenner (1995) who determined that when both biological trait, whereas gender is a groups self-identified as fans of a particular team, there sociological concept regarding were minimal or no differences on identification and Wann identity. Sport consumer research, and his colleagues who also found minimal or no until recently, has not really made a differences on team identification (Branscombe & Wann distinction between the two and 1991). many articles interchange the terms However, although there may be differences in or misuse them (myself included). In attendance behavior between men and women and in addition, until very recently, most being a fan, research does not support the existence of sport consumer research has not large differences between males and females on things acknowledged any options other such as attitudes, beliefs, intentions, motives, and things than male or female (or man or other than behaviors. For example, no differences were woman). In my own recent found on basking in reflected glory (BIRGing) and cutting off research, I no longer include sex or reflected failure (CORFing; Wann & Branscombe, 1990). gender on surveys, unless the client Zhang, Smith, Pease and Lam (1998) found no sex requests it, because differences are differences of satisfaction with ticket service, audio/visuals, so minimal, I don’t find that it accessibility and parking, arena staff, and event amenities. meaningfully predicts behavior. Although some researchers have found sex differences on There are exceptions of course, and motives for attending or being a fan (e.g., Hansen & in these cases, I ask about gender, Gauthier, 1993; Kahle et al., 1996; Wann, Schrader, & and leave a blank line to be filled in. Wilson, 1999), the differences are typically only small to moderate (based on effect size calculations).

Sex vs. Gender


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Demographics What this seems to indicate is that in the general population sex differences may exist on actual sport consumption behaviors in some sports. However, in terms of why people are motivated to be a fan or to attend games, their reactions to the games, satisfaction with the game, etc., males and females may not differ much at all. If these differences do exist, they are not large enough to be meaningful (in terms of effect size), so it would behoove marketers to be very careful in making different marketing plans based on the sex (or gender) of the individual. At least, the intelligent marketer would determine if differences were large enough in his or her own market to make it worthwhile to segment. Religious Subcultures As far as I can tell, no one has researched religious differences on sport consumer behavior. However, segmenting the sport market in the United States based on different religions does not seem to be very prudent to me. The potential for backlash if one religious group feels discriminated against may outweigh any benefit. Furthermore, as Lindridge (2005) noted referent to general consumption, within Western cultures, religious differences do not seem to predict actual consumption to an extent that it is viable to segment the market by religion. However, that does not mean that franchises have not had religious promotions. The Nashville Sounds, a AAA minor league baseball team holds a “Purity Faith Night” every year as a fundraiser for a Christian charity. Donations during the game have helped build houses in association with Habitat for Humanity and have helped fight AIDS in Africa. Other teams have done similar promotions as well. That’s not to say that religion isn’t prevalent in sport and that sport fans are not as religious or spiritual as the players. As I noted in Chapter 7, sport fans do have a need for spirituality, however I would not recommend that sport marketers try to segment their attendees by religious preference or by level of spirituality. Nor would I recommend doing religious promotions like the teams I noted above have done. However, having promotions that support Habitat for Humanity and other charities that may not have overly religious messages, would probably be fine. Copyright 2018 Galen Trail

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Demographics Geographic and Regional subcultures As we noted in the chapter on fan socialization, geography can have a fairly large influence on what teams people choose to support. In 2004, SportsBusiness Journal reported some Figure 9.9 research from Scarborough which indicated the percent of fans in various markets that went to at least three or more games across all sports within that market (Figure 9.9). St. Louis led the nation with almost 24% of the population attending more than Figure 9.10 three games, while a little less than 12% of Milwaukee’s population attended that many games. Denver tied for third with 19.8% but led the nation in the percent of fans who bought either full or partial season ticket packages (Figure 9.10). One great geographical area to examine is the New York City metropolitan area. Bill King, from the SportsBusiness Journal, did an interesting article in 2004 about how fans of the different teams were distributed in the New Figure 9.10 York area. What was surprising though, and this seems to point out that success may not be as influential as many people in the sport management profession would like to believe, is that even though the Nets made the NBA finals in 2003 and the NY Knicks did not even make the playoffs, the Knicks still outdrew the Nets by 4000 a game. The Devils won the Stanley Cup in hockey and the NY Rangers did not make the playoffs. Who had more fans per game? Rangers, by 4000! When both the Mets and the Yankees are winning (it is very rare that the Mets are winning, and the Yankees are not), the Yankees always outdraw the Mets, even during 2000 when they met in the World Series. According to King, if you would ask the owners of the Yankees, Rangers, Knicks and Giants to identify their market, they would “lay claim to every last street corner in the city and every last blade of grass in the suburbs.” King claimed that those teams think that it is their birthright because they have been in the city so long: the Yankees since 1903, the Giants since 1925, the Rangers since 1926 and the Knicks since 1946. King also noted that all of the other teams, which came into existence between 1960 Copyright 2018 Galen Trail

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Demographics and 1982, have been trying to find some combination of image, geography and demography, so that they can get out from under their big brothers. The New York metropolitan area consists of the city, Long Island, northern New Jersey, and the Hudson Valley. In this area there are over 14 million people. There are five boroughs within the New York City limits (Brooklyn, Queens, Staten Island, Manhattan and the Bronx). By itself Queens has 2.3 million residents and Brooklyn has an additional 2.5 million. Nassau and Suffolk counties have over 2.8 million people. As King pointed out, each one of these areas by itself is about the size of Denver, Tampa-St. Figure 9.11 Petersburg or St. Louis. If you were to put two of them together you now have a population the size of Atlanta or Houston. If you combine the Passaic, Bergen, and Hudson counties in New Jersey (population of about 2 million), you have more people than Cincinnati or Cleveland. When you look at the geographic location of the fans (Figure 9.11), not surprisingly the teams that have been around longer have greater geographical dispersal and often include areas in which the “upstart” teams would like to think is their home turf. However, as King noted that there are also certain geographic divisions that make it easier to patronize one team or another. Manhattan, Brooklyn and the Bronx are older boroughs that are wellserved by the subway system, which links the three of them and makes it convenient to move from one to the next. Thus, it is easiest to get to Madison Square Garden or Yankee Stadium from those three boroughs, along with the suburbs of the Hudson Valley and northern New Jersey. Queens and Long Island are heavily populated, Copyright 2018 Galen Trail

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Demographics but the mass transit options there are less extensive. While Queens is a New York City borough, its residents travel by car more often than others in the city do. As a result, Shea Stadium [now Citi Field] and Nassau Coliseum, with easy highway access and loads of parking, have made the most sense for those people. As perhaps is apparent, although there are certain geographic areas that are more highly skewed toward one team or another, it probably would not behoove any New York team to segment their market to any great extent by geographic region (unless you are the New Jersey Devils). Obviously most broadcast media will reach the entire area, so all individuals within the metro area will have the same likelihood of being aware of the advertising. In terms of billboard ads it might behoove marketers to segment in this case, if they are convinced that not enough of their own fans or potential fans would see the ads and be responsive to them. However, nationally, it makes no sense to market to the entire nation when most fans of any team are within that particular geographic region. For example, there are not enough Seattle Mariner fans across the country to put specific emphasis on marketing to areas outside of greater Seattle. That does not mean that those fans should be ignored or should not have access to Mariners’ products or media but blanketing the U.S. or the world with Mariners marketing is not financially profitable, with the potential exception of Venezuela because of Felix Hernandez (at least up until recently anyway). Age subcultures Figure 9.12 Age According to the Year in Sports Media 2-17 18-34 35-54 55+ Report (2013), MLB might have a bit of a problem as 50% of those watching it on TV are 25% 29% older than 55. The NBA 37% 50% has the youngest media 30% audience of the four at 35% 34% 45% below 34 years old 26% (Figure 9.12). 32% 28% Specific to sport 20% 17% consumption, Zhang and 13% 9% 9% 7% colleagues found that as NFL NBA MLB NHL age increased, the number of games attended increased for season ticket holders (half or full). Again this may just be a lifestyle change, but more research needs to be done. However, they did not find and significant age differences on intentions to go to games during the next season (Zhang, Wall, & Smith, 2000). Researchers have found conflicting results when looking at the relationship between age and motives for attending. Zhang and his colleagues found that age was negatively related to catharsis and aggression and salubrious effects motives, but not related to achievement seeking, community image, or stress and entertainment (Zhang et al., 2001). Unfortunately these results must be taken with some skepticism because of a lack of construct reliability of the motive dimensions. Pan and his colleagues Copyright 2018 Galen Trail

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Demographics found that older people were less motivated to attend due to social factors, however, the construct reliability of this scale was low, and no effect sizes were reported. They did not find any trends relative to age and the motives of team success or team loyalty (Pan et al., 1997). Zhang and some other colleagues found that age was positively related to the market demand variables of game attractiveness, promotions, and economics, but variance was 3.7% or less in all cases (Zhang, Lam, & Connaughton, 2003), indicating that even though these were significant findings, marketers probably would not want to spend a lot of money segmenting their market based on age. However, some have done this to a small extent. The Tampa Bay Rays, back when they were the Devil Rays (in 2004), created a fan club specifically for people 55 and older. This club was sponsored by St. Anthony’s health care and club members received discounts at businesses throughout the Tampa Bay area. Although the Silver Rays club apparently has seen its demise, the Golden Rays club replaced it in 2008 with similar objectives. Sexual Orientation Although the mainstream sport world has had difficulty acknowledging that the gay and lesbian market exists, certain teams and leagues have made inroads and embraced this market. For example, the Seattle Storm embraced the idea that a significant percentage of their fan base is lesbian. They host multiple Celebrate Pride nights, participate in the Pride Picnic and Pride Parade. The Storm has done an excellent job in understanding this market relative to many other teams. In addition, in 2014, the WNBA debuted “WNBA Pride presented by COVERGIRL; A National Platform Celebrating Inclusion and Equality.” The WNBA was the first professional league to establish a social responsibility program to focus on inclusion and equality. However, they can still do more. Unfortunately, even though the Storm shows their support for gay and lesbian issues, some of their fans have not in the past. In some research that I did for them more than a few years ago, about 10% of the fans surveyed had a negative comment about fans who were perceived to be lesbian or gay. Luckily they were in the minority, but intolerance had reared its ugly head. However, I believe that since then Storm fans have become considerably more tolerant. In that same research I found that lesbian couples had attended slightly more games than straight couples or singles during that season (8% of the variance) and intended to attend more games during the next season (7% of the variance). Lesbian couples also spent almost $100 more per season on Storm merchandise than the other two groups (7% variance). There were no significant differences on length of time of being a fan of the Storm though. Even though there were differences that explained a small to moderate amount of variance, this is only one study on a relatively small sample. In the women’s professional soccer data (Nicefaro & Goobes, 2017), there were slight differences on the product benefit of diversion (not meaningful though, 4% variance) and on the product attribute of player skill (4% variance) between heterosexuals and gay/lesbian attendees. There were also slight differences between heterosexuals and bisexuals on the player skill variable. In all cases, heterosexuals rated the variables slightly lower. This indicates that marketers should not start necessarily segmenting their market by sexual orientation. However, if marketers have an inclination that this might be an untapped market for their organization, it would behoove them to research the potential of this market. Copyright 2018 Galen Trail

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Demographics Household income According to the Year in Sports Media Report (2013), the TV viewers of the NFL, NBA, and MLB don’t differ that much by level of income. However, those three leagues pale in comparison to how rich the NHL fans are supposedly, as over 50% make more than $75,000 per year (Figure 9.13). Figure 9.13

Household Income 35% 30%

Percentage

25% 20% 15% 10% 5% 0%

NFL

NBA

MLB

NHL

< $20K

9%

13%

9%

9%

$20K-$40K

17%

21%

23%

12%

$40K-$75K

32%

32%

32%

27%

$75K-$100K

17%

15%

15%

20%

$100K+

25%

18%

21%

33%

Of course, household income certainly has not predicted sport consumption well either. In a reanalysis of some research that I did for a WNBA team I found that income level did not significantly predict attendance, watching the games on TV, buying team licensed merchandise, or intending to attend games in the future. Zhang and his colleagues found that differences existed on income and attendance, but there was no trend. Season ticket holders who made more than $100,000 attended the most games, followed by those who made $75,000-$100,000. However, there was a skip of the next income segment, as those who made between $40,000 and $50,000 attended the next largest number of games. Those who made between $50,000 and $75,000 attended fewer games than those making less than $40,000 (Zhang, Wall, & Smith, 2000). Pan and his colleagues found that as household income increased economic factors as a motive decreased, indicating that they did not impact attendance. In other words, as people made more money, they did not feel that the cost of tickets would negatively impact their attendance. However, neither group of researchers reported effect sizes so we do not know if these relationships were large enough to matter. Zhang et al. (2000) also found that income levels did not predict intentions to attend games during the next season either. Even more conclusive results were evident when looking at motives and market demand variables. Pan and colleagues found that there were no trends based on income for motives of team success or loyalty (Pan et al., 1997). Zhang and his colleagues found no differences based on household income related to any motive (catharsis and aggression, salubrious effects, achievement seeking, community image, or stress and entertainment; Zhang et al., 2001). Again these results must be taken with some skepticism because of a lack of construct reliability of the motive dimensions. Furthermore, Zhang et al. (2003) found no meaningful differences on household income for game attractiveness, Copyright 2018 Galen Trail

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Demographics promotions, or economics either. In the women’s professional soccer data (Nicefaro & Goobes, 2017), there were no differences on any brand associations. Again, this indicates that especially in terms of beliefs, attitudes, and feelings, segmenting a market based on household income would not be a good idea. In terms of behavior, it is a little less clear cut. Obviously, the pricier the season ticket is, the more likely it will have at least some effect on who is able to afford it. However, loyal fans are likely to find a way to get that ticket even when it does not seem economically feasible. Occupation Within the sport realm, no meaningful differences among occupation types on have been found on market demand variables such as game promotions, appeal of home team, appeal of opposing team, or schedule convenience (Zhang et al., 1995). Furthermore, Zhang and his colleagues also found that there were no significant differences based on occupation related to any motives for attending games (i.e., catharsis and aggression, salubrious effects, achievement seeking, community image, or stress and entertainment; Zhang et al., 2001). Again these results must be taken with some skepticism because of a lack of construct reliability of the motive dimensions. Education Differences based on education level are not apparent in sport though, even though one might suspect it because of the correlation with income. Zhang and his colleagues found no differences on education level relative to attendance. They did find some minor differences on intentions to attend, but no trend (Zhang et al, 2000). They also found no meaningful differences among education levels on the market demand variables of game promotions, appeal of home team, appeal of opposing team, or schedule convenience (Zhang et al, 1995). Nor did they find any significant differences related to any motive except for catharsis and aggression (in which there was no trend), salubrious effects, achievement seeking, community image, or stress and entertainment (Zhang et al., 2001). In the women’s professional soccer data (Nicefaro & Goobes, 2017), there were no meaningful (greater than 6%) differences on attendance, watching the team on TV, merchandise spending, number of times per week accessing the website or accessing social media. In addition, there were no meaningful differences on any brand associations. So, in general, marketers don’t really need to worry about demographics when trying to predict sport consumer behavior or segmenting their markets. There are much more important things that will predict spectator sport consumption, such as brand associations, points of attachment, and satisfaction. In addition, there are much better variables to use to segment data, as we will cover in considerably more detail in Chapter 13. Renton RoadRunners Example Let’s look at our fictitious RoadRunners example again and see whether attendees differed at all by demographics. Remember that the data below is real, it just happens to be from a different team Copyright 2018 Galen Trail

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Demographics (obviously) other than the made-up RoadRunners. In addition, it has been modified to fit the scenario in places.

Nationality Race Ethnicity

Sex

Religion Geography

Age

Sexual Orientation Household Income

Occupation Education


Demographics N/A N/A American Indian/Alaskan Native (2%) Hawaiian/Pacific Islander (.2%) Black (23%) Asian (3%) Hispanic/Non-White (5%) White/Hispanic (55%) Other (12%) No differences on any behaviors (attendance, merchandise purchasing, streaming, website usage, or social media usage). No differences on any product association. No differences on constraints. No differences on needs or values. 57.8% of the attendees were male, 41.8% were female. The remainder chose other. There were no significant differences on any behaviors or behavioral intentions. Males went to 12 games on average, Females went to 13. Males spent $87 on merchandise for themselves, females spent $99. Males streamed 5 games, so did females. Males accessed the website 3.3 times per week on average, females 2.8 times. There were no meaningful differences on any product associations. The only significant difference (but only 3% variance) was on Social Interaction, with females scoring higher. No meaningful differences on constraints, but there was a significant difference (only 3% variance) on lack of knowledge, with females scoring higher. No meaningful differences on needs or values. Need for personal safety was the largest difference (4%), with females scoring higher. N/A (14%) less than 5 miles (32%) 6 to 15 miles (32%) 16 to 30 miles (11%) 31 to 45 miles (6%) 46 to 60 miles (2%) 61 to 90 miles (1%) 91 to 120 miles (2%) >120 miles Those who lived farther away attended fewer games and streamed more, but did not differ on merchandise spending, website usage, or social media usage. Those who lived farther away felt travel concerns were a constraint. There were no differences on product associations or on needs or values. Average age of attendees was 41 years old (range from 18-78; those under 18 were not allowed to take the survey). Age was not correlated with any behaviors, behavioral intentions, motives, or product associations, except for Advertising. The older the fan, the less likely advertising would have a positive impact on their attendance. N/A (5%) Below $20,000 (11%) 20,000-$39,999 (16%) 40,000-$59,999 (17%) 60,000-$79,999 (17%) 80,000-$99,999 (29%) 100,000-$149,999 (4%)150,000-$199,999 (0%) 200,000 or above No differences on any behaviors (attendance, merchandise purchasing, streaming, website usage, or social media usage). Only very slight differences on any product association, with those in the lower incomes scoring higher on aesthetics of the game. No meaningful differences on constraints, although there was a slight trend for those in the lower income brackets to feel cost was more of a constraint. There were slight differences on need for financial security (those in the highest income brackets scoring higher). Same with work ethic. N/A 13% had a high school education, 34% had some college, 40% graduated from college, and 14% had a graduate degree. There were no significant differences on attendance, streaming, spending, website usage, or social media usage. No differences on product associations. No differences on any constraints. Nor were there any differences on needs or values.

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Demographics What we can learn from the RoadRunners example, is that team marketers really don’t need to market differently based on demographics as none of the differences were large enough to matter, when there were differences. Summary We have covered nationality, race, ethnicity, religion, geographic region, age, sex, sexual orientation, household income, occupation, and education, as demographic subcultures that marketers have frequently tried to use to create separate segments and/or predict sport consumption behavior. As may be well apparent by now, segmenting the sport consumer market by demographic characteristics would probably be a waste of a marketing budget. As I will point out in Chapter 13, sport marketers need to segment their markets so that they are able to target specific groups more concisely and succinctly, allowing them to meet the needs of each segment better. However, using the characteristics described in this chapter is not the way to go. That said, I do not mean that demographic characteristics should be ignored, as I depicted in the model, these characteristics in some cases may differ across personal needs, personality, and personal values. Thus, indirectly demographics may influence consumer behavior, specifically sport consumer behavior, but only mediated by a considerable number of other variables along the way. So next, we need to investigate how the internal customer environment impact brand attitude and the consumer pathway. On to the next chapter!


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Brand Attitude & Consumer Pathway


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Brand Attitude & Consumer Pathway

Chapter 10 Brand Attitude & the Consumer Pathway

Introduction Tom Reed, for the Columbus Dispatch, wrote an article in 2007 discussing fan interest in, and expectations for (part of active consideration), the NHL’s Columbus Blue Jackets forthcoming season. He noted that after six seasons without a playoff appearance some fans want at least some bang for their buck. “Fans have voiced their opinions regarding the lack of success the organization has had on the ice and feel it’s time the Jackets make the playoffs” (Reed, 2007). Before the 2007-2008 season, the Jackets were the only NHL franchise that had never made the playoffs. The team’s lack of success had caused a considerable lack of interest represented by attendance decreasing in each of the preceding four seasons. The Blue Jackets only sold out five games in the 2006-2007 season, down from 23 out of the 41 home games five years earlier. Perhaps the most startling indicator of the fans’ lack of interest was the Blue Jackets failure to sell out the home opener of the 2007-2008 season. The team had previously sold out every home opener since the inaugural 2000-01 season. As Reed (2007) noted, “Some fans said their expectations are high for the team's first full season under coach Ken Hitchcock, but after so much disappointment, others are taking a wait-and-see approach.” In an effort to maintain fan loyalty, Coach Hitchcock met with Blue Jackets season ticket-holders during the offseason. He noted that the fans had playoff expectations based on the improved play at the


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Brand Attitude & Consumer Pathway end of the previous season. Hitchcock said, "They want to see effort and resiliency, and that's what we want to give them. They know the difference between full effort and partial effort" (Reed, 2007). This is one example of people (fans and spectators) moving backwards along the consumer pathway. Customer Environmental Insights and the Consumer Pathway As we know from previous chapters, the customer environment impacts progression (or regression as in the example above) along the consumer pathway (Figure 10.1). We have discussed the pathway to some extent, but now we need to go into it a little deeper.

