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Family Traveler Segmentation by Vacation Decision-Making Patterns Soo K. Kang, Cathy H.C. Hsu and Kara Wolfe Journal of Hospitality & Tourism Research 2003 27: 448 DOI: 10.1177/10963480030274005 The online version of this article can be found at: http://jht.sagepub.com/content/27/4/448

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ARTICLE

JOURNAL Kang et al.OF / FAMILY HOSPIT TRAVELER ALITY & TOURISM SEGMENT RESEARCH ATION

FAMILY TRAVELER SEGMENTATION BY VACATION DECISION-MAKING PATTERNS Soo K. Kang Colorado State University Cathy H.C. Hsu The Hong Kong Polytechnic University Kara Wolfe North Dakota State University The purposes of this study were to extend the scope of family vacation decision-making research by profiling various family vacation segments based on decision-making patterns, and to provide a systematic evaluation of the segments based on their profitability (i.e., expenditure per travel party and per person), accessibility (i.e., the degree to which a segment can be effectively contacted and served), and reachability (i.e., the extent to which a segment can be attracted by products/services offered). A total of 297 travelers, who visited one of the three Travel Information Centers (TIC) on the borders of Kansas and who considered themselves traveling as a family unit, participated in the study. Results of the study generated three market segments, including intergenerational (ITG) travelers, businessmixed-with-pleasure (BMP) travelers, and visiting friends and relatives (VFR) travelers. Based on the evaluation criteria, the VFR segment was identified as the most viable market for Kansas to pursue. KEYWORDS: family decision making; market segmentation; target market selection; VFR

Families are an important market segment in the travel and tourism industry. According to the Travel Industry Association of America (TIA, 2001), family vacation travel accounted for 65% of the United States’s domestic pleasure trips in 2000. The average expenditure for a family vacation was approximately $1,087 in 1999, and more than one half (58%) of the families spent the same amount as the previous year. Close to one third (31%) of family vacationers indicated that they planned to spend more on their future vacations (National Geographic Traveler, 2000).

Authors’Note: We wish to thank the Kansas Department of Commerce & Housing, Travel & Tourism Development Division for their funding of this research project. Journal of Hospitality & Tourism Research, Vol. 27, No. 4, November 2003, 448-469 © 2003 International Council on Hotel, Restaurant and Institutional Education

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Although the number of family vacations has increased, the types of family travel have become diversified as well. For instance, multigenerational travel (which includes grandparents) or “grand travel,” has grown from 13% of all trips taken with children in 1999 to 21% in 2000 (TIA, 2001). According to the 2001 Domestic Travel Market Report released by the TIA, more than one third (37%) of the 1,247 American adults surveyed indicated that they wanted to travel more with their parents. This trend is expected to continue as baby boomers enter their grandparenthood and retirement. The consequences will be significant because, as of 2000, boomers already accounted for close to one half (46%) of the total family travel market and spent more on their family vacations than any other age group, with an average of $1,200 per person per trip. As the importance of family travelers becomes obvious, the travel and tourism industry has developed products and services, including family suites, grand travel activities, and vacation packages for family reunions and weddings, to target this particular segment (Blum, 1996; Feder, 1996; Ira, 1991; Wong, Ap, & Li, 2001). Wong et al. (2001) investigated family travelers’ preferences in selecting lodging accommodations in Hong Kong. Results of the study indicated that family travelers preferred to stay at lodging facilities providing products and services specifically designed to cater to family vacationers, including discounts for children’s stay, a swimming pool with lifeguard, and discount packages for families. Gardyn (2001) reported that national hotel chains already introduced extra large rooms, equipped with more bedding options (including day beds and pullout sofas) to accommodate family vacationers. Although it is important to comprehend the market trend and to develop tourism products and services in response to the trend, effective tourism marketing will be maximized when industry practitioners understand not only what consumers want from their vacations but also how consumers make their travel decisions (Assael, 1998; Fodness, 1992; Jenkins, 1978). As an important decision-making and consumption unit of travel and tourism products and services, vacation decision making in families is a complex hybrid activity that requires individual involvement and joint decision making (Assael, 1998; H. L. Davis, 1976; Filiatrault & Ritchie, 1980; Fodness, 1992; Jenkins, 1978; Sharp & Mott, 1956). Not only do family members influence one another’s purchasing decisions but also they are frequently involved in making joint decisions. Therefore, identifying the relative influence of each member in a travel party will provide destination marketers and tourism professionals with a crucial cue in designing their promotional materials and determining appropriate distribution channels for their products. LITERATURE REVIEW Family Decision-Making Research

The family decision-making process is different from and more complex than an individual decision-making process (Assael, 1998; Michie & Sullivan, 1990) in that it involves joint decisions among family members (Assael, 1998). Family vacation decisions (which tend to involve multiple individuals and significant