Figure 10.1

We have discussed how marketers try to create messaging, communications, advertising, etc., to generate consumer awareness. We have also discussed how people are socialized into being aware of, and hopefully fans of, the team. Now we need to look at it from the individual’s perspective. How does the individual actually become aware of the particular entity (e.g., the team, the sport, the league, the player, or whatever)? Awareness Awareness involves being exposed to and recognizing stimuli. As I noted in an earlier chapter, marketers engage in various promotional activities in order to position some type of stimuli in proximity of an individual’s senses. We may see or hear a sports broadcast or advertisements for a sport product. We also come into physical contact with products; the feel of a ball or an


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Brand Attitude & Consumer Pathway athletic shoe, for example, is a component of our perceptions. As we have already noted, there are ‘tons’ of available stimuli, particularly through various forms of media. With cable and satellite television today, there are literally hundreds of viewing options to choose from. This doesn’t even include the OTT (Over The Top) options that teams and leagues are creating by streaming their games (products) on the internet. Among the myriad of channels there is specialty programming, not only for sports news and information, but for specific sports, teams, and even conferences. In most cities now, take New York for example, you can find programming focused on professional and college teams in and around New York. Through the YES Network, people can watch the Yankees, the Nets, the Yale Bulldogs, and even “Classic YES Sports.” Consumer interest in college sports has led to the development of ‘conference channels’. Now every major conference has a channel, for example, The Big Ten Network makes available all the Big Ten sports you ever wanted, and even some you did not. Television is only one of the many, many sources of information and stimuli. There are thousands of radio stations broadcasting sporting events and sports-based programs – where would we be without all the radio call-in shows? For the ‘technology generation’, there are all the sport and sportrelated web and social media sites, which may seem at times too numerous to count. Coming back to our consumer behavior focus, do not forget about the retail stores you shop at with all the products and advertisements awaiting your attention. We are exposed to a lot of stimuli each day because marketers are working to foster awareness of their products in the minds of consumers. An important point to consider is that awareness of some products occurs on a (more or less) random basis. For example, commercials you hear on the radio, billboards you see while driving (remember not to look too long), and display ads that ‘pop up’ while utilizing the Internet. The information is not necessarily targeted at you specifically, but you do receive the stimuli. However, as we noted in the Socialization Chapter, people do not only become aware of the team or product through the media. Word of Mouth (WOM) is extremely important and probably more so than the media, because WOM typically occurs with someone the person knows (e.g., family, friends, associates) in a place that the person is quite comfortable (e.g., the home, friend’s house, school, or work). Thus, when awareness happens in such a place and due to someone that the person probably knows well and trusts, awareness may not be the end of the progress along the pathway. If the person values the views of the person or people who generated the awareness, then that might pique their


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Brand Attitude & Consumer Pathway interest in the team (or product), which is the next step in the consumer pathway. Does awareness always lead to interest? No, of course not! You have been bombarded with information through the media, social media, and WOM about teams and sports that you have no interest in what-so-ever, even though you may be aware of it. Interest As I have just explained, there is a seemingly limitless amount of information to which we are exposed. Unless a consumer is interested in and pays attention to information, there is no further progression along the consumer pathway. As such, the interest portion of the process is critical. The question comes to mind then, what factors influence consumer interest? We are going to consider three factors: stimulus, individual, and situational. Stimulus Factors. The physical characteristics of stimuli are the stimulus factors. These are the product attributes (e.g., Team Success, Star Player, Quality Management/ Front Office, Quality Head Coach, Aesthetic Play, Aggressive Play, Dramatic/style of Play, etc.) and some of the nonproduct related attributes (Brand Mark/Logo, Venue Aesthetics, Relationship Building, Promotions, Advertising) that we discussed in a previous chapter. In addition, perceptions of the product benefits (Social Interaction Opportunities, Nostalgia, Popularity, Diversion, Pride in Place, Information Provision, Supporting a Cause) are also stimulus factors. However, we also know that people can be aware of these things, and it doesn’t necessarily create interest in the product. Beyond the stimulus factors, we also need to consider the individual factors, some of which we discussed in the needs and values chapter. There must be an interaction between the stimulus and individual factors, to create interest. Individual Factors. The characteristics which distinguish one individual from another comprise what we refer to as individual factors (Hawkins, Best, & Coney, 2007). In one sense, there may be innumerable individual characteristics. In terms of better understanding consumer behavior, it is not feasible to try and list every possible characteristic that may distinguish one individual from another. Fortunately for us, most consumer behaviorists focus on two key concepts when discussing individual factors. We are no exception to that generalization; we are also going to focus on two particular characteristics, (1) ability and (2) motivation. In the context of interest, ability is a person’s capacity to attend to and process information. In other words, as a consumer you have to be able to attend to information, which could mean you are not distracted, you are ready to receive information, and you are willing to receive information. You also must also have the capacity to process information, which includes having knowledge about or familiarity with a product (at the bare minimum – awareness). In the case of a new product, or an established product seeking to reach new consumers, knowledge about or familiarity with a product will be minimal or non-existent. Efforts to impact perception in either case will focus more on stimulating interest. Familiarity with a product has the potential to positively impact perception. If a consumer is familiar with a particular product, and that familiarity is associated with a positive evaluation – e.g., I have previously watched a particular team and I enjoyed the experience – it is highly likely that the consumer will direct his or her attention to the stimuli (an advertisement for the team, for example). It is


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Brand Attitude & Consumer Pathway important to recognize that if a consumer’s familiarity is associated with a negative evaluation, your team beat my favorite team for example, the individual is likely to avoid, or choose not to attend to, a particular stimulus - an advertisement - from the “bad” team. A key point to remember from this section is that we tend to respond to that which we are familiar. Interest in a particular sport product may be enhanced when consumers have knowledge about or familiarity with a particular product. Closely related to the idea of ability is motivation. Many of the needs and values we covered in that chapter are motives. Consider this example, as a graduate of The Ohio State University, I am familiar with the university’s sports teams. Said another way, I am familiar with the Ohio State “brand.” I am more than just familiar with the brand; I am also interested in the brand because I have a need for social acceptance that I can obtain through associating with other Buckeyes. In addition, I have a need for achievement that I can fulfill vicariously through following their successful teams. Another example, if we go back to the women’s professional soccer data and Figure 7.10, is how interest in the team was driven by awareness of the opportunity to, and the ability to, support a cause (women’s sports; a product benefit), plus the need for personal growth (a motivation). Both the ability and the motivation had to be present for people to be interested in the team. In addition to stimulus and individual factors, the final component that may impact consumer interest is the situational factors category. Situational Factors. The situational factors are the stimuli other than the focal stimulus that are part of the environment in which perceptions are being formed. Situational factors, as we have discussed in the both external environment chapter and the organizational environment chapter, can be either positive (activators) or negative (constraints). For example, when there are no direct competitors in the local market and all WOM is positive and focused on the team, then the situational factors are likely to accentuate the interest in the team by the individual. However, if the reverse is true and there are multiple direct competitors to the team in the market and WOM is predominantly negative about the team because of poor management and a lack of success, then it is possible that an interest in the team will not develop (think Cleveland Browns in 2017). The goal here is to better understand the factors that influence people’s consumer decisionmaking and progress along the consumer pathway. At this juncture I have dealt with the first two stages of the pathway awareness and interest (Figure 10.2). The next stage is active consideration.

Figure 10.2

Awareness

Interest

Active Consideration


Purchase

Consumption (Usage)

Attitudinal Loyalty/ Repatronage Behavior

Lifestyle Change

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Brand Attitude & Consumer Pathway Active Consideration The active consideration stage is where the individual starts to seriously consider purchasing the product or actively accessing the product if it does not have to be purchased (e.g., watching the game on TV on a channel that has already been paid for relative to other purposes). The active consideration stage will end with an intention to purchase (or a decision not to). The individual assesses all of the information that they have about the product through the media, from the organization directly, from WOM, and from any personal experiences. As all of this information processing is going on, the individual starts to generate expectancies about the product, event, or experience. Expectancies. People have expectancies about most things in life, including consumption of a good or service. These expectancies are typically about the quality or performance of the good or service. When you buy a Coke, you expect a certain taste, a certain level of carbonation, etc. When you stay at a hotel, you have certain expectations about the service you get at the front desk, the cleanliness of the room, the quality of the mattress, and so on. In terms of sport, when you attend or watch a game on television, you typically have expectations about how the game will go (a product attribute). There are at least two dimensions that expectancies about sporting event successes can be categorized into: expectancies about outcome, and expectancies about performance. For example, you may think that one team will win. You also may feel fairly confident in your beliefs about which team will win in certain games. In other games you may not be certain at all which team will win, although you may hope that your favorite team will pull it out in the end. These are expectancies about the outcome of the game. Many people who watch the game also have expectancies about the performance of the team (or teams) that are distinct from whether or not the team will win. As I noted in the example with the Columbus Blue Jackets, the fans expected that the players would play hard and put forth their best effort every game. The fans did not necessarily expect that the Jackets would win every game, but they did expect them to play hard every game. The expectancies about performance and outcome, while distinct, are not unrelated. Most people expect that if their team plays well and gives a lot of effort, the team will win. However, most people also realize that a team can play perfectly and still lose the game if the other team happens to have more skill or is better coached on that particular night. Obviously, people can have expectancies beyond just the outcome of the game and the quality of play. People can have expectancies about almost all of the product attributes and benefits to some extent or another. These expectancies contribute to the interest level. The person then determines if their interest level in the product is high enough based on the possibility of some of the product attributes and/or benefits fulfilling needs, wants, or values. If the interest level is high and there are no potential constraints, the person may intend to purchase the product.


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Brand Attitude & Consumer Pathway Here is an example of how assessing the potential product can drive purchase intentions. The concept of perceived value is a “consumer’s overall assessment of the utility of a product (or service) based on perceptions of what is received and what is given” (Zeithaml, 1988, p. 14). It is a combination of a person’s perception of the product attributes and benefits, and it creates expectations about that product. Before purchasing a particular product, a T-shirt bearing your favorite team’s logo for example, you may consider the monetary cost versus the anticipated benefits of owning and wearing the shirt. The outcome of your cost-benefit analysis is the perceived value of the T-shirt. According to Kwon, Trail, and James (2007), the perceived value of licensed sport merchandise is likely evaluated from a utilitarian perspective and a symbolic perspective. The utilitarian perspective focuses on the functional value of a product’s attributes. Thinking about our T-shirt example, the functional value would include elements such as expectations about the shirt’s comfort and durability. The symbolic perspective “focuses on what the product represents to the consumer or what the consumer thinks the product represents to other” (Kwon et al, 2007, p. 542); in other words, the product benefits. How important is it for you to wear a T-shirt bearing your favorite team’s logo? Given a choice between a plain T-shirt and a T-shirt with your favorite team’s logo, we would guess that you would choose the T-shirt with the team logo. Your psychological connection to the team (team identification) imparts a symbolic value to the T-shirt (Kwon et al., 2007). In the Kwon et al. (2007) research, a T-shirt bearing the logo of an athletic team was shown to a group of sport consumers. The price for the shirt was also included in the presentation. No other information about the shirt was provided. The consumers completed a series of items measuring their level of team identification, and a series of items measuring the perceived value of the T-shirt. A final set of items measured purchase intention. The results indicated that perceived value was a better indicator of purchase intentions than team identification. The impact of team identification was mediated, or found to have an impact, through perceived value. In other words, the symbolic value of the team shirt was weighed against the utilitarian value. In this study, the perceived value overall had a greater impact on purchase intentions than team identification (Kwon et al., 2007). They concluded that, Sport managers should be wary of operating under the assumption that sport consumers, specifically fans, are not price sensitive…These results indicated that sport consumers, even those with a high level of team identification, might have been concerned about the price of the team-licensed merchandise, and the perceived value of a product had a much stronger impact on their purchasing intention (p. 551). In their discussion of the results, Kwon et al. (2007) explained that the mean score for perceived value and purchase intentions were low. In other words, those participating did not think the product was a good value, and they did not intend to purchase the T-shirt. Kwon et al. went on to explain that the T-shirt originally had the Nike swoosh in addition to the team logo. The swoosh was removed from the presentation materials. As such, those participating in the survey did not know the brand of the T-shirt. Kwon et al. suggested that had the Nike logo been included, the perceived value of the shirt may have been higher. However, in this instance, because of these perceptions, the intention to purchase was low, but we don’t know whether any of


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Brand Attitude & Consumer Pathway these individuals eventually bought the shirt or not. Thus, we have no idea whether these people stopped on the consumer pathway at intentions or moved along to the actual purchase stage. We imagine that they did not progress, but we don’t know for sure. In other situations, and data sets, we do know and can determine whether or not intentions led to actual purchase. For example, in some research by Trail, Anderson, and Lee (2017), they showed that intentions to attend games explained 50% of the variance in purchasing a ticket and actually attending the game (consumption). Yoshida, Heere, and Gordon (2015) showed similar results. Purchase Although we call this stage Purchase, as I alluded to earlier, it doesn’t always have to be a purchase. In some situations, it is merely access. For example, if someone handed me a free ticket, then obviously, I didn’t purchase it, but I still obtained it in one manner or another. Or to go back to the previous example, perhaps I intended to watch the game on TV on NBC. The NBC network is available through a cable subscription and I had to pay for that, however, the purchase of the cable subscription isn’t necessarily specifically for that particular game. I get many other channels and other games for my subscription purchase, so I am not really paying specifically for that game because of my intention to watch it. In addition, in some places, it is still possible to watch NBC using an antenna and not have to pay a cable or satellite subscription fee. The point being is that intention can lead to consumption without purchase, but there still needs to be some type of obtainment of access to the product before consumption can occur, whether it is through paid means or not. Regardless, even though intentions do explain 50% of the variance in actual purchase or attendance as per Trail et al. (2017), that still means that 50% is not explained by intentions. Part of this unexplained 50% is due to intentions going awry. I might intend to purchase tickets to the game, but external constraints may prevent me from doing so. For example, the other day I intended to go see a Portland Trail Blazers game down in Portland. However, by the time I got around to looking to buy tickets, there were no tickets left on the primary market (i.e., being sold by the Trail Blazers themselves). When I went to the secondary market, the prices were very expensive because they were playing the Golden State Warriors. I wasn’t willing to pay over $100 for a ticket that was originally $25. So, even though I intended on going, I didn’t end up attending because of a cost constraint and my value of frugality. As you can see, constraints, values, needs, product attributes and benefits, all can play a part in the actual purchase, on top of the intentions. The purchase or obtainment of the product may come with a variety of experiences. For example, when trying to purchase tickets from the Trail Blazers’ website, it listed tickets for a certain price (let’s say $45), but when I clicked on View Tickets and it took me to the Ticketmaster website to actually purchase the tickets, tickets for that price were not available. The cheapest ticket was more than $100. This was not a good experience. In addition, when I clicked on See Tickets, it told me that there would be fees added on to the ticket cost, but not how much those fees were. I then had to click on Get Tickets, which took me to a screen where it said I could get eTickets (free), meaning of course they were not going to charge you for printing and shipping the tickets to you. That’s fine, not a problem. I had to click on the Next button that told me the subtotal for the purchase. Once I clicked on


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Brand Attitude & Consumer Pathway that, I had to either login to the Ticketmaster site or sign up for an account. However, I did not want to sign up for an account and receive all of their emails once I did, but I didn’t have an option if I wanted tickets. The subtotal is obviously not the total, and taxes and handling fees had not been included yet, the total cost was greater. If I had actually bought the tickets, then they would have been emailed to me and I could print them off or I could have had them sent to my phone and I wouldn’t have to print off anything. All of these things were part of the experience of buying tickets. A purchaser could have been very satisfied with the process, bought the tickets and been happy, and told a friend (advocacy). Or they could have been dissatisfied with the process (as I was), not bought the tickets, and complained to friends and acquaintances, posted a bed review on social media, and could have created a bunch of bad PR for the team. We will talk more about these things in a future chapter. Now however, let’s say the tickets were purchased and we are off to the game; the next stage in the consumer pathway. Consumption Once the purchase takes place, the consumption of the product usually follows, but not always. Jill Fan may have bought the tickets as a present for her friend and not for herself. Joe Fan may have bought the tickets for himself and then not been able to go due to the constraint of becoming sick with the flu. Even purchase can’t explain 100% of consumption. In most instances though, purchase does lead to consumption. Consumption of the game leads to a variety of things, including whether expectations were confirmed or disconfirmed, which creates satisfaction or dissatisfaction with all of the perceived product and nonproduct related attributes, in addition to product benefits. We will discuss all of those in the satisfaction chapter though. However, all of this information and these experiences that an individual has, create an initial attitude about the brand (or team). So, let’s talk about that for a little bit. Brand Attitude Fishbein and Ajzen (1975) suggested that general attitudes are “a learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object” (p. 6). In other words, an attitude is a feeling an individual has toward any particular entity or object. Attitudes are comprised of both cognitions (evaluations) and affect (feelings). Based on information from Wilkie (1986), Keller (1993) defined brand attitude as the “consumers' overall evaluations of a brand” (p. 1). Keller suggested that “brand attitudes are important because they often form the basis for consumer behavior” (p. 1), which is one of the reasons that I have suggested that it predicts sport consumer behavior intentions. Fishbein and Ajzen (1975) have proposed that brand attitudes are a function of the perceptions people have about how positive or negative the attributes and benefits relative to the brand might be. For example, people might think that the New England Patriots are a very successful team, and some people might think that is a good thing, whereas, I, personally, don’t think that is a good thing. Although I can see that they are successful, I think their success is a bad thing because I think they cheat, or at least push the boundaries of acceptable “gamesmanship,” to be successful. Because of these


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Brand Attitude & Consumer Pathway beliefs and feelings that I have, I dislike the New England Patriots brand; that is, I have a negative brand attitude toward the Patriots. And not just the team, this includes the coaches and front office, all the way up to the owner. Keller (1993, p. 4) noted that “brand attitudes can also be related to beliefs about non-productrelated attributes and symbolic benefits (Rossiter & Percy 1987), consistent with the functional theory of attitudes (Katz I960; Lutz 1991), which maintains that attitudes can serve a ‘value-expressive’ function by allowing individuals to express their self-concepts” (p. 4). What this means is that brand attitude is a combination of perceptions about both product and non-product related attributes (and benefits) of all products (or services) related to the brand. Bauer, Stokburger-Sauer, and Exler (2008, p. 213) suggested that some “researchers often examine attitudes in terms of three dimensions: affective, cognitive, and behavioral (e.g., Rosenberg & Hovland, 1960).” They disagree that there is a behavioral component of attitude, which I agree with. Behavior is a result of attitude, not a component of it. However, Bauer et al. also cite Lutz (1991) in arguing that attitudes are solely affective, that is, they do not contain a cognitive component. They contradict themselves though, because they then try to measure attitude with four items, two of which are cognitive items (p. 225). So, I think that we can follow the majority of research on brand attitude that suggests that attitudes are both cognitive and affective. In sum, we are going to look at brand attitudes in the following manner. Brand attitudes are cognitive assessments and affective reactions to those assessments. Those assessments are relative to each individual’s evaluation of information that that person has about organizational environmental aspects such as product attributes and benefits, versus potential constraints, but the person also takes into account their own knowledge generated by interactions with the organization through relationship building efforts (or lack therein) and other organizational marketing and communication messaging they are aware of. In addition, though, their brand attitude is also influenced by their own personal needs, values, goals, personality traits, and internal constraints. It is the interaction between the two environments that create the brand attitude (Figure 10.3). There have been a variety of different terms that have been used in place of, or in addition to, brand attitude. Some of these terms mean very similar things, but others mean slightly different concepts or much more extensive concepts, so we need to discuss those as Figure 10.3 well for a little bit before moving on.


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Brand Attitude & Consumer Pathway Brand Loyalty First let’s discuss brand loyalty. Oliver (1999) probably has the most comprehensive definition of brand loyalty. His definition includes four dimensions of loyalty, cognitive loyalty, affective loyalty, conative loyalty, and behavioral loyalty. He suggests that loyalty is a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior (p. 34). The cognitive aspect of this definition is the “commitment”. The affective component of this definition refers to the “preferred”, implying liking. The conative (intentions) component is represented in the definition by “in the future,” therein implying intentions to purchase in the future. The behavioral component is the “repetitive same-brand…purchasing”. Oliver is fairly unique in this regard because although many researchers and theorists include cognitive, affective, (or these two combined to be attitudinal loyalty), and behavioral loyalty, most do not include conative loyalty. Let’s look at each of these a little more in depth. Cognitive loyalty. Oliver (1999) suggests that cognitive loyalty is typically the first stage of loyalty and is reflected by a preference for one brand over another. He noted that cognitive loyalty can be either based on experiential information or what he terms, vicarious knowledge (i.e., knowledge gained through information from others only). Oliver suggests that loyalty at this level is primarily focused on attribute performance but is rather shallow in nature and routine. For example, I watch the Golden State Warriors occasionally because I perceive that they play a certain style of basketball (uptempo, run-and-gun). However, my “loyalty” is very tenuous and is contingent on them continuing to play that style of hoops. Thus, I am open to switching behavior. That is, I would start following a different team if their style was more similar to my preference than Golden State’s. Oliver says that if I’m satisfied with my spectating experiences with the Warriors though, then my cognitive loyalty will start to take on “affective overtones” (p. 35). Affective loyalty. As these affective overtones develop, the person moves from the cognitive stage to the affective stage of loyalty. Oliver (1999) suggests that “a liking or attitude toward the brand has developed on the basis of cumulatively satisfying usage occasions” (p. 35). For example, if I enjoy watching the Golden State Warriors, and my enjoyment of preferred product attributes and benefits fulfill my needs or values in some way, then my affective loyalty is much less likely to be swayed by “counterargumentation” (Oliver, 1999, p. 35). However, Oliver does hypothesize that people with this type of loyalty do still remain subject to switching behavior, even though they may be satisfied with their current brand. He suggests that brands want to get people into the next stage of loyalty, to prevent this. Conative Loyalty. This next stage of loyalty is the conative (behavioral intention) stage and it is the intention to repurchase the product. It is typically based on satisfaction with prior purchase and usage experiences and is not as influenced by marketing and advertising ploys to get the consumer to switch to a different brand. If we go back to the Warriors example, if I have been satisfied with my previous watching experiences because my needs are being fulfilled by the product attributes and benefits that I prefer and enjoy, then I am most likely going to intend on watching again. It is due to my satisfying previous experiences that I intend on watching again. Advertising by the Houston Rockets, who also play a similar brand of basketball, supposedly won’t get me to switch over and watch those games because I am happy with my current experiences and want them to continue. However, if a Warriors game is not on TV, a Rockets game is on, and I really feel like watching a style of game that I enjoy, then I might watch the Rockets game. Is this switching behavior? No, because I’m not watching the Rockets instead of the Warriors; it is substituting behavior. Because my preferred product (Warriors) is not available, I substitute the Rockets to meet my needs at that particular time. This typically won’t


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Brand Attitude & Consumer Pathway cause switching behavior unless it starts occurring more and more frequently because the preferred product is not available at the preferred time and at the preferred cost. Thus, most brands want the consumer to not only have cognitive, affective, and conative loyalty, but to also have behavioral loyalty. Behavioral Loyalty. Oliver (1999) calls this stage action loyalty, but everyone else calls it behavioral loyalty, so we will go with the more common term. At the behavioral stage, the individual overcomes obstacles and all marketing efforts by competitors to continue repatronage behavior. Switching behavior does not occur in this stage according to Oliver. However, loyalty that is solely represented by repeated purchasing, might not be as sufficient as Oliver thinks it should be. People may show repatronage behavior solely because the product is the only product of that type available, or because the quality of the particular brand is so much better than other brands, the options are not comparable. In sport though, there could be behavioral loyalty represented by a different reason. Let’s say a person has bought season tickets for 10 years straight. That would be described as behavioral loyalty. However, what if the reason that this person bought the season tickets was so that they could use them for business purposes. Perhaps that person doesn’t even like the team but knows that taking clients to the game is a good way to develop relationships with those clients. Or perhaps that season ticket buyer buys them to reward employees for hitting sales quotas. If either case, the repeated purchasing qualifies as behavioral loyalty, but there is no affective loyalty along with it. In my view, teams need their fans to have all four types of loyalty; any one particular type is not sufficient. Keller (1993) agrees, indicating that brand loyalty occurs “when favorable beliefs and attitudes for the brand are manifested in repeat buying behavior” (p. 8). Keller forgot the conative part, which is necessary as well, but at least he got three out of the four. Oliver (1999) proposed that brands want to develop ways to prevent switching behavior and to increase brand loyalty. He suggested two different ways of doing that and also suggested that a combination of the two would be best. The first way is for the brand (organization) to develop (or to support existing) brand communities that offer social support. From a sport perspective, many fan communities exist for different teams. Some are more formally organized than others, for example, the different supporter groups for the Seattle Sounders. The Sounders officially recognize four supporter groups: Emerald City Supporters, Gorilla FC, North End Faithful, and Eastside Supporters. Oliver hypothesized that by creating these brand communities, people who participated would become more loyal to the brand because of the connection to similarly situated others; i.e., fans just like them.


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Brand Attitude & Consumer Pathway However, not everyone is interested in joining a group of people all supporting the team or brand. Others prefer to go their own way and develop what Oliver (1999) called individual fortitude. This fortitude is a conscious attempt by the consumer to ignore or isolate themselves from Figure 10.4 marketing and advertising from competitors trying to get them to switch. From these two dimensions, Oliver (1999) created a four-cell model (Figure 10.4). Individuals in the Low/Low cell, solely buy because of product superiority or lack of competitor products. Thus, the may have cognitive loyalty, but not much else. In support of Oliver’s (1999) second cell, Trail, James, Kwon, Anderson, and Robinson (2016) noted that: In the low fortitude and high-community social support cell, individuals are loyal because of the camaraderie provided by a social network of similar consumers. Individual’s feel a sense of community when they share the same consumption values and behaviors. Oliver terms this Village Envelopment (p. 97). This is cognitive and affective loyalty, and possibly even conative and behavioral loyalty, but the first two may be somewhat splintered. What I mean is that, people in the Village Development cell may be as loyal, if not more loyal, to the ‘village’ than to the team or brand because being part of the group fulfills the companionship and camaraderie needs for the individual more than just being a fan of the team, without the community aspect. Trail et al. (2016) view those in the high-fortitude and low-community/social-support cell, as people: committed to a product exclusively, regardless of any social or societal influences. Oliver (1999) suggests that this transcends conative and action loyalty because the loyalty is a conscious goal in and of itself. This is termed determined self-isolation. An example here is an individual who is a fan of a team regardless of team’s level of success and regardless of how the community views the individual. (p. 97) Ideally though, all consumers would be in the Immersed Self-Identity cell, where they are high in both individual fortitude and social support. Trail et al. suggested that someone like this would be in the fan club, would wear team merchandise all the time, and be active on social media. Trail et al. (2016) found support for this model but found that most season ticket holders (STH; 69%) and single game purchasers (SGP; 64%) were in the Immersed Self-Identity group. The remainder were in the Determined Self-Isolation group (18% and 17% respectively), in the Product Superiority group (7% and 16%), and in the Village Envelopment group (6% and 3%). The only surprises were how few of the single game ticket purchasers were in the Village Envelopment group. We had expected this group to be low for the STHs because buying season tickets primarily for the social aspects is a large expense. However, we figured that a fair number of people would go the single games for the social aspect, especially in baseball. This was not the case, at least in this sample.


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Brand Attitude & Consumer Pathway Trail et al. (2016) also found that there were some differences in attendance, but the differences were not that great. This supported Oliver’s (1999) supposition that the behaviors would not be that different across the groups, even though the types of loyalty differed. Not surprisingly, the single game ticket purchasers in the Immersed Self-Identity group did attend more games than any of the other three groups (except in one instance), but the other three groups did not differ significantly (Figure 10.5).

Figure 10.5

Similar results were determined for the games intending to attend (Figure 10.6). What this means in terms of brand loyalty is that those that are loyal to the brand cognitively, affectively, conatively, and behaviorally (those in the Immersed Self-Identity group) are going to

Figure 10.6

stay with the team through thick and thin, thus generating more lifetime value for the team. Those in the Determined Self-Isolation group are ones that are easy to take care of because they don’t want or need the social interaction or social support that the Village Envelopment people are going to want and need. The Village Envelopment people are going to want a lot of interaction with the team and with other fans. The Product Superiority group is attending because they are cognitively loyal and don’t see a better option at the moment. For this group, marketers need to keep promoting the entertainment value of the tickets relative to other options and marketing the quality of the product. In sum, marketers need to understand that there are different types of loyalty and people will need to be handled differently depending on the type of loyalty.


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Brand Attitude & Consumer Pathway The reason that we started talking about loyalty was to show the similarity between brand attitude and brand loyalty. Brand attitude is the first two stages of brand loyalty (cognitive and affective loyalty). We measure the last two stages of loyalty differently and that is shown on the consumer pathway as well. There are other terms that researchers have used to measure brand attitude as well, such as commitment, involvement, engagement, identification, and attachment. In general, though they are all pretty much the same thing as they all try to capture the connection to the team. Commitment has been defined in a variety of ways (see Pritchard, Havitz, & Howard, 1999, for a review), but Crosby and Taylor’s (1983) definition as the ‘tendency to resist change’ seems to reflect the idea the best. However, when Mahony, Madrigal, and Howard (2000) created the Psychological Commitment to Team (PCT) scale, it incorporated cognitive, affective, and behavioral items, supposedly measuring commitment. The PCT is correlated with other measures of connection, such as involvement. Involvement is poorly defined by Beaton, Funk, Ridinger, and Jordan (2011). They say that sport involvement is present when individuals evaluate their participation in a sport activity as a central component of their life that provides both hedonic and symbolic value. They hypothesized that involvement was a multi-dimensional measure including hedonic value, centrality, and symbolic value. This lasted about two years. In Doyle, Kunkel, and Funk (2013), they referred to the three dimensions as pleasure, centrality, and sign. Pleasure is an affective dimension of enjoyment. In Doyle et al., it refers to the enjoyment of watching sport. The centrality dimension is a cognitive dimension of importance of following a team (at least in Doyle et al.). The sign dimension is more of a behavioral dimension as it refers to behaviors reflecting the individual (Doyle et al.). These dimensions are correlated with the commitment dimension of Mahony et al. and with media consumption behaviors not surprisingly (Doyle et al.). Engagement has not been investigated to the same extent as some of these other concepts, but Yoshida, Gordon, Heere, and James (2015) looked at engagement as the behaviors that a fan engages in to promote their association with the fan community. They defined it as the “consumers’ escalating behavioral involvement in a fan community that includes socially committed behaviors such as self-expression, story-telling, and fan community participation” (p. 108) as per Schau, Muniz, and Arnold (2009). So, they have limited it primarily to behaviors. They found that it was relatively highly correlated with fan community identification and positive word of mouth (advocacy), but not attendance.


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Brand Attitude & Consumer Pathway Identification, as I noted in Chapter 2, has been examined from both the identity theory perspective and the social identity theory perspective. Like I explained in Chapter 2, social identity theory looks at the individual as a member of a group, reflective of how Yoshida et al. (2015) looked at fan community identification. The person sees herself as being a member of a group of like-minded fans, forming a community. This is considerably different from how researchers using identity theory look at it. From their perspective, the person sees herself as a fan of the team, irrespective of anyone else. Trail and colleagues used this perspective to derive the concept of points of attachment. Each point of attachment is a different role identity for the person. Each role has a different level of importance. Each role can be salient at different times or may be salient at the same time is some instances. Points of Attachment As I discussed in the fan socialization chapter, people become fans of a team in different ways; that is, they are socialized into being a fan by family, friends/peers, the media, organizations, geographical, support for causes, location/community, and sport itself. However, although the major focal point is the team, the team is not necessarily the only point of attachment that an individual may have (Figure 10.7).

Figure 10.7

For example, people may be attached to a player or players on the team. People are big fans of Russell Wilson and Tom Brady, LeBron James and Steph Curry, Giancarlo Stanton and Clayton Kershaw, Maya Moore and Elena Delle Donne, Ronaldo and Messi. The attachment to the player may continue as he or she moves from college to the professional ranks or moves from team to team in the pros. This


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Brand Attitude & Consumer Pathway was readily evident when Michael Jordan unretired for the second time and came back to play for the Washington Wizards. Many people who were Jordan fans when he was with the Bulls, became Wizards fans for the two years that he played for the team. The connection to the team was solely due to the connection to the player. At the collegiate level this is often seen when family and friends of an athlete become fans of a college team because the athlete now plays for that university. People may also be attached to the coach. Several examples come to mind. When Steve Spurrier left the University of Florida Gators to become the head coach of the Washington Redskins, many Gator fans began to follow the Redskins because of their attachment to Spurrier. Similarly, when Phil Jackson left the Chicago Bulls and eventually became the Los Angeles Lakers coach, many former Bulls fans started following the Lakers because of their attachment to Jackson. At the collegiate level, the attachment to the coach is often apparent when a coach has been successful over a long tenure, for example Mike Krzyzewski, the men’s basketball coach for the Duke Blue Devils, or Paul “Bear” Bryant when he was at the University of Alabama. Many peoples’ connection to the team is because of their attachment to the coach. At the same time, people can be fans of the team, and dislike the coach. Interestingly in the Shreffler and Trail (2010) research, Cubs and White Sox fans were socialized into being fans of the respective managers in different ways. Fans of the Cubs’ manager were socialized into their attachment by family. However, fans of the White Sox manager were socialized into it by their connection to the local community, in this instance, the south side of Chicago. Another potential point of attachment is the organization. Typically, a greater distinction between the organization and the team is seen at the collegiate level. People can be attached to the organization (the university) and have no attachment to a team. For example, a faculty member could be highly attached to the university for which she or he works, and yet have absolutely no interest or connection to that university’s basketball team. The same can apply to many alumni, who are attached to their alma mater, but have no attachment to the sport teams. A distinction is made between the organization and the sports teams associated with the organization. At the professional level this distinction is rarely if ever made, because typically the sport organization is part and parcel with the team. You rarely hear someone say, “Well I’m a LA Lakers fan, but I can’t stand the team.” It is certainly possible, but usually is expressed by the fan saying that they do not like the coach or some of the players, but not the team as an entity. Regardless, many people are fans of collegiate teams because of their association with the university, either as alumni or as faculty, or by some or organizational connection as we discussed in the socialization section. People can be connected or attached to a team because of a connection to the sport. Through socialization into a particular sport, an individual can become a fan of a particular team. However, there usually has to be some other type of connection in association with the attachment to the sport, such as an organizational attachment or player attachment. For example, an individual may have become a basketball fan because they started playing basketball in elementary school (socialized into it because of friends), and enjoyed it so much that they like watching it as well as playing it. However, just because the individual likes the sport, it does not cause the individual to become a fan of a specific team. On the other hand, if the individual hates the sport (basketball), it is highly unlikely that the individual will become a NY Knicks fan. For many people, an enjoyment of the sport preceded an attachment to a


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Brand Attitude & Consumer Pathway particular team, but not in all cases certainly. Furthermore, it is possible to be a fan of a particular sport and not be a fan of any individual team, but it is not very likely. Two agents played a part in socialization of people into being baseball fans in the Shreffler study (Shreffler & Trail, 2010). One agent was family. People who were Cubs’ fans indicated that they became baseball fans in general because of someone in their family. However, White Sox fans indicated an interesting result. The more of a general baseball fan they were, the less a socialization agent friends were; that is, there was a negative relationship between the two. Apparently on the South-side of Chicago, friends don’t let friends be baseball fans. For some individuals, the attachment to the sport is too broad. For these individuals, they may prefer a certain level or type of sport. For example, many people prefer collegiate sport over professional sport. Others may prefer women’s sport over men’s sport, or vice versa. A preference of type or level of sport over another is typically due to support for a cause. For example, many people prefer collegiate sport over professional sport because they think that it is a “purer” game played by amateurs. This argument is becoming harder to make anymore as the higher levels of intercollegiate athletics is becoming more similar to professional athletics every year. However, the belief that collegiate sport is played by amateurs who are playing for the fun and enjoyment of the game and not because it is the player’s job and the player is being paid for it, appeals to many people. Other people prefer professional sports because they believe that pro sports are the pinnacle of athletic accomplishment. The pro game is perceived as being the best quality, being played by the most competent players in the world. Some people also have preferences based on the gender of the players. Many people are fans of women’s teams because they support women’s sports opportunities. In some of Funk and his colleagues’ work (Funk, Mahony, Nakazawa, & Hirakawa, 2001), support for women’s teams and leagues were often


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Brand Attitude & Consumer Pathway driven by the idea that by supporting the team and/or league was providing support for equality for women. Funk et al. noted that “In a capitalist society, professional sports survive and thrive only if they receive support (i.e. money) from spectators and corporate sponsors. If fans choose not to support them, these professional opportunities will disappear” (p. 296). Finally, some people are fans of a team because of their geographical location and connection to that community. Many people are fans of the team that is in the local community. If the fan lives in the Seattle area, she is a fan of the Mariners and Seahawks. For many people, they have become socialized into this fandom by the community itself and the media in that community. They feel that supporting the team is supporting the community that they live in. However, again there typically has to be some other point of attachment other than just the association with the community; either an attachment to the sport or the organization, or some other point of attachment. Many people live in large cities and have no attachment to any of the community’s teams. Shreffler and Trail (2010) found that Cubs fans felt attached to their community (north side of Chicago) because of their geographical association, either growing up there, living there, working there, etc. However, they also felt a connection to their community because of the influence of the organization itself. So there may be some reciprocity being evidenced here. With the White Sox fans, there was not the same connection for some reason. Overall, Shreffler and Trail (2010) found a fair amount of support for the connection between socialization and attachment model they tested. The largest relationships are depicted in both of the diagrams below; one for Cubs fans (Figure 10.8) and one for White Sox fans (Figure 10.9).


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Brand Attitude & Consumer Pathway Figure 10.8

Figure 10.9


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Brand Attitude & Consumer Pathway Trail and his colleagues (Robinson, Trail, Dick, & Gillentine, 2005; Trail, Robinson, Dick, & Gillentine, 2003) have shown that these points of attachment exist and have differential relationships with each other. For example, they found that typically the attachment to the team, the coach, the players, the community, and the organization are related to each other. The other two points of attachment, attachment to the sport itself and attachment to the particular level of sport were related to each other, but not related to the other points of attachment very highly. We can extrapolate their research and Funk and his colleagues’ research to propose a model that looks like Figure 10.10. The diagram depicts that attachment to the player, coach, organization, and community all contribute to the attachment to the team. Attachment to the type of sport, the level of sport, and the gender of the sport contribute to the overall degree of being a sport fan. Notice also how being a fan of the team can contribute to being a sport fan but being a sport fan does not contribute to being a fan of the team. This certainly makes sense. Just Figure 10.10 because Joe Fan is an overall sport fan, it does not necessarily make him a fan of the Seattle Mariners. He even could be a baseball fan, a fan of major league baseball, and still not be a fan of the Mariners. However, if he is a fan of the Mariners, it is likely that he considers himself a sport fan, and probably a fan of Major League Baseball and baseball in general. This was supported by the findings of Woo, Trail, Kwon, and Anderson (2009) who showed that fans of the team were sport fans, but not vice versa. Points of attachment impact brand attitude differentially as is apparent in Figure 10.11. In this data set on Formula 1 race attendees from the Ballouli, Trail, Koesters & Bernthal (2016) research, it was the attachment to the sport of racing in general that predicted the most variance in a positive brand attitude toward Formula 1. Attachment to the race itself (COTA – Circuit of the Americas) was second,

Figure 10.11 followed by the attachment to a specific team and a specific driver. Attachment to the community (or pride-in-place) had no impact whatsoever on brand attitude. This is important to marketers when they are marketing the event. In this case, marketers had marketed the idea of supporting the community in hopes of improving the brand attitude and attracting people to the event.


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Brand Attitude & Consumer Pathway This didn’t work as you can see from Figure 10.12 as attachment to the community explained about 2% of intentions to go to the next race (.135 x .135 = .018 or 2% of the variance of conative loyalty). On the other hand, marketing the attachment to Formula 1 and the attachment to the race itself, would be a really good idea; attachment to COTA (the race) predicts 36% of going next year and attachment to Formula 1 predicts 43%. When all of the points of attachment are included, they explain a total of almost 60% of future attendance.

Figure 10.12 As we know from the Revised Structural Model of Sport Consumer Behavior (e.g., Figure 8.2), product attributes and benefits can also predict brand attitude, but they can also predict different points of attachment as well. For example, if the COTA marketers wanted to know what product attributes and benefits predicted attachment to Formula 1, so that they could emphasize those aspects in the marketing messages, they could look at the relationships depicted in Figure 10.13. The data show that product attributes explain 51.7% of the variance in attachment to Formula 1, primarily driven by the attributes of drama and Figure 10.13 driver skill. Product benefits explain less in attachment to Formula 1, but still a fairly large amount (43.2%), primarily driven by information provision and nostalgia. You might note the green number of 57.4%. This represents the combined amount of variance explained by both attributes and benefits (see sidebar on next page). Knowing these numbers, a marketer would market Formula 1 using images of dramatic racing and skilled drivers. The marketer would also provide lots of information about Formula 1, the skill of the drivers and the drama of the racing. In addition, marketing communications would play on the nostalgic events of dramatic past races where driver skill played an important part of an exciting finish.


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Back in Figure 10.12, we could see that Attachment to COTA was the second most impactful point of attachment predicting intentions to attend in the future (conative loyalty). COTA (Circuit of the Americas) is the race track itself. So, the marketer, employed by COTA, would want to know what creates that attachment to COTA in the first place. Although we didn’t collect socialization factors, we do know Social Interaction is a primary benefit that draws people to the race (Figure 10.15). In addition, the primary non-product related attribute is the aesthetics of the race venue itself. Thus, the marketer would focus marketing those aspects to this group of people.

Figure 10.15


Now you are probably asking yourself, why can’t we just add up the amount of variance explained by each of the variables to get the total. Unfortunately, it doesn’t work that way because benefits and attributes are correlated with each other as well. You can’t double count the correlated part. A Venn diagram (Figure 10.14) is probably necessary to explain this concept better. Product Attributes and Product Benefits are each a separate circle, but they overlap to some extent. That overlap is the shared variance between the two (squared correlation); in other words, it is the similarity between the two concepts. In addition, each overlap with Conative Loyalty. This overlap represents the amount of variance that attributes and benefits predict in conative loyalty (intentions to attend in this case). From Figure 10.13 above, we know that the total variance predicted is 57.4%. This is represented by the pinkish part in the Venn diagram. However, as you can see, there is a darker blue part underneath the pink part, where all three circles overlap. This is the part that both attributes and benefits explain in conative loyalty but is also the shared variance between the two (attributes and benefits). You can’t count this part twice. In other words, you can’t give credit to both attributes and benefits for this section. You can give it to one or the other, but not both. That is why the total amount of variance explained in conative loyalty (57.4%) is smaller than the combined amount (51.7% + 43.2%). You might, as I did, think that you could calculate the difference between the combined amount and the total and get the shared amount. Unfortunately you can’t. There is a mathematical reason for this that is way beyond this book, but if you are really interested you can read Cohen and Cohen (1983).

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Brand Attitude & Consumer Pathway Attachment to the Figure 10.16 racing team is the next largest predictor of attendance intentions (Figure 10.12). So, the marketer would want to know what predicts Attachment to the Team (Figure 10.16). Not surprisingly, success is the best product-attribute predictor and information provision is the best product-benefit predictor. The marketer here would want to communicate successes in great detail to the people that were primarily interested in the racing team. This information helps a couple of different marketers actually. It helps marketers for the team obviously, but it also helps marketers for the race itself, because they know that they need to market the teams as well. Figure 10.17 The next point of attachment that the marketer of the race wants to look at is the attachment to the sport of racing in general. People in this group might also enjoy Indy Car racing and other types of racing as well as F1. Fans of racing are attracted to it because of the skill of the drivers, the drama of the racing, and nostalgia of past races (Figure 10.17). The marketer needs to provide these people with information, communications, and marketing promotions that focus on these aspects (i.e., driver skill, racing drama, nostalgia, and success of the drivers or team).


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Attachment to the driver is the second to last point of attachment. It still explains about 22% of the variance in future race attendance intentions (Figure 10.12). It doesn’t predict attendance intentions as well as the aforementioned points of attachment, but it still does a pretty good job. The marketer for COTA would want to create some communications that promotes the success of the driver and his skill (Figure 10.18). Sorry ladies, currently there are no female drivers in F1. Only two women have ever qualified for a F1 race, and Desire Wilson is the only woman to ever win one, back in 1980.

The final point of attachment that was included in this data set was attachment to the community, in this case Austin, Texas. If you go back and look at Figure 10.12, you can see that Attachment to the Community explained a neglible amount of variance (2%) in attending next year. What this means is that marketing “Pride in Place” would have no impact whatsoever in causing people to come to future races. If it had though, the COTA marketer should focus on ads showing the look of the new cars and the sound (Figure 10.19). Interestingly the new engines in the cars that year were much quieter than the old engines. If you look at Figure 10.19 some of the path coefficients from Sound to some of the other points of attachment, such as racing, you will see a negative number, indicating that the quieter engines had a negative impact on fans of racing. They did not like them!


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Brand Attitude & Consumer Pathway However, for those that were attached to Austin, an environmentally sustainable city, they preferred the quieter engines. All that doesn’t really matter though because not many of those people came to watch the race 😊. Renton RoadRunners Example Let’s look at our fictitious RoadRunners example and see what information the marketers were able to collect regarding points of attachment and brand attitude. Remember that the data below is real, it just happens to be from a different team (obviously) other than the made-up RoadRunners. The survey accessed people in the team’s database, thus there was at least some connection to the organization already otherwise they wouldn’t be in the database. Brand Attitudes – Points of Attachment Attachment to the Sport (Basketball)

Attachment to the Level of Sport Attachment to the Gender of Sport Attachment to Sport in General Attachment to the Coach

Attachment to a Player(s)

Attachment to the Organization Attachment to the Community Attachment to the Team

Brand Attitude

82.7% of the respondents indicated at least some attachment to the sport of basketball (scoring above neutral on the scale). 31.2% indicated a 7 on the 7point scale, indicating extreme attachment. Attachment to the sport explained about 9% of the variance in Brand Attitude. 45.8% indicated at least some attachment to the level of sport (minor league basketball), with only 2.2% indicating extreme attachment. Attachment to minor league basketball explained only 1% of Brand Attitude. N/A N/A 85.1% indicated that they were at least somewhat attached to the coach, but only 4% were extremely attached. Attachment to the Coach explained about 16% of Brand Attitude. 93.3% were at least somewhat attached to the players, with 8% extremely attached to the players. Attachment to the Player(s) explained about 19% of the variance in Brand Attitude. 77% were at least somewhat attached to the organization, but less than 20% were extremely attached. Attachment to the Organization explained about 34% of Brand Attitude. 95% were somewhat attached to the community, and almost 49% were extremely attached. Attachment to the Community explained a little less than 30% of Brand Attitude. 87.1% indicated that they were at least somewhat attached to the team, with 17.3% indicating extreme attachment. Attachment to the Team explained about 41% of the variance in Brand Attitude. 95.5% had at least a slight positive attitude toward the brand, with 22% having a very positive attitude for the brand (7 on the 7-point scale). Brand Attitude predicted 15% of the number of games that they would attend next year.