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JOURNAL OF HOSPITALITY & TOURISM RESEARCH

resources) are referred to as high-involvement decisions (Michie & Sullivan, 1990) that function more as processes than events (Assael, 1998) and are governed by special rules (H. L. Davis, 1976). Therefore, understanding the family decision-making process will render vital marketing intelligence for practitioners in the travel and tourism industry. Due to the paucity of research on family decision-making process in the travel and tourism field, a comprehensive literature review on all types of purchase decision making in the realms of marketing, consumer behaviors, and family studies was conducted. Family decision-making studies were first published in the marketing literature in the early 1970s. Most of these studies focused on the examination of relative influences of husbands and wives on the purchase of various products (e.g., H. L. Davis, 1970, 1971, 1976; H. L. Davis & Rigaux, 1974; Feber & Lee, 1974). For example, H. L. Davis and Rigaux (1974) attempted to identify marital roles on 25 consumer purchases (including vacation) in three stages of the decision-making process: problem recognition, information search, and final decision making. Results of the study showed that marital roles could be differentiated based on various phases in the decision-making process and different consumption categories. The majority of family decision-making studies have operationally viewed family decision making as being one of three categories: husband-dominant, wife-dominant, or joint decision between husband and wife (Blood & Wolfe, 1960; Nichols & Snepenger, 1988; Sharp & Mott, 1956). Each member’s influence varied by the type of product (Corfman, 1987; Howard & Madrigal, 1990; Jenkins, 1978; Martinez & Polo, 1999), the decision-making stage (Bonfield, 1977; H. L. Davis & Rigaux, 1974; Nichols & Snepenger, 1988), and family/ household characteristics (Fodness, 1992; Howard & Madrigal, 1990; Jenkins, 1978; National Geographic Traveler, 2000). The pioneers in family decision making in travel and tourism were Myers and Moncrief (1978) who investigated decision making on the selection of a pleasure travel destination, the route of the trip, and a commercial lodging accommodation. Results of the study indicated that joint decision making was most frequently used among couples in the younger age categories. Wives in families with lower economic status had greater influence in making choices compared to other families. As for decision types, husbands exerted more influences on route decisions than wives, whereas the lodging decision was shared by both partners. A recurring result reported in a number of family decision-making studies in the travel and tourism literature indicated that joint decision has been the most dominant decision-making type in family vacation-related decisions (Filiatrault & Ritchie, 1980; Fodness, 1992; Jenkins, 1978; Nichols & Snepenger, 1988). This may be attributed to several factors: 1. Family vacations are likely to be taken by multiple members; therefore, every member’s opinions are incorporated into the decision-making process. 2. Because family travelers view family vacations as an important function of maintaining their family’s health, well-being, and lifestyle (National Geographic Traveler, 2000), an effort is made to ensure that every member’s preferences are considered as much as possible.



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3. As a result of the importance of family vacations, they are likely to involve more resources (Assael, 1998; Michie & Sullivan, 1990), thus generate more joint decisions than individual dominant decisions.

Jenkins (1978) explored family vacation decision-making behavior—with subdecision areas—by interviewing both spouses. In his study, the relative influence of husband, wife, and each child was measured on a constant-sum scale by asking each spouse to allocate 100 points among family members. In measuring the relative influence, the constant-sum scale was proven reliable by allowing more analyses possible, especially important when more complex decisionmaking units, such as a family, were involved (Filiatrault & Ritchie, 1980). Segmentation Research in Travel and Tourism

Using cluster analysis as a market segmentation technique has been widely documented in tourism studies and proven to be effective in generating promotional and marketing strategies by yielding viable market segments (Arimond & Elfessi, 2001; Frochot & Morrison, 2000; Mazanec, 1984; Punj & Stewart, 1983; Shoemaker, 1989; Singh, 1990). Groups derived from the cluster analysis were differentiated by various segmentation criteria, including demographic attributes (e.g., McCleary, Weaver, & Li, 1994; Weaver, McCleary, Lepisto, & Damonte, 1994), psychographic characteristics (e.g., Weaver et al., 1994), benefits sought (e.g., Calantone & Johar, 1984; Frochot & Morrison, 2000; Jang, Morrison, & O’Leary, 2002; Loker & Perdue, 1992; Moisey & McCool, 1990; Snepenger, 1987; Woodside & Jacobs, 1985), motivation (e.g., Lang & O’Leary, 1997), bedand-breakfast amenity preferences (e.g., Arimond & Elfessi, 2001), geographic origins (e.g., Moscardo, Pearce, & Morrison, 2001), travel purposes (e.g., Shoemaker, 1989), and traveler behaviors (e.g., D. Davis & Sternquist, 1987; Dimanche, Havitz, & Howard, 1993; Lang & O’Leary, 1997). Arimond and Elfessi (2001) explored the viability of categorical variables as traveler-segmenting criteria with tourists staying at a bed and breakfast (B&B) operation. In the study, a multiple correspondence analysis was used as a dimension reduction tool before conducting a cluster analysis. The cluster analysis generated four segments based on various B&B travelers’ trip-related variables, including travel purpose; preference for amenities, services, and activities provided by the B&B; and willingness to revisit the B&B. Instead of recommending a single target market among the four resultant segments, the authors provided marketing implications for each segment so that B&B operators could choose their own target market based on the amenities and services provided. Results of the study also supported the validity of using cluster analysis with categorical variables. Hair, Anderson, Tatham, and Black (1998) cautioned that using statistical analysis alone to determine exclusive segments is insufficient and may be misleading in drawing conclusions and interpreting clusters. Moscardo et al. (2001) pointed out that although segmenting customers by forming clusters is ubiquitous in tourism research, methods of determining resultant segments’ adequacy are still deemed incomprehensive. Therefore, as an attempt to validate the resultant