What we can learn from the RoadRunners example, is that the team marketers need to focus on building attachment to the team and taking advantage of the attachment to the community. Although attachment to the coach explained a decent amount of the variance in Brand Attitude, that data reflected attachment to the old coach and not the new one that was just hired for the upcoming year. Focusing marketing plans on attachment to the players is also somewhat problematic as again the data reflects last years’ players, many of


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Brand Attitude & Consumer Pathway whom have moved on, including the star player. Although there are some new players that are coming in that are supposed to be good, they haven’t played at this level, and so knowing how they would perform at the moment is impossible. Thus, the marketers decided to focus the team and the community, with a small campaign introducing the new coach, to try and build attachment to him. Summary So, what does all of this have to do with brand attitude and progress along the consumer pathway? Brand attitude is a cumulative concept as we have discussed above. What I mean by this is it covers (represents) the whole pathway. Those who have just become aware of the brand, may not have any attitude whatsoever toward the brand. However, as they move along the consumer pathway, a brand attitude will develop. In the interest stage, the attitude can be negative or positive as the individual learns more about the brand. However, after this stage, the brand attitude pretty much needs to be positive, to move along the pathway. In addition, it will need to be increasingly positive to move further along to some of the stages such as attitudinal loyalty and repatronage behavior. We also discussed how many different terms are used in the sport marketing literature to describe connection to the brand or team. One such is attachment and there are many points of attachment. Each point of attachment either is a component of brand attitude or is related to brand attitude. In sum, it is critical that sport marketers know what aspect of the organization or sport the fans attach to; that is, what are the components of brand attitude. For example, for the University of Florida women’s volleyball program, a large segment of the fans is attached to the coach, Mary Wise. The players come and go, but Coach Wise is a very stable, fan-friendly coach. If she were to leave the program, attendance and support for the team would dramatically decrease. The athletic department’s marketing team has realized that she is the focal point for a majority of the fans for the volleyball team and thus has promoted and marketed her by having giveaways of autographed merchandise, dinners with Coach Wise, participation with Coach Wise on radio and TV shows, etc. All of this impacts, or is part-and-parcel of, the brand attitude, which represents various stages on the pathway. As I pointed out earlier, someone who has just become aware of the brand will not have an attitude toward the brand yet. However, as they gain knowledge or become interested in the team (or brand), then a liking or disliking of the brand (attitude) is initiated. If the liking intensifies, then the person may intend to consumer the product, ultimately buy the product and consume it. The consumption will then amplify the brand attitude or decrease it if the experience is not a good one. So now it is time to move on to the experience itself and post-experience evaluation in the next chapter.


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Chapter 11 Expectancy (Dis)Confirmation & Satisfaction

What Can We Actually Expect?! I noted in the previous chapter that the Columbus Blue Jackets fans’ expectations for 2007-2008 season were not very high, even with a new coach. And rightly so. Although the team improved slightly in the 2007-2008 season, they still did not make the playoffs. However, in the following season (20082009) they did make it for the first time in team history, losing in the conference quarterfinals. That success raised expectations again, with fans now wanting further progress in the playoffs. Unfortunately, in 2009-2010 they finished with the second worst record in the conference, did not make the playoffs, and Coach Hitchcock was fired. The lack of meeting fans’ expectations killed attendance to start the 2010-2011 season, as the fans got tired of waiting for the Jackets to be successful. The Jackets did not qualify for the playoffs until the 2013-2014 season, where they lost in the conference quarterfinals. With that “success” though, attendance jumped by almost 1000 fans per game to the highest level since 2006-2007 for the 2014-15 season and raised expectations once again. Unfortunately, those expectations were dashed as they just barely missed the playoffs, creating lots of dissatisfied fans, and attendance dropped back down again. However, in the 2016-17 season they had their best season ever, but once again lost in the first round. Attendance went back up. Expectancies this time were positively disconfirmed, creating satisfaction, leading to attendance increases. Copyright 2018 Galen Trail

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Guaranteed Win Night The Hartford Wolf Pack had an interesting marketing ploy to try and fill their arena; as the picture above shows, they desperately needed something to help them get “butts in seats.” Each season the team designated a game or two as a “Guaranteed Win Night.” The Wolf Pack promised their fans they would win the game on that night, or the fans would get a free game. If Hartford lost or tied that game, all fans, including season-ticket and game-plan-holders, with ticket stubs for the game, could exchange their stub for a complimentary ticket to an upcoming game. Fans received a ticket for a seat comparable to the seat designated on their stub. The Wolf Pack’s free seat promotion was an attempt to get people to come to the game whether the team won or lost. At first glance, giving away a free game does not seem like a bad idea; guaranteeing a win, however, is problematic. Expectations are raised, and if the team loses, the expectations are dashed. “Our fans have gotten spoiled.” During the 2009 season the Seattle Storm drew about 7900 fans per game, a 5 percent drop in attendance from the 2008 season, but still among the league leaders in paid attendance. The decrease came in spite of spending over $800,000 on advertising. As Evans (2009) reported, “Storm CEO Karen Bryant said the decline was partly due to the poor economy and the Storm not having a group-sales staff in place to push packages.” CEO Bryant came across as a little defensive when questioned about the Storm losing in the first round of the playoffs for the fifth consecutive year. "I don't buy into the notion that the Storm and losers are any way synonymous," said Bryant (Evans, 2009). "Our fans have gotten spoiled. We've made the playoffs for six straight years. That is no small accomplishment. Sure, when you get there, anything


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less than a WNBA championship is a disappointment, but we have had a team that has competed every year." Bryant’s off-the-cuff comment exposes two things. First, Storm fans have higher expectations than just making the playoffs each year; they expect to go back to the Finals. When the Storm does not

live up to these expectations, fans are not happy, and the CEO feels it. Second, Bryant needs to be a little more careful in her comments. Calling her most loyal consumers “spoiled” does not help her cause at all. Supposedly there was a backlash by the fans after the comment with some not renewing their season tickets. However, all was forgotten and forgiven after the Storm won the 2010 WNBA Championship. Season ticket renewal was at 87% and the Storm led the WNBA in new season ticket sales. Overview As I pointed out in Chapter 2, the examination of sport consumption does not end after the individual consumes the product. The post-consumption evaluation is just as critical if not more so than the pre-consumption aspects. One of the first people to examine different post consumption aspects and investigate the relationships among the (Dis)confirmation aspects was Oliver (1977). of Expectancies Figure 11.1 In his satisfaction theory he suggested that expectancies about the product are either confirmed or Satisfaction disconfirmed. (Dis)Confirmation then leads to satisfaction or dissatisfaction with the product. If the individual Behavior was satisfied, then he or Copyright 2018 Galen Trail

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she would purchase the product again (Figure 11.1). If not, then there would likely be no repeat purchase. Building on Oliver’s (1977) work, Trail, Anderson and Fink (2005) proposed and tested several different models which included confirmation and disconfirmation of expectancies, affect (which included mood and satisfaction), self-esteem responses and behavioral intentions. I have depicted this in Figure 11.2.

Figure 11.2 Let’s take you through what happens after you consume a product - a game in this example - for the first time. The first thing that happens is that your expectations about the game are either confirmed or disconfirmed. The (dis)confirmation stimulates an affective (mood) response, either you feel good about the experience or you do not; you feel satisfied or not. Based on these feelings you probably also have a self-esteem related response, either BIRGing (Basking In Reflected Glory) or CORFing (Cutting Off Reflected Failure). I will talk about these aspects more in the next chapter. After you BIRG or CORF, you either intend to go to another game or not. Then, the whole process starts all over again. In this chapter, we are going to focus on the disconfirmation or confirmation of expectancies and the affective response from the consumption experience. Expectancy Confirmation/Disconfirmation As I noted in the last chapter, people have expectancies about most things, and definitely about the team, attending the game, winning or losing, team play, and the venue experience to mention a few. However, the crux of the issue here is not whether people have expectancies about these things, or specifically, game outcome or team performance; the critical point is whether the expectations will be confirmed or disconfirmed, and whether there will be a positive or negative result. For example, if you think your team will win and they do not, your expectation will be negatively disconfirmed (top left box in Figure 11.3); your expectation is disconfirmed, and it is in a negative way (losing). If you think that your team will lose, and they do, then your expectation is negatively confirmed (2nd box in from the left on the top in Figure 10.3). However, if you think that your team will win, and they do, then that is Copyright 2018 Galen Trail

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positive confirmation. Perhaps even better is when you do not expect your team to win (oh ye of little faith) and they come through with the major upset, that is positive disconfirmation. Your expectancy

Figure 11.3 was disconfirmed (you did not think the team would win, and they did), and it was in a positive manner (a win). The degree and the direction of the expectancy confirmation or disconfirmation influence the person’s mood dramatically. Specifically, if expectations are disconfirmed, mood is elevated more so than if expectancies are merely confirmed. Think back to the example where your team wins in a major upset when you were not expecting it. You are going to be ecstatic. On the other hand, if you expected the team to win and they did, you will be happy, but the intensity of your feeling typically will not be as strong as the feeling you have when the team wins unexpectedly. In other words, there is a difference between a “happy” feeling and a feeling of “ecstasy.” The effect is the same thing when the team loses. When your favorite team loses to a team you will admit is better overall (come on, be honest), you feel bad. When your favorite team loses to a team they absolutely should beat, you feel REALLY bad. In the latter situation, your mood is typically a lot worse. These effects are represented in the bottom set of boxes in Figure 11.3. The intensity of feelings is greater at the ends of the continuum than in the middle of the continuum because the expectations are disconfirmed, creating greater disappointment or euphoria than confirmation can. These distinctions among feelings or moods are described in the next section. Affect/Mood Ekkekakis and Petruzzello (2002) hypothesized that affect (or mood) could be expressed on two dimensions: activation and valence. Activation refers to the intensity of the feeling that an individual has. Sometimes people are very apathetic toward an object or an experience, indicating a low level of activation of affect. For example, one night a couple years ago, the New Jersey Nets were playing the Minnesota Timberwolves. The Nets were 3 and 33 and the Wolves were 8 and 29; a combined 11 wins between them. Why the game was televised is beyond comprehension. Even though I am a big NBA basketball fan, the game was not even slightly appealing (I did not care enough to turn on the TV). Valence, on the other hand, is the degree of pleasantness (or unpleasantness) that you feel. Back to the basketball game example, pretend I decided to watch the Nets and Timberwolves game. As expected, the game turned out to be really poor, and as a result I experienced an unpleasant affective Copyright 2018 Galen Trail

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response (a bad mood). However, the level of activation was not very high because I cared very little about the game in the first place. On the other hand, when I watched The Ohio State Buckeyes play the University of Oregon Ducks in the CFP National Championship back in Figure 11.4 January of 2015, being huge Buckeye fan (and alumnus), my activation level was very high. In addition, because the Buckeyes played well (except for the 4 turnovers!) and won, my affective response was very positive (i.e., a pleasant mood). Emotions, both positive, and negative, are affective states and can be placed into different quadrants in Figure 11.4 (Ekkekakis & Petruzzello, 2002). For example, “ecstatic” might be placed in the upper right quadrant, because when someone is ecstatic that (Ekkekakis & Petruzzello, 2002) typically implies that they are experiencing a pleasurable emotion and the feeling is likely intense. Similarly, “thrilled” would probably be placed in the same quadrant. “Dejected,” on the other hand, implies an unpleasant emotion, but still an emotion that is intensely felt. Dejected would be placed in the High ActivationUnpleasant Affect quadrant. “Despondent” might also be placed in the same quadrant. “Content” might be a feeling that describes someone in the Low Activation–Pleasant Affect quadrant. Content implies a pleasant mood, but the feeling is not very intense. Conversely, an individual who was “somber” might be placed in the lower left quadrant. Somber implies an unpleasant feeling but is not necessarily overly intense. It is important to understand that although the diagram has been divided into quadrants, and we chose to use descriptors as examples that easily fit into each quadrant, the quadrants are not distinct. Each of the two dimensions is on a continuum and thus feelings and emotions can, and do, exist anywhere within the circle. Feelings and emotions are purely affective, in other words, they supposedly do not have a cognitive component. For example, if your team wins on a last second shot to pull an upset, you do not think, “Oh! My team just made a last second shot! I should be thrilled.” There is no cognitive processing. The emotion happens immediately, Copyright 2018 Galen Trail

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even reflexively. Later you might cognitively appraise it and engender the emotion again, but during the initial event, emotion comes immediately. Harrolle and Trail (2006) noted that previous research has examined the relationships between the outcome of a game (win vs. loss) and the mood of the spectator at the end of the game. Malmon (1990) found a significant difference in the moods of fans based on whether or not their preferred team won or lost the game. If a fans’ preferred team won the game, their positive mood ratings increased, and their negative mood ratings decreased. Vice versa, fans whose teams lost showed a decrease in positive mood ratings and increases in negative moods. The outcome of the game was found to be the most powerful determinant of the fans’ post-game mood. Additionally, Malmon (1990) noted that a fan’s experience and resulting mood, based on the performance of the team, could have consequences on subsequent behavior. Harrolle and Trail (2006) noted, however, that Malmon (1990) did not take into account the degree to which someone was a fan of the team. Wann, Dolan, McGeorge, and Allison (1994) seemed to rectify that problem, finding that highly identified fans reported an increase in positive emotions following a win and an increase in negative emotions following a loss. They also found that a fan’s level of identification affected the degree to which emotions changed after a loss. Spectators low in identification showed fewer negative emotions relative to fans with high levels of identification. Although Wann, Dolan, McGeorge and Allison’s (1994) study was an improvement, Harrolle and Trail (2006) noted that neither of these studies examined the effect that the confirmation or disconfirmation of expectations about the outcome of a game had on mood after the game. Harrolle and Trail suspected that the expectations were important because Madrigal (1995) had found that expectancy (dis)confirmation explained 16% of enjoyment after a game. In addition, Trail, Anderson and Fink (2005) had explained approximately 37% of the variance in mood after the game. Thus, Harrolle and Trail hypothesized that, based on Oliver’s (1993) satisfaction theory, the disconfirmation or confirmation of expectancies about the game would mediate the relationship between outcome of the game and the individual’s resulting mood. Harrolle and Trail (2006) found that the outcome of the game predicted 34% of the attendee’s mood after the game. After the disconfirmation/confirmation of expectancies was subtracted out of the equation, however, the outcome of the game only explained 17% of the attendee’s mood. This indicated that the expectations (and whether they were confirmed or disconfirmed) explained the same amount of the attendee’s mood after the game as the actual outcome of the game. What is even more interesting, and very important from a managing and marketing point of view, is that the effects of a loss can be ameliorated by adjusting the expectancies of the fans. In the Harrolle and Trail (2006) study, fans who attended the game in which the team lost were unhappy after the game. However, those fans who, going into the game, expected the team to lose scored a half point higher on the positive mood scale. Although they were not happy, the fans were at Copyright 2018 Galen Trail

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least at the neutral point on the scale. Because the fans’ expectations were confirmed, even though negatively, they were not as unhappy as if they thought that the team was going to win beforehand. From a marketing and management standpoint these findings indicate that the sport organization should not increase the fan expectations beyond what is reasonable. An excellent example of this is when the University of Florida men’s basketball team won the NCAA championship in 2006. All of the players indicated that they would return for the following season, thus creating high expectations among the fans that there was a possibility for a repeat championship in 2007. The athletic department’s marketing staff immediately implemented a marketing campaign that focused on the idea of “Back-to-Back” and “Repeat” championships. This was an extremely risky marketing plan because it increased the expectations by the fans that the Gators should win a second national championship. Luckily for the marketing staff the Gators did win a second championship the following year. As Harrolle and Trail (2006) pointed out: After winning any type of championship, sport marketing departments at all levels need to consider the effects of fans’ expectations on their emotions during the next few seasons. When expectations for additional wins are high, fans will more likely be disappointed when their favorite team loses, which is inevitable in sports. Statistically, sport teams have an extremely low chance of winning championships season after season, and sport marketers need to address the best way to market championship teams. (p. 2). They happened to be wrong in that particular instance regarding the Gators, but right in general. If expectations are elevated too high, and the expectations are disconfirmed negatively by not winning another championship, the fans emotions will be considerably more negative than if the expectations had been moderated by the coaching staff before the second season. Harrolle and Trail (2006) focused primarily on the confirmation or disconfirmation of expectancies about outcome and did not take into account the expectancies about performance that we discussed in the previous Figure 11.5 section. So Harrolle, Trail, and Anderson (2007) examined whether the (dis)confirmation of each type of expectancy had a differential effect on mood (both positive and negative). As can be seen in Figure 11.5 (dis)confirmation of performance expectations is different from (dis)confirmation of outcome expectations (the shared variance is about 17% [.415 x .415 = .17 or 17%]; indicating that 83% of the variance Copyright 2018 Galen Trail

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is not shared!). More importantly, we see that (dis)confirmation of outcome has a greater effect on mood than (dis)confirmation of performance. Although there is a slight difference between the effects on positive mood (performance explains 17% of positive mood, whereas outcome explains 22% of positive mood), there is a much larger difference on negative mood. Performance explains only 7% of negative mood, but outcome explains 18% of negative mood. What the figure shows is that when the outcome is negatively confirmed or disconfirmed, a negative mood is much greater than if performance is negatively confirmed or disconfirmed. For example, if you thought that your favorite team was going to play well, and they did not (negative disconfirmation) you would be disappointed. However, your level of displeasure would be much higher if you thought your favorite team was going to win and they did not. Not surprisingly, most people are more influenced by the outcome of the game (their favorite team winning or losing) than they are by the performance of the team. This goes back to the old saying by Al Davis of the Oakland Raiders, “Just win baby,” even if the winning is ugly. Satisfaction Mood and satisfaction are highly correlated because satisfaction is both affect and cognition. Some researchers, including yours truly, do make a distinction between them because of the cognitive component of satisfaction. Think about mood; most people do not analyze why they are happy or not. Many people do, however, analyze why they are satisfied, which means we can divide satisfaction into similar components like we did with expectations. Specific to games or sporting events, spectators may be satisfied with the performance of the team or the athlete. They may also be satisfied (or not) with the outcome of the game or event. In some research that Harrolle, Trail, and Anderson (2007) did with potential college football fans, they found that satisfaction with performance and satisfaction with outcome were related but still distinct (the correlation between the two was .66). We interpret the correlation to mean that although people could be satisfied with the performance and not satisfied with the outcome, or vice versa, typically the two were related. Harrolle et al. (2007) also found that when people’s expectations about team performance and outcome were confirmed or disconfirmed, it influenced both satisfaction with performance and satisfaction with outcome differentially. As is shown in Figure 11.6, confirmation/disconfirmation of expectancies about performance explained a little more than 56% of the variance in satisfaction with performance. Remember that variance can be thought of as the amount of the total “pie” that can be explained by a predictor, in this case Figure 11.6 (dis)confirmation of performance (to calculate the variance, multiply the path coefficient by itself, e.g., .751 * .751=.564 or 56.4%). This 56% is considerably more than the amount of variance that (dis)confirmation of performance explains in satisfaction with outcome (only 21%; Copyright 2018 Galen Trail

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or .459 * .459 = .21). What this means is that if you have expectations about your team’s performance and they are confirmed then you are satisfied with the team’s performance. However, if your expectations about the team’s performance are confirmed, that does not have a lot of effect on your satisfaction with the outcome of the game (the 21%). That makes sense obviously because you would expect that performance would be more highly related to performance than outcome. Harrolle et al. (2007) also found that when expectations about outcome were confirmed or disconfirmed, satisfaction or dissatisfaction with outcome was impacted a lot more than satisfaction/ dissatisfaction with performance (53.8% to 12.5%). What this means is that if you have expectations about the outcome of a game, and they are confirmed, you are satisfied with the outcome of the game. In addition, your satisfaction level with the outcome is influenced considerably more than your satisfaction with the performance. From a marketing standpoint Harrolle et al. (2007) noted that if the team typically puts forth a great deal of effort, win or lose, this effort can be promoted, and it will have an impact on the satisfaction of the spectators, at least to some extent. Although the potential outcome could be promoted (e.g., “We will win”), the chances of negative disconfirmation may be too high to risk such a promotion. Harrolle et al. suggested that from a management standpoint, hiring coaches or general managers that promote effort, may be one way to ameliorate the deleterious effects of losing. Satisfaction with Venue Experience. The previous information on satisfaction is specific to satisfaction with the performance of a team and the outcome of a game. Those elements focus specifically on satisfaction with the core product (the game) and can exist whether the individual attends the game or watches it remotely. However, as I noted when discussing constraints in a previous chapter, people can identify constraints to attendance, many of which focus on the venue itself. In this section, we are going to focus on aspects specific to the venue, and the relationship between attendee satisfaction with these aspects and the perceived importance of these aspects to the attendee. The nice thing about a discussion of the venue is that management typically has at least some control over these ancillary aspects, unlike the core product. Satisfaction, as we noted earlier in the chapter, influences your attendance at the game to some extent. Most managers also believe that your satisfaction with the venue and your experience at the venue is expected to influence whether you attend future games as well. Management, however, needs to know more than just your overall satisfaction with the venue and/or elements of the venue. Management needs to also know whether these elements are important to you. For example, if you are satisfied with an usher’s treatment in giving you Copyright 2018 Galen Trail

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directions to your seat, does that typically have any impact on repeat attendance? The answer is a resounding “NO!” Satisfaction with ancillary items has little impact on attendance unless the item becomes important to the individual and something happens to make the person dissatisfied (i.e., a potential constraint, like we discussed in an earlier chapter). Trail, Anderson, and Fink (2002) examined six ancillary elements and used a Satisfaction/ Importance grid to examine the relationship between the two. A Satisfaction/Importance grid allows the relationship between levels of satisfaction and perceived importance to be graphed. As you can see in Figure 11.7, Satisfaction is on the y-axis and Importance is plotted on the x-axis. Satisfaction is scaled from 1 being “Extremely Dissatisfied” to 7 being “Extremely Satisfied.” A 4 indicates that Figure 11.7 the individual is neither satisfied nor dissatisfied. Importance is scaled from 1 to 7 as well, with 1 equaling “Very Unimportant” and 7 equaling “Very Important.” A 4 would equate to neutral on the importance scale. A diagonal line can be drawn through the grid as depicted in Figure 11.7. If fans’ scores fall above the diagonal line, that indicates that their satisfaction is greater than that element is important to them. Scores below the line indicates that they think the element is more important than their level of satisfaction. The grid can also be divided into four quadrants. If fans’ responses are in Quadrant #1 on some element, that means that they are satisfied, but the element is unimportant to them. In this case, Management needs to maintain at least some level of satisfaction, but might be able to cut back funding in the particular area because fans do not really care about this element. If scores fall into Quadrant #4, that indicates they think the element is important, but they are not satisfied. This is a major warning sign to management; they had better figure out how to fix the problem and quickly. Quadrants #2 and #3 are a little more difficult to interpret. Scores in Quadrant #3 indicate fans are dissatisfied with the element, but it is really not important to them. If the scores are above the diagonal line then that indicates that management needs to keep an eye on that element, but probably does not need to do anything about it because the level of fan-satisfaction is greater than the level of fan-importance. Fans are dissatisfied, but they really do not care enough about it to do anything. For example, if the usher does not greet you, a season ticket holder, by name, this might bother you, but probably not enough to do anything about it because it is not that important. If the score is below the line though, management needs to figure out what is wrong. It may not need to be fixed because people do not really care about it that much, but if it is easily fixable and does not cost much then management should fix it. If the element cannot be easily fixed, then management should keep an eye on it to make sure that it does not become a bigger problem. Copyright 2018 Galen Trail

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For Quadrant #2, if the element is above the line, that means fans are very satisfied and are more satisfied than the element is important. This is an element that the organization is doing well, and it is very important to do it well. If the element is below the line in Quadrant #2, management should fix it quickly because it is very important to fans and it will not take much to bump their satisfaction level up above the line. Specific examples for these ideas would probably help a little ☺. The six non-product related attribute elements that Trail et al. (2002) examined were: cleanliness of venue, parking, audio experience in the venue, restrooms, concessions, and venue/ticketing staff. They also segmented their analysis by gender of the attendee and gender of the sport (men’s basketball game vs. women’s basketball game). This was important because both teams played in the same venue and people attending the different games might have different experiences and/or impressions. In addition, males and females attending the games might have different opinions about the different elements. In each of the examples, all the elements ended up being in Quadrant #2, so all of the figures were simplified to only depict Quadrant #2. Figure 11.8 shows male attendees at men’s basketball games. As you can see, parking was the only real problem (the only aspect below the line). Males thought that parking was important and although they were at least somewhat satisfied, they were not as satisfied as they thought it was Figure 11.8 important. The issue seemed to have two aspects. First, donors received reserved parking spots close to the venue. Non-donors had to park farther out in the parking lot. Parking for non-donors was free, but first-come first-served, so many people came early to get closer parking. Those who came less than a half hour before the game started had to park a fair distance away and then walk past the reserved spots that were empty because the donors did not have to arrive early to get good parking. Management had not communicated this well enough to the fans, so there was a lot of bad will from non-donors attending games. Our recommendation to the athletic department was to publicize the availability of close parking spots with a minimum donation. Some of the fans donated and got close-in spots, others did not, but at least they then knew why those spots were reserved. The second Copyright 2018 Galen Trail

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aspect was described when I talked about constraints, but I will briefly discuss it here. The egress out of the parking lot was very slow due to incompetent parking lot staff. This caused a lot of angst for many attendees. The only other element that management really needed to be careful with was concessions. Although it was still above the line, it was close. Partially this was because at the beginning of the season, there was a limited selection of food (hotdogs, popcorn, candy, and pop). The food was not always fresh. Later in the season new items were introduced and the management pressured the concessionaire to improve the freshness. This seemed to appease people. The remainder of the elements was done well; people were substantially more satisfied than they thought those particular elements were important, indicating that the athletic department was doing a good job on these aspects. Females at the same men’s basketball game had slightly different feelings (Figure 11.9). They indicated that parking was even more important than the men and were even less satisfied with it, for the same reasons. Of course, Iowa in winter does typically have a lot of snow and cold during basketball season, so this might have had a small effect on the dissatisfaction with walking long Figure 11.9 distances through the parking lot ☺. Copyright 2018 Galen Trail

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Women were also less satisfied with the concessions than the men and deemed it more important. Management did realize this and fixed it by the middle of the season, alleviating this issue. Women also indicated that restrooms were close to being a problem. Primarily due to long lines. The venue had fewer women’s restrooms than men’s restrooms and considerably fewer toilets. The only reason that this was not a large problem at these games was that attendance was down for the men’s team this particular year, so there were fewer women using the restrooms than normal. Similar to the men, women were perfectly happy with the cleanliness of the venue, the audio experience, and the staff. For males attending women’s basketball games, the responses were very similar to the responses of males attending men’s basketball games (Figure 11.10). Males were more satisfied with everything than they thought those aspects were important, with the exception being parking again. In this case there were no reserved spaces, but egress was even more difficult because average attendance was higher at the women’s games. This was the first time that the women’s basketball team outdrew the men’s team across the entire season. The athletic department had not been ready for the volume of people attending the women’s games and had fewer staff directing the egress from the parking lot, causing long lines and much Figure 11.10 confusion as people tried to leave. Copyright 2018 Galen Trail

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Females at women’s games had a slightly different perspective than the men did about several things. One element in particular ended up being very expensive to fix for the athletic department and the organization that managed the venue. As noted above, the women’s basketball team set attendance records that particular year, exceeding an average of 10,500 attendees per game, far in excess of anything previously. The percentage of attendees that Figure 11.11 were female was also considerably higher than was normal for men’s games in the same venue. This created considerable issues for the facility that was originally built in 1971, with no expectation that women would ever attend sporting events in equal numbers to men. Thus, the facility was built with fewer women’s restrooms than men’s restrooms, and significantly fewer toilets. During that particular season, the lines for the women’s restrooms sometimes would wind through the concourses and women would miss whole quarters of basketball waiting to use the restroom. As is evident from Figure 11.11, the women thought that restrooms were important, and they were not satisfied. Figure 11.11 actually inflates the satisfaction level because of a multi-item scale that included satisfaction with the cleanliness of the restrooms and satisfaction with how well they restrooms were stocked with supplies, in addition to the item about the wait for the restroom. When the former two items were removed, the satisfaction level dropped down below 4.0 on the scale, indicating dissatisfaction. Many complaints were made to the management. The solution was to add more women’s restrooms to the facility and change one of the men’s restrooms to a women’s restroom. These structural modifications ended up costing hundreds of thousands of dollars across several years. In the end it was certainly worth it. Female attendees ended up being satisfied with their restroom experience and four years later set an attendance record with over 14,000 showing up for a playoff game. Parking, however, was still a problem that year, and had not been solved four years later. Apparently though it was recently solved with improved egress routes and additional parking lots constructed. Copyright 2018 Galen Trail

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Here is another example. This data is from the women’s professional soccer project (Nicefaro & Goobes, 2017). I haven’t included all of the aspects that we measured for satisfaction and importance, just the ones that had either large differences between women and men or that the sport organization needed to deal with (Figure 11.12). Women were more satisfied with the match day experience in general than the men, but there were minimal differences between the two groups on level of importance. Women, more than men, thought that security was important, but both were similarly satisfied. Both women and men thought that the venue experience was important, and they were not adequately satisfied compared to the importance. Cleanliness of the restrooms was much more important to women than men, but neither were satisfied at all. Finally, both men and women thought that the quality of concessions, wait time for concessions, and variety of food options was important, and both groups were not sufficiently satisfied.

Figure 11.12

Managers for the team need to fix these issues. You might be thinking that why does security need to be fixed. Both groups are above the line. Yes, that is true, but the problem is that women don’t feel sufficiently secure. Once the managers fix these issues, then marketers need to communicate that the problems have been fixed and how much better the concessions are now, for example. The question then arises whether (or how much) these things really impact future attendance. As we noted earlier, sometimes if these things are really bad, then it will prevent people from coming, but if they are really good, it won’t make people come to more games than they would have anyhow. To determine that, we need to look at the correlations. Copyright 2018 Galen Trail

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Looking at the path coefficients in Figure 11.13, we can see that only satisfaction with the gameday experience directly impacts attendance intentions, but the variance is small (6%). However, many satisfaction aspects indirectly Figure 11.13 impact attendance intentions either through supporting the team or through satisfaction with the matchday experience. For example, satisfaction with concession offerings impacts gameday experience (.314), which then impacts attendance intentions (.248). Satisfaction with the process of entering the venue (ticket takers, bag searches, waiting in line, etc.; .286) indirectly impacts attendance intentions through the supporting the team variable (.443). However, this indirect effect is rather small (.286 * .443 = .13; .132 = .01 or 1%). So overall, these aspects of satisfaction don’t really impact attendance intentions very much. Taken together with Figure 10.12, even though there are some aspects where the fans are not as satisfied as they could be, relative to importance, it really doesn’t change their attendance intentions. Specifically, let’s say I scored a 3 on the 7-point scale on Satisfaction with Matchday Experience. To get me to go to just one more game for the season, the team would have to move me up to a perfect 7 on the Satisfaction scale. The way you figure this out is by looking at the correlation value of .248. That means for every increase of one point on the satisfaction scale, I would go to .248 more games. To get me to go to one more game you would have to multiply .248 by 4 to equal 1 approximately. Thus, you would have to move me up four points on the satisfaction scale to get me to go to one more game. That’s a pretty tall order for most managers. Copyright 2018 Galen Trail

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Renton RoadRunners Example Let’s look at our fictitious RoadRunners data set and see what information the marketers were able to collect regarding (dis)confirmation of expectancies, affect, and satisfaction. Remember that the data below is real, it just happens to be from a different team (obviously) other than the made-up RoadRunners. (Dis)Confirmation of Expectancies, Affect, & Satisfaction (Dis)Confirmation of Expectancies (Performance) (Dis)Confirmation of Expectancies (Outcome)

Positive Affect

Negative Affect

Satisfaction with Performance Satisfaction with Outcome

Satisfaction with Concessions

Satisfaction with Cleanliness of the Venue Satisfaction with Parking Satisfaction with Employee Service Satisfaction with the Restroom Satisfaction with Seating

82.2% indicated that they thought that the team performed better than expected (i.e., that the effort was there). 36.8% indicated that the outcome of the previous season was much better than expected. 2.3% indicated that it was as expected, and 60.9% thought it was worse than expected (ergo the firing of the coach). Positively exceeding outcome expectations only predicted about 5% of satisfaction with outcome and less than 2% of attending next season. 12% indicate that they were at least somewhat happy after the season. Positive affect predicted about 16% of supporting the team the next season and was correlated with satisfaction with outcome more than satisfaction with performance. 80.5% indicated that they were at least somewhat unhappy after the season. The more they were unhappy after the season, the less likely they were going to attend games next year. 74.8% were satisfied with how the team performed, but that didn’t predict more than 2% of the variance in supporting the team the next year. Only 14.6% were satisfied with the outcome of the season. Satisfaction with the outcome predicted about 35% of whether the attendees would support the team during the next season. 39.7% were at least somewhat satisfied with the concessions. The less satisfied they were with concessions, decreased the likelihood of attending but not by much (4% variance). Satisfaction with concessions would have to drop three points on the scale for people to come to one fewer games. 64.2% were at least somewhat satisfied with the cleanliness of the arena, but it had no impact on attendance intentions. 76% were at least somewhat satisfied with parking, but it had no impact on attendance intentions. 56% were at least somewhat satisfied with the service by personnel at the venue, but it had no impact on attendance intentions. Only 43% were at least somewhat satisfied with the restrooms, and it had a very slight, non-significant negative impact on attendance intentions. 68% were at least somewhat satisfied with seating at the venue, but it had no impact on attendance intentions.

Satisfaction vs. Importance A. Parking B. Restrooms C. Concessions D. Employee Service E. Cleanliness F. Seating


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What we can learn from the RoadRunners example, is that the marketers need to focus on making sure expectancies are not set too high with the new coach coming in and a bunch of new players. Management needs to fix the issues with the restrooms before they get worse and also needs to improve concession service, offerings, and prices. Parking, seating, and cleanliness are not problems. Employee service looks like it may be a problem soon if it is not already, so further investigation on this aspect to determine what the exact issues are, is necessary. Summary So, what does all this mean for our study of sport consumer behavior? Former CEO Karen Bryant for the Seattle Storm may be correct in the observation that fans are spoiled. That, however, is not really the issue. What we have to understand is the expectancies people bring to the sport consumption experience. We as sport marketers and managers need to realize that the (dis)confirmation of spectators’ expectancies will impact their satisfaction, which ultimately will directly influence their (future) behavior. As we have noted several times throughout the chapter, we can make our lives easier,

or at least somewhat less complaint-filled, if we do a better job managing expectancies. This approach will require that we understand what elements of our sport service are important to consumers, which of those elements we can realistically influence, and whether we are doing a good job managing those elements – or said another way, are consumers satisfied with their experiences. Remember though, satisfaction begins with the expectancies that consumers/ spectators bring to a consumption experience. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions


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Chapter 12 Self-esteem Behaviors & Behavioral Intentions

Cleveland Browns’ Fans: A Case of BIRGing Back in 2009, before no one would be caught dead in brown and orange, the Cleveland Browns teamed up with Dick’s Sporting Goods to 'Wear Your Team Out' and help paint the town Brown and Orange on Fan Fridays! Both organizations were encouraging fans to wear their Browns paraphernalia on Fridays throughout the season to show their team spirit and to promote their fandom with the Browns. Fans could even win a Cleveland Browns prize pack from Dick’s by submitting a photo displaying their team pride. Many fans already wore their jersey and other apparel on Sundays to support the Browns, but this was the first time the team tried to organize something like this.


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Washington Redskins Fans: A Case of CORFing Washington Redskins fans were so upset they were talking boycott. Dan Steinberg reported on his D.C. Sports Bog (that’s not misspelled, that’s what he calls it; a Sports Bog) that John Feinstein is so offended with the pathetic play of the Redskins that all Redskins fans should stop going to the games. According to Steinberg: "Let's get to the point here," Feinstein said. "The Washington Redskins have become the Saturday Night Live routine of the NFL, all right? The owner thinks he knows football and doesn't know football. He's got a henchman sideguy to his right, who's a radio talkshow host half of the week and is calling Bingo callers to fix the offense the rest of the week. It is a joke. Let me say this for the record, I know what Redskins fans are saying right now. They're angry, they're upset. Ok, DO something about it. DON'T go to the game Sunday. I'm serious about this. If you really want to send a message to the owner, stop calling radio talk shows, stop sending e-mails to this show, and DON'T GO on Sunday. Don't give the guy your money for parking, for concessions. "Tell him how upset you are by staying home and watching the game on television. They ought to just stay home. Don’t go. STAY HOME AND WATCH THE GOOD GAMES. DON'T GO!"


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Dallas Cowboys Fans: A Case of Blasting(?) Eatman, also in 2009, reported that Dallas Cowboy fans were upset, and for good reason. He noted: “All week long, the emails and phone calls have come in. The fans are mad, irate, humiliated, upset, disappointed or just downright pissed off. Some have given up, threatened to turn in their Cowboys gear and "40 years" of fan-ship all because they've had enough. In a way, a lot of Cowboys fans have given up; the same exact way they've accused the players of acting last week. And I'm sure the terms "gutless" and "heartless" will again be thrown around if there is indeed another loss this season, regardless of the final score. However, the more I think about it, the more it hits me - you guys are right. The fans should be upset. They should be extremely disappointed in "their team." It's not a shot against the players, owner or coaches. But these fans should have the right to be fed up with this. How many teams must they go through this same ending? The Cowboys have teased them for a few years now - some years a little more than others - but the result has pretty much been the same. After a while, you can't blame the fans for getting upset. And no, they're not going anywhere. For those of you who say you've had enough after 50 years and you've never seen a more embarrassing team . . . OK, we all know you'll be back in front of your TV on Saturday night with your Blue & Silver on, yelling and screaming for no one other than your neighbors to hear. And that's what this team needs. They need some passion like that.” Copyright 2018 Galen Trail

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Overview As I have noted in other chapters, need for achievement and social identity theory have been used to explain many aspects of sport spectating. According to Elliot (1999), the idea of achievement motivation may have originated with James (1890). Elliot suggested that achievement motivation was “the energization and direction of competence-based affect, cognition, and behavior” (p. 169). What do all those fine-sounding words mean? People seem to have a need for achievement (Maslow, 1943). As they achieve goals, they feel good about themselves. The feelings of accomplishment are satisfying. However, some people are unable to achieve as much as they would like to on their own, so they try to achieve vicariously (Sloan, 1989). Achieve vicariously? That simply means they “share” (in their own mind anyway), the achievements of others. As we discussed previously, this need for vicarious achievement can be fulfilled by association with successful others. Think about the following, what if you were having a “rough patch,” for example, perhaps you lost your job, or did not get a promotion you were working hard for, or maybe you are not as successful in social relationships as you would like, or you do not think you are achieving as much as you would like in your life. How would you feel? Not very successful. With all the rough stuff going on, you make it to the weekend and your favorite team wins a close game over a despised rival. How would you feel? FANTASTIC – WE WON!!! That is vicarious achievement. You find someone or something else that is successful to attach to. Through this association with a successful other, you experience success vicariously. Consider another example, while attending college I knew an individual who tried to make the varsity basketball team. Although he was able to play on the junior varsity team and had a couple of brief “sniffs” of the varsity, at the beginning of his junior year, he tore up his ankle for the second time, and was unable to play for an extended period of time. During the rehabilitation, he realized that he was never going to be good enough to play at the varsity level. This was quite a blow to his ego and his Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions identity as a basketball player. His self-esteem was highly connected to his ability as a basketball player. As he realized that one of his goals had no chance of being achieved, he felt a loss. As a coping mechanism, his attachment to the Los Angeles Lakers basketball team increased considerably. Although he was a pretty big fan before this happened, after the injury he became even more identified with the team. One of the reasons that he increased his attachment to the team was because the Lakers were extremely successful during this time in the 1980s. They had won several NBA championships and would continue to do so in the following years. The individual started wearing Lakers’ clothing more frequently, put posters up in his apartment, and in general expressed his connection with the Lakers as much as possible. He was trying to promote his connection with a “successful other” (the Lakers) in an attempt to make himself feel that he was successful and to get others to see the connection with the successful other. The latter point, wanting others to “see” the connection with a successful other is an important element of Basking In Reflected Glory (BIRGing).

BIRGing Wanting others to “see” you having a connection with a successful other is what Cialdini, Borden, Thorne, Walker, Freeman, and Sloan (1976) termed Basking in Reflected Glory or BIRGing. The brief snippet at the beginning of the chapter about the Cleveland Browns encouraging fans to wear their Browns paraphernalia is a good example of this phenomenon. As Tajfel (1982) suggested, people try to create a positive self-image by the strategic attachment to successful entities. Wann and Branscombe (1990) noted that within sport, individuals do this "as a means of moderating their public self-image or self-esteem" (p. 112). Trail, Anderson, and Fink (2000) extended this idea and suggested that, “individuals watch sports to fulfill achievement needs and hope to bask in the reflected glory of successful others. Being ‘a part of the team’ when the team is successful allows them to glean status and self-esteem through their identification and association” (p. 170). Although the connection with a successful other may help someone feel they are more successful, Cialdini et al. (1976) noted that it often is not sufficient unless others perceive the connection and make positive comments about it. These positive comments reaffirm the connection and increase the person’s feelings of the vicarious success. Thus, BIRGing behavior typically is evident by the person verbally letting others know about the association with the successful team, or publicizing the connection with the team by wearing apparel, and/or other behaviors such as flying team flags, having team credit cards, and putting team logos on one’s car. As Madrigal and Chen (2008) noted, “fans enhance their own esteem in the eyes of others by communicating their affiliation with a team whose actions they consider praiseworthy” (p. 721). So how much BIRGing behavior goes on and how much of an impact does it have on the bottom line for teams? Well, in 2009 after the New York Yankees won the World Series, over $450 million of Yankees championship related Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions merchandise was sold (Schwartz, 2010). Also consider the New Orleans Saints. After winning the Super Bowl in 2010, Trahan (2010) noted that Saints merchandise sales increased 60% compared to the previous season. That’s a lot of BIRGing going on! Not all people BIRG, however, and those that do, do not necessarily do it all the time. Wann and Branscombe (1990) suggested that sport consumers who are high in team identification tend to BIRG more than those who are low in team identification. Madrigal (1995) replicated that finding as did Trail, Anderson and Fink (2005). Madrigal and Trail et al. (2005) found that as team identification increased, BIRGing behavior increased as well. In an extension of previous research, Kwon, Trail, and Lee (2008) evaluated people that watched the New York Yankees play the Florida Marlins in the World Series in 2003. Kwon et al. determined that 44% of the variance in BIRGing behavior associated with the Marlins was due to the prior identification level with the Marlins. This may explain why the Cleveland Browns have now abandoned the “Wear the Team Out” Fridays. Ever since they started doing that, the team has not won more than 5 games in a season and in 2017 won none! Fans can’t BIRG if the team isn’t successful! All of the research mentioned above looked at the relationship between team identification and BIRGing behaviors. Kwon, Trail, and Anderson (2005) wondered whether other points of attachment, other than attachment to the team, would show a relationship. As we noted in the chapter on brand attitudes and the consumer pathway, people may be attached to a team through a variety of points: players, coach, organization, level of sport, type of sport, etc. Kwon et al. (2005) determined that team identification, not surprisingly, had the largest effect on BIRGing behavior. However, in their study of collegiate football fans, as the attachment to the university increased beyond the attachment to the team, BIRGing behavior increased even more. In addition, similar results were found with attachment to the coach and attachment to collegiate football in general. In all cases though, attachment to the team was the most important influence on BIRGing behavior, with the other points of attachment influencing BIRGing behavior significantly less. From a practical standpoint this seems to indicate that marketers should focus on the increasing attachment to the team as a whole, rather than looking at the different composite aspects of what may make up the team. Many researchers have wondered if the team identification– BIRGing relationship might be influenced by other things. Bizman and Yinon (2002) found that the relationship between team identification and BIRGing behavior may change based on team performance. This makes sense in that BIRGing behavior is supposedly predicated on positive team performance, and typically it has been hypothesized that people high in team identification would be less influenced by fluctuations in team performance. That is, loyal fans would not reduce their BIRGing behavior just because the team lost. However, what is interesting is that Bizman and Yinon found that even highly identified fans reduce their BIRGing behavior after a loss for a short period of time. Over the long haul though, loyal fans maintained their allegiance to the team, whereas those low in identification did not. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions The distinction between the BIRGing behavior of highly identified fans and those characterized by a low level of identification might be explained in part by the results reported by Kwon et al. (2008) mentioned earlier. They determined that as the need for vicarious achievement increased, the likelihood of BIRGing behavior increased as well. However, they also determined that this relationship was fully mediated by team identification. You may be asking yourself, “What does ‘fully mediated’ mean?” Good question. What this means is that the relationship between need for vicarious achievement and BIRGing behavior is entirely contingent on team identification. Think about it this way, as the need for vicarious achievement increases, the level of team identification increases, and then BIRGing behavior increases. People need to have a viable connection with the successful other before they will BIRG. It also means that once people form a strong connection with the team, need for vicarious achievement will have less of an effect on BIRGing behavior. This is critical for marketers to realize. People who have a high need for vicarious achievement will jump on the bandwagon when the team starts to win, however, their attachment to the team will not be very high at the beginning. Thus, if marketers want to retain those people, they need to help them increase their attachment to the team by encouraging other connections with the team, more than just the success of the team. Again, the Cleveland Browns obviously were unable to do that and attendance went down the following year as people jumped off the bandwagon. There are many variables that influence BIRGing behavior. I examined student perceptions of the Iowa State University football team and determined that a number of relationships existed. As we noted in the chapter on satisfaction, the positive confirmation or disconfirmation of expectancies typically leads to positive affect and satisfaction (see Figure 12.1). In addition, it was determined that positive affect, satisfaction with performance, and satisfaction with outcome all influence BIRGing behavior. This makes a lot of sense. As people become happier and more satisfied with the performance of the team and the outcome of the games played by the team, BIRGing behavior is likely to increase.