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clusters as viable and actionable segments, researchers have incorporated general market segmentation principles to evaluate the quality of market segments (e.g., Frochot & Morrison, 2000; Loker & Perdue, 1992; Moisey & McCool, 1990; Perdue, 1996). For example, in Moisey and McCool’s (1990) study on benefit segmentation of tourists, each segment’s economic power was evaluated by using a measure of average expenditure per segment. Wilkie (1994) proposed that three core requirements should be satisfied for a group to be declared a feasible market segment. The criteria were: a high segment identity, a segment’s ability to respond uniformly to a specific marketing mix, and product/service provider’s capability to create an efficient marketing mix for the segment. In addition, Perdue (1996) proposed a two-dimensional model for classifying alternative markets based on existing sales and incremental sales potential for the downhill-skiing industry in Colorado. Results of the study showed how Colorado destination marketing organizations could allocate their promotional efforts among alternative markets to achieve the goals of maintaining important existing markets and penetrating markets with high incremental sales potential. Loker and Perdue (1992) investigated vacation-benefits-sought segment and provided a systematic evaluation method to identify the most profitable market segment. Each segment was ranked by its performance on the profitability, accessibility, and reachability dimensions. The profitability for each cluster was measured by examining its overall expenditure volume and person-night performance. The accessibility for each cluster was evaluated by analyzing the type and number of travel-planning information sources used and geographic concentration of the market segment. The reachability dimension was assessed with information on travel purpose, activities participated in, and trip satisfaction. Results of the study generated six viable market segments, including naturalists, nondifferentiators, family/friend oriented travelers, excitement/escape travelers, pure excitement seekers, and escapists. The ranking procedures that used to compare segments in the study lacked preciseness in terms of providing the degree to which one segment outperforms the other. Nonetheless, Frochot and Morrison (2000) supported the notion that the viability of market segments derived from tourism research should at least be evaluated using the average visitor spending, and ideally be assessed by their accessibility and reachability as reported in Loker and Perdue’s (1992) study. Jang et al. (2002) employed a factor-cluster analysis to examine three benefitbased segments of Japanese outbound travelers to the United States and Canada. Four target market selection criteria—profitability, risk, risk-adjusted profitability index, and relative segment size—were applied to establish valid decision rules for selecting target markets. Profitability of each cluster was assessed based on its mean expenditure. Risk was estimated by the coefficient variance (which was defined as the standard deviation of expenditure divided by the mean expenditure) of each segment’s profitability. Risk-adjusted profitability index was defined as the segment’s mean expenditure divided by its standard deviation times 100. Relative segment size was the product of the mean expenditure times the probability of the occurrence of a specific market. The authors pointed out that few studies used a heuristic approach in selecting target markets. Therefore, this



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study endeavored to address this weakness by evaluating segments derived from cluster analysis and determining the most viable market based on multiple criteria. PURPOSES OF THE STUDY

Previous decision-making studies in travel and tourism failed to investigate the family decision-making process beyond the investigation of relative influence or the identification of decision-making types (e.g., husband-dominant, wifedominant, and joint decisions). Therefore, the purposes of this study were to segment family vacationers based on their decision-making patterns and to identify the most viable and actionable target market with a systematic evaluation of the segments based on their profitability, accessibility, and reachability. In this study, profitability was assessed based on a segment’s size and its expenditure on various activities. Accessibility referred to the degree to which the segment can be effectively contacted and served (Kotler, 1991; Morrison, 1996). Reachability was defined as the extent to which the segment can be attracted by the products and services offered (Morrison, 1996). Specific objectives of the study were • to identify prevalent family decision-making modes used in various stages of the decision-making process; • to segregate family travelers into mutually exclusive clusters by decision-making variables; • to portray each segment with unique characteristics in accordance with its decisionmaking patterns, travel behaviors, and demographics; and • to evaluate each segment emerged with regard to its profitability, accessibility, and reachability to identify its adequacy as a market segment. METHODOLOGY Research Instrument and Data Collection