Figure 12.1

Positive feelings about the event will cause people to promote their connection to the team. This was evident with the students at Iowa State University. What was perhaps even more interesting is that satisfaction with the performance of the team was the most important predictor of BIRGing behavior. As the students were satisfied with the performance of the team (the players played hard and the quality of the play was good), they were more likely to BIRG. Satisfaction with the outcome did not play as great a part in predicting the BIRGing behavior. In other words, the satisfaction with the quality of the team’s performance was more important than the satisfaction with the outcome of the game. For the marketers of ISU football, this is a good thing to realize. It is more important to market the effort of the


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Self-esteem Behaviors & Behavioral Intentions players and the team than try to promote overall team success – especially when there is not a lot of team success. In addition, these results also showed that as BIRGing behavior increased, conative loyalty increased as well. Conative loyalty is represented by the intentions to attend games in the future, buy merchandise in the future, and consume media about the team in the future. This is an extremely important for marketers to understand. If they can encourage BIRGing behavior, it is likely that people will go to games more frequently. They are also more likely to buy merchandise, and as a result increase ancillary revenues. In addition, they are more likely to watch games on TV or go onto the websites to get information about the team. Knowledge about information in these areas can then be passed along to advertisers and sponsors to increase advertising and sponsorship revenues. In data collected by Harrolle and her colleagues (2010) from spectators at a Florida Marlins game, similar results were found (see Figure 12.2). However the difference between the satisfactionwith-performance to BIRGing relationship and the satisfaction-with-outcome to BIRGing relationship was not as large as in the Iowa State data. In the Marlins’ data, satisfaction with performance had less of an impact on BIRGing than in the ISU data, and satisfaction with outcome had a greater impact on BIRGing than in the ISU data. These results indicate that marketers need to survey their own fans and spectators to determine how important these aspects are before just assuming that satisfaction with performance is the primary predictor of BIRGing behavior.

Figure 12.2


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Self-esteem Behaviors & Behavioral Intentions Another point to consider from the Marlins data is that BIRGing behavior predicted 54% of the variance in conative loyalty (from Figure 12.2; .736 x .736 = .54 or 54%). This is substantially more than the 30% explained in the ISU data (from Figure 12.1, .55 x .55 = .30 or 30%). Once again this indicates two things. First it is important for marketers to examine their own fans and spectators, and second, increasing the opportunities for fans and spectators to bask in the reflected glory of a successful team is very important. As people are able to promote their connections to a successful team they are more likely to attend games, buy merchandise and consume media about the team. All of these things help to engender loyalty in people, increasing fan identification. As we noted above, if people do not identify with the team, when the team’s fortunes turn, these people who are only associated with the team because of the winning will probably no longer associate with the team. These individuals will “cut themselves off” from the unsuccessful team. This is called CORFing.

CORFing Anyone who follows sports understands that no team wins all the time, not even the New England Patriots. According to Snyder, Lassegard, and Ford (1986), people will distance themselves from unsuccessful others in order to maintain self-esteem levels. “Evidence clearly indicates that cutting off reflected failure can be distinguished as [an] image-protection strategy for the purpose of avoiding a negative evaluation” (Snyder et al., p. 387). Atkinson (1957) suggested people do this in order to avoid failure and the shame and humiliation that occur as a consequence of failure. What this means for us is that some fans will try to Cut Off Reflected Failure (CORF) when a team or player does not do well. This idea is illustrated in the excerpt about the Washington Redskins at the beginning of the chapter. I never heard whether this “boycott” happened or not, but attendance since 2009 plummeted like a rock dropping by 10% by 2011 before plateauing through 2017. There was a 12,000 attendance difference between 2008 and 2011, indicating that a lot of people CORFed! This distancing behavior allows an individual to maintain existing levels of self-esteem, whereas a continued association with an unsuccessful other might reflect poorly on the individual. Think about it this way, fans of some teams who are performing very poorly often put bags over their heads to show their disgust with the team and to figuratively disassociate themselves from the team. Although the two people at the New Jersey (before they moved to Brooklyn) Nets’ game in the picture to the left do not want to be recognized as fans of a poorly performing team, they still were at the game. They were also wearing Nets apparel. These actions indicate the two fans did not totally disconnect themselves from the team. Other individuals may completely cut off any connection to a team that is not winning. These people typically jump off the band wagon at any evidence that the team is no longer going to be as successful as it was in the past. Of course, these are also the same people who jump on the band wagon when the team is at the high point of success. It is entirely probable that these people are so driven by the need for achievement, that they are unable to take any “hits” to their self-esteem. These people disassociate with unsuccessful others extremely quickly in order to maintain their fragile self-esteem. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Madrigal and Chen (2008) suggested that CORFing and BIRGing are bipolar endpoints of a continuum of self-esteem behavior. If this idea is accurate, then, as people stopped BIRGing they would start CORFing, and there would be a high negative correlation between the two. However, it is possible that BIRGing and CORFing may not be at opposite ends of a single continuum. Previous research by Wann and colleagues (Wann & Branscombe, 1990; Wann, Hamlet, Wilson, & Hodges, 1995) indicated that just because a person is not BIRGing, it does not mean that the person is CORFing. Results from Trail and his colleagues lend credence Figure 12.3 to these results and give evidence supporting the idea that BIRGing and CORFing may exist on two separate parallel continuums (see Figure 11.3). Trail et al. (2005) tried to determine if people who were not BIRGing with the Iowa State women’s basketball team were instead CORFing away from the ISU women’s basketball. They found that BIRGing and CORFing were only related at a correlation of .450, indicating that as one increased, the other did not decrease at the same rate. The Trail et al. (2005) research focused on BIRGing and CORFing behavior in reference to one team. In contrast, in some more recent research, Trail and his colleagues focused on BIRGing and CORFing behavior with two different teams. They found that when television spectators of a NCAA National Championship game who had a low level of identification with either team were tested on BIRGing with the winning team and CORFing with the losing team after the game, there was a small positive correlation between the two (r = .260). These results “tell us” that as BIRGing increased with the winning team, CORFing only increased with the losing team to a very small extent (Trail, Kim, Kwon, Harrolle, Braunstein-Minkove, & Dick, 2012). What is even more interesting is that this varied by need for vicarious achievement. As the need for vicarious achievement increased, the correlation between BIRGing and CORFing increased to .388. However, for those who were low in need for vicarious achievement, the correlation was only .120. What all this really indicates is that BIRGing and CORFing are probably not on the same continuum. Theoretically it makes sense that BIRGing and CORFing are not on the same continuum. BIRGing behavior can be defined as a public promotion of the association with a successful other. It is a means of increasing positive public comments about the individual’s connection, thus supposedly allowing that person to increase good feelings about themselves and therefore increase their self-esteem. CORFing behavior is more self-centered. The individual is trying to disassociate themselves from unsuccessful others in an attempt to maintain existing levels of self-esteem. To do this, an individual is not going to say to another individual, “I am disassociating myself from that team.” If the individual were to do this, two things would occur; first the individual would have to admit that previously there was an association, which is antithetical to CORFing. Second, other individuals would probably derogate the person for being a bandwagoner and abandoning the ship in times of trouble. In both cases, this defeats Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions the purpose of CORFing because referent, or even non-referent, others would criticize the individual, thus negatively impacting the individual’s self-esteem. Therefore, CORFing must be an inner-centered behavior; clothing that promotes the association is discretely discarded, flags are taken down, all connections are removed, and the attachment is replaced with a different team that is successful for the moment. It is important to note that not all people CORF when a team loses. People who are true fans (highly identified with the team) do not CORF, whereas as some individuals who are not as highly identified with the team, or who became fans because of the team’s success, apparently tend to CORF more frequently (Wann & Branscombe, 1990; Wann et al., 1995). This information indicates that perhaps the level of fan identification has an influence on the fan's reactions to event outcomes. Trail et al. (2000) suggested that fans high in identification were much less likely to CORF after a defeat when compared to fans low in identification. Kwon et al. (2008) reported evidence to support this assertion when they found that people who were not identified with the NY Yankees were much more likely to CORF after the Yankees lost the World Series against the Florida Marlins in 2003. Those individuals who were fans of the Yankees, however, did not CORF even though the team lost. These results support the findings of previous researchers as well (Trail, Fink, & Anderson, 2003; Trail et al, 2005; Wann & Branscombe, 1990). Bizman and Yinon (2002) disagreed with the preceding. Their results indicated that even strongly identified fans CORF after a loss in order to “restore their self-esteem and positive emotions” (p. 391). Bizman and Yinon suggested that their findings, which are inconsistent with all previous research (and most research since as well), may have occurred because highly identified fans only CORF temporarily, immediately after the game, and then recover later, reestablishing their allegiance in the long run. Another explanation is that Bizman and Yinon did not really have highly identified fans in their study. Maybe they were studying bandwagoners that were jumping off the bandwagon. Kwon et al. (2008) found support for the idea that individuals “jump off the bandwagon.” They noted that people who were high in the need for vicarious achievement (NVAch), CORFed more frequently than those who had a low NVAch. In this specific study, those people who were high NVAch distanced themselves from the NY Yankees in order to protect their existing self-esteem. However, in the work by Kwon et al. (2008) and Trail et al. (2012), the researchers found that the relationship between NVAch and CORFing was mediated by team identification. What this means is that even when people were high in the need for vicarious achievement, if they were highly identified, they did not CORF. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Marketers need to take heed of this information. When a team starts losing (or perhaps continues to lose in some instances), people will continue to be loyal as long as they were highly identified with the team before the team started on its “road to failure,” even people who were high in the need for vicarious achievement. One marketing implication is to place more emphasis on promoting attributes or characteristics of the team that people are likely to consider personally important. As noted by Funk and James (2001, 2006), a strong psychological connection forms when a team becomes intrinsically important to an individual. In order to maintain a fan base through losing seasons, it is imperative for sport marketers to move beyond short term strategies that focus on social-situational (free give-aways) and hedonic motives (social interaction with friends) and focus on the personal importance of a team to fans. As we noted in the BIRGing section, confirmation or disconfirmation of expectancies can influence affect, which can then influence self-esteem behavior. In the study with the students at Iowa State University and their cognitions, feelings, and behaviors relevant to the football team, we found that as the team did worse than expected, negative affect and dissatisfaction increased. The team had been somewhat successful the previous year and the expectations were elevated. Unfortunately, the team did not win as many games as people had expected. This caused some of the students to be disappointed and dissatisfied with the team. As the disappointment increased, CORFing behavior increased as well. Some of the students started to distance themselves from the team in order to maintain their levels of self-esteem. However, as the small path coefficient indicates (.198; see Figure 12.4), that relationship was not very large. These results indicate that many of the students did not

Figure 12.4

CORF even though they were not very happy with the outcome of the season. In the ISU study, as CORFing increased, conative loyalty decreased. This indicated that those students that were CORFing did not intend to come to games the next season, did not intend to buy team merchandise, and did not intend to consume media about the team. These students intended to disassociate themselves from the team in order to maintain their levels of self-esteem and that meant no more games, paraphernalia, or TV. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Somewhat similar results were found by Harrolle, Trail, Rodriguez and Jordan (2010) with Florida Marlins spectators, except that the relationship between negative affect and CORFing was much stronger. Those spectators that were disappointed with the outcome of the game were much more likely to CORF than those who were not. In addition, they were much more likely to CORF than the ISU students. However, even though they were going to CORF, it did not influence their future behavioral intentions (conative loyalty) much. Although the path coefficient was negative (see Figure 12.5), the value was small, indicating that CORFing predicted less than 2% of conative loyalty. Apparently, even

Figure 12.5

though some people were going to CORF, it would not influence their future attendance behavior. There are two possibilities to explain this relationship. First, perhaps these people were not highly identified in the first place and thus were not intending on going to more games anyway, and also were not intending to watch games on TV either. A second explanation is that there were a small number of people who were going to CORF and those individuals would stop going to games, but the majority would continue to attend. The mean scores seem to indicate the latter. The mean for CORFing was very low, and the conative loyalty score was high. Furthermore, when the data was split between those high in team identification and those who were not, the relationship between CORFing and conative loyalty changed slightly. Those who were high in team identification did not CORF, and conative loyalty was still high. For those who were low in team identification, CORFing was twice as high and conative loyalty was about 25% less. Marketers need to consider the results from the preceding research. There will be a segment of people who will be dissatisfied or unhappy and will CORF. The CORFing will lead to declining attendance behavior, no longer buying merchandise, and no longer watching the team on TV or following them on the internet. However, it seems that these types of feelings and behaviors can be ameliorated by increasing the level of team identification.

Blasting Another self-esteem maintenance behavior exists according to Branscombe and Wann (1994) and Cialdini and Richardson (1980). For those fans who are faced with their favorite team performing poorly, but are truly loyal fans, CORFing does not seem to be an option. Wann et al. (1995) suggested


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Self-esteem Behaviors & Behavioral Intentions that these fans would need to engage in other coping mechanisms such as out-group derogation and/or aggression. This is termed Blasting. What is blasting you ask? Another good question. Fans, in order to maintain their own self-esteem, would derogate, or blame, the referees, the other team, or even the other team’s fans. This venting behavior – this Blasting - allows fans to show their displeasure and at the same time try to shift the blame for a team’s poor performance. Fans want to avoid the perception of failure by blaming poor performance on some factor other than the team’s actual performance. In other words, my favorite team lost, not because the team was bad, but because either the referees were biased or poor, or because the other team was not playing fair. These fans are too loyal to want to disassociate from their team, that is to CORF, but they are so displeased with the lack of success of the team they must do something, so they Blast. End (2001) found that fans of a losing team were much more likely to Blast fans of the winning team than fans of a winning team were to Blast fans of a losing team. Blasting may occur within the comfort of others within the in-group, where the Blasting is acceptable. However, Blasting is not necessarily a process in which is done to gain social approval like BIRGing and CORFing may be. Blasting other entities may occur when the other entities are unable to respond or unable to identify the Blaster; for example, booing the team or yelling at the referees. It is also possible that Blasting may occur when no one is around to hear. For example, I know someone very close to me, when watching his favorite team play on TV, blasts the referees and the other team quite frequently. The Blasting behavior seems to be an attempt to maintain self-esteem levels without CORFing and is done by people who are identified with the team (Branscombe & Wann, 1994; End, 2001). People who are not identified with the team CORF, and do not bother to Blast. In the vignette at the beginning of the chapter it was suggested that Dallas Cowboy fans were Blasting and not CORFing. This did seem to be the case as attendance didn’t vary that much and they have led the league every year since 2009, setting a record in 2017 with over 92,721 per game which is more than 14,000 more fans per game than the next closest team (Green Bay Packers). BIRFing and CORSing Campbell, Aiken, and Kent (2004) suggested two other potential outcomes that might be related to BIRGing and CORFing. They proposed a framework in which there were two general dimensions: Outcome and Association. The Outcome dimension refers to the outcome of the event (e.g., a game or a season). The Association dimension refers to the level of identification or connection the individual has with the entity (team). Each dimension has two potential demarcations: Positive and Negative. The categories of BIRGing, BIRFing, CORFing, and CORSing are supposedly determined Copyright 2018 Galen Trail

Figure 12.6

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Self-esteem Behaviors & Behavioral Intentions by this framework (see Figure 12.6, modified slightly from Campbell et al., 2004). The first new category Campbell et al. suggested is BIRFing - Basking In Reflected Failure. As Campbell et al. noted: While the team may be losing, fans in this case are reveling in the loyalty, camaraderie, rebelliousness, and other alternative reasons for fanship. The BIRFing fans may be managing selfimage through other positive characteristics of fanship. Since the team is not winning, the fan may highlight other positive aspects in order to manage their image. A primary desire may be avoiding being labeled a fair-weather fan (p. 153). The second category is labeled CORSing (Cutting Off Reflected Success). Even though the team may be winning, the individual wants to disassociate from the team. As Campbell et al. suggested: Here, we again posit reasons of rebelliousness, loyalty (to an earlier era, a previous style of play, prior coaching/management, etc.), a need for individuality (informally seen as a need to stand apart from the crowd), and possibly a fear of success (e.g., to ascend to new heights implies a chance for a greater fall). The CORSing fan does

not want to be associated with the new era of winning, but rather they prefer to stay linked to the past (p. 153). Although each of these new categories may have intuitive logic, neither the categories nor the framework has been tested empirically. Without supporting evidence, there is little reason to take these dimensions into account when marketing to fans. As was evident from the information several pages prior, BIRGing and CORFing typically have an influence on behavioral intentions. However, what behavioral intentions are we talking about, and do they only impact behavioral intentions. What else do they impact? Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Behavioral Intentions Behavioral intentions are also known as conative loyalty (Oliver, 1993) and when investigating intentions, I have typically included the intention to support the team, the intention to attend games, the intention to purchase merchandise, the intention to wear team paraphernalia, the intention to watch the games on TV (or more recently, streaming), in addition to social media consumption related to the team. Dean Anderson, Don Lee, and I Figure 12.7 collected some data from students at a university across a whole year (preseason, postseason, and end-of-the-year) regarding the college football team. We found that the number of games they attended during the prior year explained about 33% of the variance in the actual number of games they attended during the current season (Figure 12.7). The number of games they attended during the current season predicted postseason BIRGing and CORFing behavior to a small extent (9% and 4%, respectively). BIRGing and CORFing predicted postseason conative loyalty (behavioral intentions) relatively well, with BIRGing predicting 30% of the variance and CORFing predicting 12%. We then looked at whether the postseason conative loyalty would predict attitudinal loyalty 5 months later at the end of the school year. It did, explaining about 19% of the variance. Endof-the-year attitudinal loyalty also was related to end-of-the-year CORFing and BIRGing, which led to end-of-the-year behavioral intentions. BIRGing and CORFing showed some stability across time as well, as postseason BIRGing was correlated with end-of-year BIRGing at .624 and postseason CORFing was correlated with end-of-year CORFing at .527. What all of this indicates is that BIRGing and CORFing are related to behavioral intentions (conative loyalty) across time as well as at the same time. In addition, it also shows that the more games people go to, the more likely they are to BIRG and the less likely they are to CORF. From a marketing standpoint this supports what sport marketers intuitively know: If you can get them to the games, they are going to become fans and then they will stick with your team. Advocacy. I talked about advocacy back in Chapter 5 from the perspective of the organization, specifically how the organization should encourage consumers to be co-creators of the brand. This is obviously a behavior and behavioral intention that we want consumers to do. This WOM, whether it be through social media or person-to-person can have a large impact on what others do. For example, in the women’s professional soccer data set (Nicefaro & Goobes, 2017), we found that 71% of the respondents indicated that they planned to advocate the team to others. In addition, we found that those who were advocates were much more likely to support the team in the future, attend more games during the next season, purchase more merchandise for the next season, more likely to post on the team’s Facebook page, and even slightly more likely to purchase products from the team’s sponsors. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Renton RoadRunners Example Let’s continue to look at our fictitious RoadRunners data set and see what information the marketers were able to collect regarding Self-esteem behaviors and behavioral intentions. Remember that the data below is real, it just happens to be from a different team (obviously) other than the madeup RoadRunners. Self-esteem Behaviors and Behavioral Intentions BIRGing

CORFing Support the team in the future Purchase the team’s merchandise in the future Wear team’s paraphernalia in the future. Attend the team’s games in the future. # of games will attend next year. # of away games will stream next year.

$ spent on team paraphernalia for self. $ spent on team paraphernalia for others. Advocacy

67.2% said that they BIRG. BIRGing predicts supporting the team (24%), purchasing the team’s merchandise (22%), and wearing the team’s paraphernalia (23%), but not attending the team’s games in the future (4%). Only 3.4% admitted that they CORF. CORFing was negatively related to attending games in the future (18%), and supporting the team (12%). 95% said that they will support the team in the future. This predicts the number of games intending to attend next year (12%) but not much else. 82.6% said that they will buy the team’s merchandise in the future. This is positively related to the number of games going to attend next year (9%). 80% said that they will wear team paraphernalia in the future. Wearing paraphernalia is positively related to advocacy (17%). 97.8% said that they would attend the team’s games in the future (but this doesn’t indicate when), and only lower predicts the number of games they will go to next year (6%). 53% said they would attend more than 10 games. An additional 23% will attend 6-10 games, and an additional 20% would attend 2-5 games. This is lowly associated with streaming games (5%). 11% said they would stream more than 10 away games. An additional 12% will stream 6-10 away games, and an additional 13% will stream 2-5 games, but 53% said they won’t stream any. This isn’t really related to any of these things, probably because most don’t stream. 12% said that they would spend $100 on themselves, 13% said they would spend $50 to $99. 20% said they would spend $25 to $49, and 10% would spend $10- $24. The remainder, ~45% didn’t intend to spend anything. 8% said that they would spend $100 on others, 9% said they would spend $50 to $99. 13% said they would spend $25 to $49, and 8% would spend $1- $24. The remainder, 57% didn’t intend to spend anything. 67% said they would advocate to others about the team. Advocacy is correlated with supporting the team (22%), not surprisingly, and purchasing team merchandise (16%).

Marketers need to focus on making sure the attendees have opportunities to BIRG, buy inexpensive team merchandise, and encourage everyone to wear the team paraphernalia as much as possible. In addition, encourage loyal fans to be advocates for the team and positively reward them when they do by reposting fan posts on the team social media accounts. Management needs to provide at least some inexpensive merchandise for purchasing, because a large percentage don’t have any. It would make sense to get sponsors to provide free t-shirts and/or caps or other clothing that people will wear. Copyright 2018 Galen Trail

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Self-esteem Behaviors & Behavioral Intentions Summary Self-esteem responses to sport consumption such as BIRGing and CORFing are one part of the entire post-consumption process. As we noted above, an individual will consume the sport product. This consumption will either confirm or disconfirm prior expectancies that the individual had before consuming the product. If the expectancies are either positively confirmed or positively disconfirmed,

Figure 12.8

then the individual is happy and satisfied that he or she consumed the product. This positive affective state typically leads to BIRGing behavior as a way to promote the association with a successful other and bask in the reflected glow. BIRGing behavior typically leads to intentions to consume the product again. On the other hand, after the individual consumes the product, if expectancies are negatively confirmed or disconfirmed, the individual is typically unhappy and dissatisfied. This negative affect typically leads to CORFing or Blasting by the individual in an attempt to maintain levels of self-esteem. CORFing behavior typically leads to decreased intentions to consume the product again (Figure 12.8). Based on these relationships, it is imperative that sport marketers and sport managers know their customers/clients and continue to build relationships with them as much as possible. However, it is not sufficient to only focus on one aspect within the post-consumption process. There is a sequential relationship among all of the things (variables) that we have discussed, and all of them are important. If sport marketers and sport managers can determine these relationships within their own customers, and possibly segment them by whether they are likely to BIRG or CORF, then they can create plans to address the possible self-esteem issues that are inherent in the post-consumption of sport products. Thus, sport marketers need to make sure they understand the relationships among all of the variables in the Revised Structural Model of Sport Consumer Behavior (Figure 12.9). They


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Self-esteem Behaviors & Behavioral Intentions Figure 12.9


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Self-esteem Behaviors & Behavioral Intentions need to understand how the external environment (culture, context, constraints) socializes people into becoming fans and how it impacts the internal organizational environment. They also need to understand how the external environment impacts the customer environment beyond the socialization aspects (e.g., how it can move people along the consumer pathway; Figure 12.9).

Figure 12.9

For example, in the men’s professional soccer data collected from people in their database (Gallo, Yu, & Sharma, 2017), we found that 100% were aware of the


Figure 12.10

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Self-esteem Behaviors & Behavioral Intentions team, not surprisingly. We also found that there was a high degree of interest in the team via social media and the website. Active consideration was also very high because this group of people not only were in the database, but also were willing to fill out the survey. A large number (98%) already had made their purchase of tickets, and 97% attended games. Furthermore, there was a high degree of repatronage and just over 50% said that they “live and die” with the team. Obviously, this is not a representative sample of attendees, but it is a sample of very loyal fans. In general, I have been discussing attendees or fans as one whole group and not discussing how they may differ across people, with the exception of discussing differences during the demographics chapter. Determining which people are similar to each other and which are different, is called segmentation, and that is what the next chapter is going to be about.


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Chapter 13 Market Segmentation Why is market segmentation a topic in a sport consumer behavior book? The title of the chapter may lead some to think they are reading an excerpt from a sport marketing textbook rather than a book dealing with sport consumer behavior. It is important to recognize though, that any study of consumers should include information about segmentation or market segmentation. In the following sections we explain the broad view of segmentation, the concept of market segmentation, and the importance of understanding market segmentation in relation to sport consumer behavior. What is segmentation and market segmentation? Segmentation is simply the process of dividing some object into segments or parts. For example, when a deep-dish apple pie is cut into four slices (you cut your pie into eight slices, I prefer four), the pie has been segmented. Okay, back to sports. When you go to a basketball arena you are likely to find lower level, mid level, and top level seating (the nose bleed section). The seating area has been segmented. Within a particular level, say the lower level, you may find further segmentation. For example, there may be floor seats, club seats, and box seats. If you follow the sport of baseball in the United States, you should recognize that professional baseball is segmented into two leagues, major and minor. Within the major league there are two additional segments, the National and American leagues. The minor leagues are segmented into AAA, AA, A, and Rookie leagues (among others). Segmentation is all around us and an integral part of sports. Market segmentation is literally dividing a market into different groups and subgroups. In a broad sense the term “market” refers to all possible consumers, so when we use the phrase “market segmentation” we are not just considering the topic of marketing. We are dealing with consumers and seeking to clarify which consumers, or segments of consumers, an organization will focus on. Decisions about segmenting the market of all consumers into viable groups will impact marketing and other organizational decisions, which influence sport consumer behavior. Later in this chapter we will deal more with the topic of what criteria might be used to segment a market. For example, we will explain the idea of identifying segments based on consumer needs or benefits desired from a product. At this point though, we need to consider in more detail the purpose of segmentation.


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The purpose of market segmentation is to divide a larger market into smaller segments that have similar needs, wants, beliefs, motives, or desires specific to the focal product. For us, one example of a focal product would be a sports team. Dividing a market allows for development of a marketing or communications plan to fit the needs of the smaller market segment better than blanketing the entire market with the same plan that typically does not meet the needs of many individuals. For example, a marketing plan designed to increase attendance at a university’s men’s basketball games should be different for students attending the university than the marketing plan focused on the general public. Students are likely to be motivated to attend for different reasons than the people that live in the community who did not actually attend the university or have some direct connection to the university (see the chapter on socialization). Market segmentation is important because it allows an organization (including sport organizations) to develop profiles or personas of consumers. Developing such personas includes identifying characteristics of the consumers who will most likely have an interest in the specific sport product. The information is then used to design a marketing strategy. Information obtained from a market segmentation study can be used to define the market of an existing product, provide information for introducing a new product, or help find new opportunities in a market. Included in developing a marketing strategy is making decisions about the marketing mix such as pricing, distribution, and promotional methods, which can be refined to match the profiles of the specific market segment(s). Each market segment, however, has to be large enough to make it profitable to be served. Although each individual may have unique needs or motives, it typically is not profitable to market to each individual separately. The exception is if the individual is willing to pay to be served individually. For example, if an athletic department has a donor willing to make a sizeable donation to the athletic department (say $5,000,000), then that donor should get individualized attention along with many other perks. As the value of the customer goes up, the individualized attention should go up as well. Relationship marketing is based on this premise. This brings us to an important question; what approaches can be used to segment a market? Two approaches to market segmentation Market segmentation historically has followed one of two approaches. The first approach, mass marketing, is typified by mass production manufacturing. Supposedly, Henry Ford said that consumers of his Model T Ford could have any color they wanted, as long as it was black. Ford’s early production and sales were based on a mass marketing approach. The same product was made available to all people. By producing the same car in large numbers, Ford was able to keep the cost to consumers “reasonable.” Another way to think of mass marketing is “one size fits all.” The goal with mass marketing is to sell your product to as many consumers as possible. The focus is on the product rather than the consumer; in this sense mass marketing is not really segmenting with respect to focusing on different consumer groups. With mass marketing, there is one BIG group, anyone capable of purchasing the focal product. Another phrase for mass marketing is undifferentiated marketing, which refers the same product for all consumers. In other words, you would not differentiate your product from one consumer (or group of consumers) to another.


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A sport product that some might think of as being mass marketed is the Olympic Games. To some degree there is one product, the Games, which is made available for consumption (viewing) by all consumers. When we look more closely at the broadcast of the games, for example, we find there is a different product for different groups. One example would be the different foci of broadcasts in respective countries. In Japan, the broadcasts will include more coverage of Japanese athletes in events that are more popular in Japan. In the United States, the broadcasts will have more coverage of American athletes in events that are popular in the U.S. The broadcasts are literally developed for different audiences, suggesting not a mass marketed approach but a differentiated or targeted marketing approach. The second general approach to market segmentation is target marketing. Staying with the automobile example a bit longer, consider the history of General Motors. Albert Sloan, a name you might not be as familiar with compared to Henry Ford, recognized that not all consumers wanted a black, Model T Ford. General Motors grew using a targeted approach to automobile manufacturing and sales. GM was one of the first automobile manufacturers to recognize the importance of offering a car for every pocketbook and every personality. For example, when people think of luxury cars, Cadillac is likely a car that comes to mind. Chevrolet is thought of more as a Is it a target market or a niche market? middle class, affordable car. Pontiac You are likely to come across the phrase, niche historically has been produced to market, and wonder how that is different than a target appeal to the sports car crowd. market. A niche for all intents and purposes is a Some might argue that a specialized market segment. Is that different than a product such as an automobile is still target market? Not really. Those that really want to mass marketed because while cars are split hairs and suggest that a niche market is different produced in different colors and with from a target market explain that a niche is more different features (two-door vs. fourspecialized and smaller in size. That explanation does door, automatic vs. standard) they are not clarify for these authors how a niche is different still the same product. If you accept this than a target. For our purposes, the phrases target argument, then an automobile is a market and niche market may be used mass marketed product. The counterinterchangeably. argument to that position is that not only do automobiles have different features, but also have different styles and types. One simple example is cars versus trucks; both are technically automobiles, but they are substantially different. As noted in the preceding paragraph, there are sports cars, luxury cars, sedans, economy cars, etc. The differences are more than just featurebased; there are significant product differences that support the position that automobiles are target marketed. The manufacturing process may be mass production, but the product is marketed based on target marketing. It is important to recognize, however, that target marketing includes more than selling different products to different segments of consumers. Target marketing can also include selling the same product to different segments of consumers. Sports organizations learned long ago that one ticket option is not sufficient; if a Major League Baseball team only sold season tickets, they would have a lot of empty seats at every game. History shows that there are not enough consumers interested in season tickets to fill up a ballpark every single game. Some people enjoy going to multiple


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games, but not every game. For those folks some type of partial season ticket (or mini) plan is most appropriate. Still others are only interested in attending a single game. For them, single game ticket sales are most appropriate. An important point from the preceding illustration is that the benefits sought, needs associated with, or desires relative to consuming a sport product such as a baseball game are different across various consumer segments. The basic product, a ticket to a baseball game is essentially the same, but marketers must develop different marketing strategies for different target groups. Consumers have unique needs and different attitudes, interests, and opinions. Not all sport consumers are the same; consequently, a targeted approach to market segmentation is essential. Which leads to another important question, “How do we segment a market?” Criteria for market segmentation To compete successfully in today’s sport marketplace, mass marketing is increasingly an undesirable option for most sport organizations. An exception to the preceding statement may be the introduction of a new sports product. For example, when a new sports team emerges or a team moves from one city to another. In the introductory phase of the product (borrowing from the product lifecycle terminology), a mass marketing approach may be used in order to introduce as many people as possible to the sports team. Beyond that introductory phase, however, which is usually short in duration, target marketing will likely by the segmentation approach of choice. Target marketing Segmentation Criteria based on the unique needs, desires, interests, and Actionability Identifiability opinions of consumers is the best approach for sport organizations to take. A challenge for sport Responsiveness Substantiality managers is deciding what criteria should be considered when selecting a target market. Wedel and Kamakura (2000) suggested Stability Accessibility six criteria: identifiability, substantiality, accessibility, stability, responsiveness, and actionability. • Identifiability is the extent to which marketers can identify consumers in each segment by using the segmentation bases. • Substantiality requires that market segments are large enough to meet organizational performance goals. • Accessibility is the extent to which an organization can reach the targeted segments. • Stability. Market segments must also be stable so that they can effectively be targeted over time. • Responsiveness requires that segments be unique enough to respond to differentiated marketing mixes. • Actionability. Finally, to satisfy the actionability requirement, the identification of each segment must provide enough information for managers to take effective action with their targeted marketing campaigns.


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Thinking about the six criteria, it seems to us there are really four key criteria, identifiability, substantiality, accessibility, and responsiveness. The notion of stability over time is arguably a function of substantiality. While not the same ideas, a large enough segment must exist over time (stability) in order to be viable. Perhaps we could think of the criteria as substanable, a combination of sustainable and substantial, (or maybe not ☺). In terms of actionability, the notion of having enough information to take action is very similar to identifiability, which is identifying segments based on information from one or more of the bases of segmentation. The other aspect of actionability, taking action, is really the process of segmenting, not a separate criterion. The first criteria noted, identifiability, is of particular importance at this juncture. We begin segmenting a market by evaluating information about consumers. The information we evaluate constitutes what is often referred to as the bases of segmentation. Bases of market segmentation How many bases of market segmentation are there? It depends on who you ask. In most marketing and sport marketing textbooks you will read about four primary bases for segmentation: demographics, psychographics, geographic, and behavioral. As illustrated in Figure 13.1, there are different elements comprising each basis for segmentation. The different elements provide the specific information from which our consumer profile or picture is developed. In the following paragraphs I discuss the different bases of segmentation. It is important to recognize that while we talk about each

Figure 13.1


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basis separately, market segmentation may be done utilizing more than one basis. In other words, the bases are not mutually exclusive. In fact, segmenting a market using demographic, psychographic, and behavioral information provides a more detailed or a more complete profile of a consumer group. Also take note the examples provided for the various bases and specific elements do not represent an exhaustive discussion. The content is provided to explain the various bases and to give examples of the various elements and how they may be used for market segmentation. Demographic characteristics are some of the most widely used variables for market segmentation. There are two simple reasons for the popularity of demographic characteristics. First, the characteristics are easier to identify and measure. Second, information about demographic characteristics is more or less readily available. Often you can find demographic information in secondary sources. You may have heard or come across the phrase demographic characteristics in the past, but not actually thought about what it is the phrase refers to. Demographic characteristics are simply characteristics of human populations such as size, distribution, and vital statistics. The latter are usually most relevant to market segmentation. If you think about forms or questionnaires you have filled out in the past, you really do know what vital statistics are. For example, whenever you answer questions or provide information about age, gender, ethnicity, marital status, income, education level, occupation, etc., you are providing vital statistics. Each of the various statistics can and have been used in market segmentation. Again, remember that multiple variables may be used; the more information we have about consumers, the better we understand who they are, but typically demographic variables do not explain what influences people to purchase our sport products nor do they predict future purchasing behavior.


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Geographic segmentation is helpful in making distinctions about where consumer groups are located. From one perspective that might mean segmenting markets based on local, regional, national, and/or international parameters. Recognizing the breadth of appeal that a particular sport product has is one gauge of where our attention should focus regarding geographic boundaries. A minor league baseball team, for example, is likely most interested in local consumers and to a lesser extent, prospective regional consumers. Think about the task of trying to foster attendance at a minor league baseball game. Jeff James did research in this setting and found that over 80% of the people attending a minor league baseball game drive less than 60 minutes to reach the ballpark. With a few simple mathematical calculations, you can figure out based on distance and speed of travel how far away the majority of people attending live. A simple way to get this type of information for segmenting purposes is to find out the zip code area in which folks live. With this simple bit of information, you have one piece of developing a marketing strategy. You now have some idea of the proximity around your ballpark where the majority of your promotional activities may be focused. The accompanying table illustrates some other drive time figures we have found from research with different sports. The constraints associated with geography could be included in here as well. For example, traffic issues due to the geography on the way to the stadium. As I mentioned before, due to the large bodies of water and the mountains around Seattle, traffic is very different than in places in the midwestern United States without those characteristics. However, it typically doesn’t explain purchasing behavior or why people purchase, but you might find why people are constrained from purchasing (too far away). Psychographic information, also referred to as “state-of-mind,” includes information such as needs, values, attitudes, personality traits, lifestyle preferences, opinions, and interests. A common maxim found in marketing is that demographic variables help us describe who buys products, while psychographic variables help us understand why they buy products. This type of information may help us appreciate why consumers may be interested in specific products or product attributes. For example, certain needs may align with certain sport products. People with a need for risk-taking may be interested in watching and/or participating in some of the action or extreme sports. Someone who has a need for social interaction might seek out crowd or group experiences like attending sporting events because that fits with his or her needs. Preferences can also be important. For example, some people may enjoy indoor or outdoor activities more than others. Another group may desire to spend time with family; most professional sports teams promote some type of family night for many reasons, one of which is likely satisfying this lifestyle preference. Jeff James did some research on tailgating at college sporting events (a tough job


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but someone had to do it) and found that spending time with friends was the reason cited most often as to why people continue tailgating. Much of the work that has been done on sport consumer motivations be thought of as psychographic information. Psychographic behavior can explain some of the ‘why’ behind purchasing as was discussed in the needs and values chapter, but psychographics don’t do a great job in predicting consumption behavior. Behavior as a basis of market segmentation can include various pieces of information. As illustrated in the bases of segmentation figure (Figure 13.1), elements such as usage rate and intentions, among others, may be used to segment a market. Technically intentions are not yet behaviors, but they can be included in this basis for now. Usage rate within sport can be the number of games attended, the amount of time spent on social media related to the team, number of games watched on TV, the dollar amount spent on merchandise, etc. Usage rate can also be measured by the type of ticket packages; season tickets, partial season tickets, and single game tickets that were mentioned above. The different packages are intended in part to match with different consumer behaviors. Past behavior predicts future behavior the best usually, as long as it is the same type of behavior (i.e., past attendance behavior predicts future attendance behavior very well), but it doesn’t help you understand why the person attended. Brand Associations as a basis for market segmentations can include the product attributes, product benefits, and constraints associated with the organizational environment. Benefit segmentation, for example, focuses on what benefit(s) a product offers or perhaps what problem(s) a product may solve, and seeks to reach consumers interested in the particular benefit(s). Similarly, product attributes can be very important. For example, when you buy athletic shoes, what about the shoes is important to you? Are you looking for a fashionable shoe? Comfort? Performance? Fit? A combination of the preceding and other attributes? For those of you who are golfers, think about buying golf balls. What attributes do you look for in a golf ball? Distance? Accuracy? Durability? (Too bad my golf swing has as much or more impact on those elements and it is not just up to the golf ball.) Brand associations explain the ‘why’ behind the behavior fairly well, usually the best of all of the bases. They also predict future behavior better than most of the other bases, except for the Behavioral Basis. As you think about the different bases of segmentation (presuming you ever do ☺), an important point I want to emphasize, again, is that a strength of segmentation comes not from any one of the bases of segmentation, but from utilizing multiple bases of segmentation to develop more complete profiles or pictures of our sport consumers. For example, we could collect brand association information and past behavior information. If we add to that, information about geographic location, information about psychographic motives for attending a sporting event, and demographics, we have a lot of information from which we can develop a marketing plan to most effectively reach and ideally influence our consumers. In the preceding section I wrote about the criteria for market segmentation; in this section I have given you some ideas about the four traditional bases for segmentation. At this point it is important to think about the criteria and the bases for segmentation together. Wedel and Kamakura (2000) provided an evaluation of the different segmentation bases in relation to the criteria for market segmentation. The work by Wedel and Kamakura considers whether a segmentation variable is general, product specific, observable, or unobservable. The traditional elements we have discussed are aligned within these four categories. At the same time, the categories may be matched to provide a more in depth profile or picture of consumers. The segmentation bases using this classification would be observable-general bases, observable-product specific bases, unobservable-general bases, and unobservable-product specific bases. The effectiveness of the different bases may be further evaluated


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in relation to the different criteria for segmentation. Voorhees (2006) provided a modified version of Wedel and Kamakura’s work which blends the segmentation criteria and the bases of segmentation. I have modified Voorhees work further to reflect what we have discussed in this book and it is shown in Figure 13.2.

Figure 13.2

Observable, general bases such as demographics, geographic variables, and media usage variables have been some of the most widely used in early segmentation research. They have been popular because, as we previously explained, they are easy to collect and tend to be stable. However, some studies suggest that such segmentation bases are too general and are not distinct enough to allow marketers to identify unique market segments (Frank, Massy, & Wind, 1972; Wedel & Kamakura, 2000). Additionally, observable, general bases perform well on most of the segmentation criteria, but they fail to identify actionable or responsive segments. So, using these bases exclusively for segmentation is generally not practical for marketing managers (Wedel & Kamakura, 2000). Another perspective for segmenting markets is using observable, product-specific bases that tend to be related to buying behavior. This involves using variables such as usage frequency and usage situations to identify segments. In terms of effectiveness, these bases perform well on most of the segmentation criteria and better on Actionability and Responsiveness. Similarly, unobservable, general bases such as personality traits and personal values only moderately meet the segmentation criteria. Despite their shortcomings on a number of criteria, such as Actionability and Responsiveness, unobservable, general bases are still commonly used because they


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provide a more lifelike picture of consumer motivations. Some researchers have been particularly fond of variables such as consumer values and value systems to segment consumer markets (e.g., Kahle, Beatty, & Homer, 1986). Studies using these bases tend to conclude they are best used to predict general consumer behaviors and are not very effective in determining actual behavior toward a specific brand as we found out in the chapter on needs, values, and goals. Markets may also be segmented using unobservable, product specific bases that include variables such as product-benefit perceptions, product-attributes, brand attitudes, and behavioral intentions. While these bases do provide excellent information to segment markets, the importance that consumers attach to these perceptions is also critical (Fishbein & Ajzen, 1975). Unobservable, product specific bases seem to be well suited for conducting benefit and attribute segmentation. A recommendation that seems clear from the information is that an optimal segmentation strategy has to include numerous bases in order to satisfy all segmentation criteria. To this point I have presented information focusing on the basics of market segmentation including: what is market segmentation, the approaches to segmentation, and the criteria and bases for segmentation. In the next section I present several frameworks of sport consumer segments. Sport Consumer Segments Not all people who consume sport are alike. Some people are much more invested in the whole process. You probably know some of them. These people might never miss a game, paint their bodies, wear distinctive apparel, (e.g., dog masks), and live and die with the success or failure of their team. There are multiple ways that people can be segmented as noted above. In the first example (below), I have categorized all people into four categories based on level of brand attitude (Unobservable, Product-Specific Basis): Non-fans, Interested Individuals, Attached Fans, and Loyal Fans (Figure 13.3). A key point here is not just identifying four categories, rather it is understanding that there is a continuum

Figure 13.3


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that ranges from people who are totally unaware of the sport product and therefore do not consume, to those rabid fans that we just described who are living and dying with their team. It is a lot easier for our purposes in discussing sport consumer behavior to identify and label several categories, as I have, rather than use a continuum. A large percentage of the population is in the Non-fan category. For example, think about the New York Yankees (even you Red Sox fans), there are some people out there that are unaware of who or what the Yankees are. (My mom is an example. She may be the only one, unless there are a couple others in Tibet.) In addition to those people that are unaware of the Yankees, there are those who are aware, but are not interested. Perhaps they do not like baseball or sports, maybe they are into ballet and opera, but for whatever reason, they just are not interested. Another group of people that can be categorized as Non-fans of the Yankees are people who may be knowledgeable about baseball and the Yankees, but do not like the Yankees, for example the Boston Red Sox nation. Another group of Nonfans could be people who buy tickets for business entertainment purposes, but have no interest in the team. All of these people, those that are unaware, those that are aware but not but interested, and those who may consume the product once in a while, but have no connection to the team, are considered Non-fans for our purposes. With respect to the evaluation of segmentation bases, this group is segmented primarily through observable, general bases. Since there is no connection to the team (or very little), product-specific bases would yield little information. While we could assess some of the unobservable bases, knowledge about traits, values, and/or lifestyle preferences would again be of limited use since there is no connection to the team. By capturing demographic information and perhaps geographic information, we have identified a segment that is likely substantial, accessible and stable. Unfortunately, they will likely be unresponsive to efforts to spur consumption. The next largest percentage include the people in the category we have labeled Interested Individuals. These people have some type of interest in the team and may have a positive attitude about the team, but are not really committed to the team. As Funk and James (2006) noted, these people may be interested in the team for social or hedonic motives, or maybe they attend a game now and then because of situational factors. For example, they might attend a game because their friends or family are going and they want to interact socially with the other people. Or maybe they wanted to get away from the office and needed some entertainment. They chose a particular game because they like the sport and wanted to go to a game rather than go to another entertainment alternative such as a movie. Another possibility is that there was a 2-for-1 deal and the team was giving away thunder sticks. Regardless, in all cases, there is some interest in going to the game and thus consuming the product, however there is little, if any, personal connection to the particular team. The Interested Individuals are segmented using a mixture of the bases. Since there is still little to no personal connection to a specific team, use of general rather than product specific bases would be most likely. This would include demographic and geographic information. With this group, observable and unobservable bases may be helpful. Information about traits and lifestyle preferences, attending games with family members or going to games in order to spend time with friends, are examples of information associated with the unobservable bases of segmentation. The next category consists of those people that we have labeled Attached Fans. These people are psychologically committed to the team. When the team wins they may say something like, “When we won yesterday and beat the Yankees, boy did that make me feel good.” These people consider themselves fans and typically have intentions of consuming the team product whether it is watching the games on TV, buying some team merchandise, or attending a game or two. The final category is comprised of those people we have labeled Loyal Fans or those that Funk and James (2006) refer to as Allegiant Fans. These people differ from the Attached Fans by degree of their repatronage (i.e., the consistency of their consumption) and by their persistence of consumption, regardless of the situation. Loyal fans do not jump off the bandwagon if their team starts to lose. Loyal


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fans attend the game in all sorts of weather; in the snow in April at Cleveland Indian games, or in the rain in November at Seattle Seahawk games (actually maybe that is in the rain in September, October, November, and December in Seattle). These people are resistant to overtures to transfer their allegiance to another team. These characteristics distinguish them from those fans that are only “attached” to the team. The segmentation of the latter two groups is likely based on observable, unobservable, and product specific bases. Information about product usage, frequency of attendance for example, attitude towards the team, behavioral intentions, values and motives are important pieces of information that may be used to segment the market, and from which effective marketing strategies may be developed. Why people consume sport products differs considerably in some areas based on their level of “fandom.” In general, people at $250.00 the Loyal Fan end of the $200.00 continuum typically consume $150.00 more products and spend more money doing it than people at $100.00 the Non-fan end of the $50.00 $ Spent on spectrum. In an analysis we did $Merchandise per year on a WNBA team (Figure 13.4), Loyal Fans spent, on average, $230 per person per season on team merchandise, Attached Fans spent about $90 per Figure 13.4 season, Interested Individuals spent $40, and Non-fans spent only $12 per season, and most of that was for someone else. Loyal Fans, on average, attended 13 of the 17 home games, Attached Fans attended 10, Interested Individuals attended 6 and Non-fans attended 3. In an analysis of college women’s basketball, we found that Loyal Fans spent about twice as much as Attached Fans on merchandise and clothing over the season, 26 times the amount that Interested Individuals spent, and more than 75 times the amount that Non-fans spent. Loyal Fans also attended 1.5 times as many games as Attached Fans, 5 times as many games as Interested Individuals and 12 times as many games as Non-fans. Loyal Fans also watched televised games of the team 1.5 times more than Attached Fans, 2.5 times 6.00 more than Interested 5.00 4.00 Individuals and about 5 times 3.00 more than Non-fans. 2.00 1.00 In an analysis of NCAA # of Games Attended football games (Figure 13.5), we found similar results based on past attendance. Loyal Fans went to 5.5 games on average out of the 6 home Figure 13.5 games that year. Attached Fans went to about 4 games,

$ Spent on Merchandise per Year

# of Games Attended


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Interested Individuals went to about 3 games, and Non-fans went to approximately 1 game on average. In an analysis of two Major League Baseball teams, we also determined that similar trends were apparent on peoples’ intentions to attend games during the next season (Figure 13.6) and peoples’ team website consumption (number of hours per week; Figure 13.7). All of these Figure 13.6 results cumulatively illustrate several important points. First, even though some people indicate that they are not fans, they still consume the product sometimes, assuming they are aware of it. Second, in all cases, as the level of the connection to the team grows, (i.e., the more people consider themselves fans), the level of consumption of the product increases. Third, Loyal Fans consume products and attend games more than anyone else because of their consistency and perseverance in their connection to the team. Thus, when evaluating aspects that predict sport consumer behavior, the variable that explains sport 5 consumption the best is 4.5 the degree to which an 4 3.5 individual is a loyal fan of 3 a team. Because we are 2.5 2 able to reach the 1.5 conclusion that fan 1 Internet Consumption 0.5 loyalty predicts fan 0 behavior, we can better understand our sport consumers and through market segmentation based on the various Figure 13.7 bases and criteria of segmentation, market to them more effectively. Thus, the proposed framework of sport consumer segments gives us the opportunity to differentiate behaviors, particularly the frequency of consumption. However, although this is one way of segmenting consumers and potential consumers, it doesn’t really help you understand why they are motivated to come, other than by level of brand attitude. Therefore, a marketer would have a hard time marketing to, or communicating with, each segment because they wouldn’t know the motivations behind consumption of the product.

Team Website Consumption


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In this next segmentation example, I go back to the information we discussed about Oliver’s Loyalty Framework (Figure 13.8) in Chapter 10. If you will recall, he proposed four cells and named them Product Superiority, Village Envelopment, Determined Selfisolation, and Immersed Self-identity. These four groups were segmented by level of community/ social support and level of individual fortitude. This framework would help a marketer a little bit more than the Figure 13.8 first one because they would know that those in the Village Envelopment and Immersed Self-identity cells are motivated by being part of a fan community and the social support that they get from the group of fans. The marketer would also know that the other two groups are not motivated by social aspects. The marketer understands that the Product Superiority segment is motivated by a quality product, and that is about it. The Determined Self-isolationists are also motivated by a quality product, but unlike the Product Superiority group, won’t CORF if the team starts losing. As we saw in Chapter 10, these groups vary on some of the consumption behaviors (usage rates from Observable, ProductSpecific Basis), but other than those who are in the Immersed Self-identity group, the behaviors didn’t vary that much. This framework helps the marketer a little bit more, but it is still not sufficiently detailed such that the marketer could create quality communications that would motivate people to consume more. In this third example, we are going back Figure 13.9 to the professional women’s soccer data (Nicefaro & Goobes, 2017). Using a cluster analysis on product attributes (Team Success, Aggressive Play, Dramatic Play, Athlete Skill, and Players as Role Model), and product benefits (Social Interaction Opportunities, Information Provision, Diversion, and Supporting a Cause), they determined that there were six different segments: Women’s Soccer Supporters, Casual Fans, Loyals, Family-Focused, Bandwagoners, and Socials. Not only did these segments differ by the number of games that they intended to attend (Figure 13.9), but also by Product Attributes (Figure 13.10) and Product Benefits (Figure 13.11). Socials, surprisingly attended the most, closely followed by Loyals though. Bandwagoners intended to attend the least, quite likely because they


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anticipated that the team would not be very good the upcoming season after missing the playoffs during the prior season. Casual Fans actually intended to attend more games than Bandwagoners.

Figure 13.10

Not only did the segments differ within the segments on product attributes, but they varied across the segments on the different attributes. For example, Casual Fans did not perceive the players to be role models, but the Loyals and Socials sure did, as did the Women’s Soccer Supporters and the Family Focused segment. On the other hand, the Casual Fans perceived that the team played aggressively, much more so than Women’s Soccer Supporters and Bandwagoners, but not quite as much as the other three segments. Not surprisingly, the Loyals perceived that the team was successful to a much greater extent than did the Bandwagoners and Women’s Soccer Supporters. The perception of dramatic play was similar for three segments, but Bandwagoners scored much lower and the Loyals and Socials scored higher.

Figure 13.11


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Similar results were seen relative to the Product Benefits (Figure 13.11) in that the segments differed within themselves, but also, they differed across segments by benefit. For example, Information Provision differed dramatically across segment with Loyals perceiving it as substantial benefit, but Bandwagoners definitely did not perceive it as a benefit. The Family Focused segment, along with the Socials and Casual Fans were fairly neutral about Information Provision as a benefit. On the other hand, all segments thought that following the team was definitely supporting the cause of women’s sports. Most of the segments thought that the matches provided a good diversion from work or everyday life, except the Family Focused segment and the Bandwagoners. Socials, Loyals, and the Family Focused segment all thought that the game provided plenty of social interaction. The other three segments only thought that it provided a moderate amount of social interaction. The above information though only shows the perceptions that these segments had about product attributes and product benefits. It doesn’t show how these things (or anything else) predicted attendance intentions. As we have discussed before, mean scores show us how people view things (i.e., how important things are to them or how much they agree or disagree with certain concepts). However, mean scores do not predict behavior or behavioral intentions. To do that, you have to use relational statistics, such as regression, path analysis, or correlations (among others) to show the relationships between variables. Therefore, let’s look at the variables that predict the number of games that each segment intends to attend and see if those variables vary across the segments. If they do, then that is further affirmation that segmenting the data was appropriate. As you can see from Figure 13.12, again with the women’s professional soccer data and with the same six segments (Casual Fans, Women’s Soccer Supporters, Loyals, Family Focused, Bandwagoners, and Socials), the segments do differ on what predicts the number of games they intend to attend in


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2017. For Casual Fans, the more that they see that the game provides a diversion from their everyday life and the more they are satisfied with their previous matchday experiences, the more games they intend to attend in 2017. Thus, marketers for the team need to focus on communicating how coming to a match can really help relieve the stressors of everyday life. For the Casual Fans (Figure 13.13), the marketers determined that the diversion of attending the game helped fulfill the needs of social acceptance and companionship for this segment. So, creating a campaign specific to those things for Casual Fans would work well. In addition, the marketers needed to figure out what made this segment satisfied with matchday experiences. Looking at the data, the marketers determined that satisfaction with the stadium staff, the safety of venue, the ease of entering the stadium, and the cleanliness of the restrooms impacted matchday satisfaction the most. Marketers could then share these results with the management of the team, so that management could make sure all of those things were as good as possible, hopefully exceeding fan expectations. This is dramatically different from the Women’s Soccer Supporters’ segment (Figure 13.14) who were motivated to attend more games through their attachment to the community and to the coach. Thus, marketers could market the idea that supporting the team supports the community itself. The marketers could also create opportunities to interact with the coach so that they could become even more attached. Normally this would be a good idea, because increasing the attachment to the coach would increase the attachment to the team, which would also increase the number of games intending on going to. However, the coach at this time had been involved with the national team to some extent and was being considered for that head coaching position. She ended up leaving the team at the end of the 2017 season, but rather than going to coach the national team, she ended up coaching another team in the league, generating a lot of ill will from the fans. The Loyals segment was somewhat similar to the Women’s Soccer Supporters in that both were attached to the community in which the team played, but the Loyals were also motivated by the skills of


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the players. As they perceived those skills to increase, they were likely to attend more games. In addition, as depicted in Figure 13.15, Dramatic Play and Players as Role Models was associated with Athlete Skills. Thus, for this segment, team marketers need to promote, not only the skill of the players, but how dramatic their play is, and what good role models they are for kids. In addition, marketers need to emphasize how supporting the team supports the community. For the Family Focused segment, things were quite a bit different and less direct. The more this segment was motivated by promotions, the more likely they were to attend at some point in 2017. The more likely they were to attend at some point in 2017, the more games they intended on attending in 2017 (Figure 13.16). However, promotions did not directly predict the number of games they intended on attending. The promotions only indirectly predicted that this segment was likely to attend more than once. However, if we look at the indirect effects of promotions on number of games, it is very small [(.319 *.522)2= .1672 = 2.7% of the variance], reinforcing the idea that promotions might get people to one game but not more, at least without more promotions. As shown in Figure 13.16, this segment would be motivated by patriotic promotions. As patriotism values increased, the more likely this segment would be motivated by promotions. The Bandwagoners had already started jumping off the bandwagon when we started surveying these people. This was reinforced by the data. It showed that the more this segment CORFed (Figure 13.17), the less likely they were going to attend in 2017, and the less likely they were going to attend, the fewer games they were going to attend in 2017, not surprisingly. This is also reflected in the mean

scores (go back and look at Figure 13.9) that showed this segment intended on attending the fewest


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games of all segments and this segment was also the most likely to CORF. Obviously in this case, the team’s marketers need to determine how to prevent these people from CORFing. The data showed (Figure 13.17) that if the marketers could increase the attachment with the coach and increase supporting the cause of women’s sports, then CORFing would decrease. Unfortunately, as noted above, the coach resigned from the team at the end of 2017, so that marketing opportunity should not be implemented, nor should it be switched to the incoming coach. The latter should not be done because there would not be any attachment to the new coach. Thus, marketers need to focus on moving the people in this segment into the Women’s Soccer Supporter segment. They could do this by showing them how supporting this team, regardless if they were winning or losing, helped support women’s sports and provide role models for girls. In addition, this would fulfill this segment’s need for social acceptance (Figure 13.17). Finally, for the Socials segment, the more they were able to BIRG and the more they were attached to the community, the more games they were intending on attending in 2017 (Figure 13.18).

Thus, marketers need to provide as many opportunities to BIRG as possible for this group. The data shows that attachment to the team predicts BIRGing the best, so marketers should provide opportunities to interact with the team, provide paraphernalia that has team logos, and provide opportunities to socialize with other like-minded fans (Figure 13.18). The team’s marketers can also promote the connection to the community as they do with the other similarly-situated segments. As is apparent from all the above information, the team’s marketers can focus communication and marketing campaigns more narrowly and vary them for each segment because now they know how the segments perceive the different product attributes, benefits, constraints, etc. The cluster analysis and segmentation showed that the segments existed and how different the segments were based on the mean scores. In addition, looking at how different variables predicted attendance intentions across the segments, indicated that each segment did, in fact, need separate communication strategies, rather than mass marketing to everyone the same. Renton RoadRunners Example Back to our fictitious RoadRunners example and let’s see how their marketers segmented their attendees. Remember that the data below is real, it just happens to be from a different team (obviously), other than the made-up RoadRunners. The marketers ran a cluster analysis and determined that there were four segments: Neighbors, Good Timers, Bandwagoners, and Loyals.


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They found that the segments differed significantly (and meaningfully) on needs.

However, although there were significant differences on values, they were not meaningful.

Values MEANS

9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00

Freedom Values

Work Ethic Values

Competitiveness Values

Neighbors

7.44

7.35

5.79

Good Timers

5.99

6.08

4.84

Bandwagoners

8.09

8.13

7.23

Loyals

7.55

7.47

5.70

In general, there were minimal differences across the segments on constraints, with the exception of

Internal Constraints MEANS

5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Poor Player Behavior

Price (too high)

Lack of Knowledge

Travel Constraints

Lack of Interest (Family)

Lack of Interest (Friends)

Lack of Interest (Partner)

Neighbors

2.89

3.36

3.33

4.48

2.27

3.03

2.59

Good Timers

2.76

3.26

2.14

4.17

2.29

3.07

2.71

Bandwagoners

1.98

3.39

2.41

4.59

2.13

2.97

2.39

Loyals

2.17

2.90

1.56

4.30

1.92

2.75

2.34


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lack of knowledge. In addition, most were not constraints, except for travel to the venue. Perceptions of product attributes did not vary significantly.

Product Attributes 7.00 6.00

MEANS

5.00 4.00 3.00 2.00 1.00 0.00

Team Success

Aesthetic Play

Aggressive Play

Dramatic Play

Neighbors

4.62

3.91

3.63

5.45

Good Timers

4.58

4.04

3.75

5.35

Bandwagoners

5.40

5.02

4.20

6.13

Loyals

5.06

4.44

3.72

5.85

The non-product related aspects such as Impact of Ads and Social Media also did not vary significantly across the segments, nor did the segments vary on the Impact of Promotions.

Ads & Social Media MEANS

6.00 5.00 4.00 3.00 2.00 1.00 0.00

Newspaper ads

TV commercials

Radio ads

Twitter

Facebook

Pinterest

Mobile App

Neighbors

4.35

4.64

4.62

4.09

4.57

3.93

4.40

Good Timers

4.18

4.48

4.42

4.03

4.56

3.92

4.52

Bandwagoners

4.63

5.04

5.05

4.45

5.21

3.99

5.10

Loyals

4.43

4.86

4.87

4.40

4.97

4.06

4.98

Promotions MEANS

7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00

Special Promotions

Giveaways during the game

Intermission events

Pre-game events

Post-game events

Neighbors

5.79

5.29

4.76

4.56

4.45

Good Timers

5.58

5.19

4.52

4.57

4.50

Bandwagoners

6.12

5.81

5.29

5.17

5.10

Loyals

5.91

5.49

4.78

4.83

4.81


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The segments did not vary meaningfully by the Impact of Word of Mouth.

Word of Mouth 6.00 5.00

MEANS

4.00 3.00 2.00 1.00 0.00 Word of mouth from Friends

Word of mouth from Family

Word of mouth from Acquaintances

Neighbors

5.03

4.99

4.81

Good Timers

4.74

4.64

4.53

Bandwagoners

5.38

5.44

5.26

Loyals

5.13

5.07

4.89

The perceptions of Product Benefits did vary significantly, but not substantially.

Product Benefits MEANS

7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00

Social Interaction Opportunities

Diversion

Information Provision

Players as Role Models

Neighbors

5.14

5.24

4.59

4.75

Good Timers

4.85

5.17

4.58

4.78

Bandwagoners

5.86

6.12

5.49

5.72

Loyals

5.53

5.81

5.13

5.46

The perceptions of the Internal Organizational Constraints did vary for Poor Player Behavior, but not

Internal Organizational Constraints MEANS

4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

Poor Player Behavior

Price (too high)

Neighbors

2.89

3.36

Good Timers

2.76

3.26

Bandwagoners

1.98

3.39

Loyals

2.17

2.90


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Price, but neither appeared to be much of a constraint. The segments differed significantly and meaningfully on the number of years they had been a fan and the number of games they went to.

Fans Years and Attendance MEANS

18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Fan Years

# of Games Attended this year

# of Games Attended Last Year

Neighbors

8.17

7.04

7.21

Good Timers

11.49

9.85

12.11

Bandwagoners

11.99

13.06

13.88

Loyals

9.95

14.00

15.67

They also differed on the amount of money they spent on merchandise for themselves and others.

MEANS

Merchandise Purchasing Behaviors $120.00 $100.00 $80.00 $60.00 $40.00 $20.00 $-

$ spent on merch for self last season

$ spent on merch for others last season

$ spent on merch for self this season

$ spent on merch for others this season

Neighbors

$47.69

$55.53

$47.01

$41.59

Good Timers

$88.15

$72.55

$72.26

$47.76

Bandwagoners

$113.09

$98.69

$103.86

$89.68

Loyals

$104.92

$82.05

$101.06

$86.28

Finally, the segment did differ on their social media usage regarding the team.

Social Media MEANS

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

Avg. times/week access team Website:

Avg. times/week access team Facebook page:

Avg. times/week access team Twitter account:

Avg. times/week access team Youtube Channel:

Avg. times/week access team Pinterest page:

Neighbors

1.51

1.21

0.77

0.15

0.01

Good Timers

3.07

2.15

1.17

0.38

0.11

Bandwagoners

3.87

3.53

2.25

0.59

0.1

Loyals

3.6

3.37

1.98

0.74

0.15


Page 367

Overall, the RoadRunners marketers can see where the segments differed and where they were similar. I did not provide all of the different aspects that were in the data set, so this is not a complete picture of the RoadRunners segments. In addition, I didn’t provide the diagrams of what predicted attendance behavior for each of the segments, like I did with the women’s professional soccer data earlier in the chapter. I figured one example was enough in this chapter 😊. However, if you were doing this for your marketing department or for a client, then you would most assuredly provide such. By looking at the segments and their profiles, it helps marketers create different marketing campaigns by segment, which will hopefully be more successful than one campaign for everyone who might come to a game. Summary In sum, segmenting enables us to create profiles for each segment that distinguishes consumers based on multiple pieces of information including but not limited to psychographic, demographic, geographic, behavioral, and product-perception characteristics. The important point to remember at this juncture is the value of using multiple bases for segmentation to identify sport consumer segments and assessing the segments relative to the various segmentation criteria. In addition, as you might have noticed from the women’s profession soccer data and diagrams (Figures 13.12 - 13.18), even though each of the segments differed by the variables that had the greatest influence on future attendance, all of the relationships among the variables in segments supported the structural model and people’s progression along the consumer pathway. Hopefully this book has given you some insight into sport consumer behavior and all of the aspects that may influence it so that you are able to put at least some of these concepts to work as you move forward in the industry.


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