A questionnaire was developed based on a review of literature and in consultation with the funding agency—Kansas Department of Commerce and Housing, Travel and Tourism Development Division. Travel characteristics and behaviors—consisting of primary trip purpose, average expenditure on various categories, the size and composition of travel party, information sources used in travel planning, the usefulness of information obtained from Travel Information Centers (TICs), and activities that respondents participated in while in the state— were obtained. Respondents who considered themselves as traveling in a family unit were asked to answer the questions on family decision making. A total of 10 questions on family decision making covered the need recognition (i.e., felt the need to take this trip), information collection (i.e., collected travel information), information evaluation (i.e., reviewed travel information collected), final decision, and actual purchasing stages (i.e., made travel arrangements) of the decisionmaking process as identified by numerous consumer behavior researchers (e.g.,


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Assael, 1998; Crompton, 1977; Mayo & Jarvis, 1981; Um & Crompton, 1991; Woodside & Lysonski, 1989). The final decision stage comprised six subdecisions, including the determination of general vacation destination (i.e., decided where to travel in general), particular location to visit (i.e., decided to visit a particular location), length of trip, travel budget, activities to participate in, and accommodation. Using the constant-sum scale developed by Jenkins (1978), respondents were to be asked to indicate each family member’s influence by allocating 100 available points to themselves, their spouse/partner, their children, and/or their parents for each of the 10 questions. Pilot test respondents (n = 20) indicated the difficulty of assigning 100 points among various family members. Therefore, their recommendation of using a 10-point scale was adopted. An example on how to answer this type of question was also provided, as suggested by pilot test participants. Nine demographic questions (including state of residence, age, marital status, employment status, presence of children under the age of 18, the age of the youngest child, gender, education, and income) also were included. Three TICs on the borders of Kansas were asked to distribute the questionnaires. The TIC staff was trained by the project leader for proper survey administration. Every 60th visitor who signed the guest book at each TIC from March to September 2001, was given a copy of the questionnaire along with a small souvenir of the state as a token of appreciation for participation. The names and addresses of visitors who agreed to participate in the study were obtained for follow-up purposes. The participants were instructed to complete the questionnaire on the completing their visit to the state and return the survey that had a postage-paid return address printed on the backside. A follow-up letter and a replacement questionnaire were sent to nonrespondents. A total of 1,480 questionnaires were distributed, and 553 (37.4%) were returned. Data Analysis

Descriptive statistics were calculated for all survey items. The decisionmaking mode for each of the 10 decision-making questions was categorized as individual dominant (i.e., self, partner, child, parent), couple joint, one partner with child(ren) joint, other joint, or family decisions. When a family member received more than 75% of the total points, that individual was defined as a dominant influencer in that decision-making stage. Although Jenkins’s study had three categories of possible influencers (i.e., husband, wife, and children), this study added one more category—parent(s). Therefore, a more conservative approach was taken to determine a dominant influencer by using 75%, instead of 50% as did in Jenkins’s study. When no single member was assigned more than 75% of the total points, the sums of all possible member pairs were calculated to determine whether any type of joint decision occurred. For example, when the sum of two partners’ points exceeded 75% of the total, it was defined as a couple joint decision. Other joint decisions included decisions made by one partner and his or her parent(s) and between his or her child(ren) and parent(s). When more than two members’points were required to reach the 75%, it was classified as a family decision. Chi-square analyses were conducted to detect any differences on decision-



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making type, which was transformed from a scale measure (i.e., constant-sum) to a nominal measure, for all 10 questions based on the gender of respondents. A combination of hierarchical and nonhierarchical cluster analyses was employed to segregate respondents into mutually exclusive groups based on their decision-making categories on the 10 family decision-making questions. Using both approaches was proven to be more reliable than using only one method in defining resultant clusters because these two techniques complement each other’s benefits (Hair et al., 1998; Milligan, 1980; Punj & Stewart, 1983; Singh, 1990). The sample was randomly divided into two groups. From the first sample, a hierarchical cluster analysis with a Ward’s (Ward, 1963) method was conducted to obtain an initial seed point and to establish the number of clusters by reviewing the dendrogram and cluster (agglomeration) coefficients. Subsequently, a nonhierarchical cluster analysis, or K-means, with the holdout sample was carried out to form groups based on respondents’ family decision-making patterns. Reliability of the cluster solution was accomplished by conducting a cross-validation analysis. The sample was divided in half, and a cluster analysis was run for each group. Descriptive statistics of the two sets of clusters were compared to determine the degree to which similar clusters have been identified (Punj & Stewart, 1983). As an attempt to evaluate each cluster’s adequacy as a market segment, each cluster was assessed based on the three criteria (i.e., profitability, accessibility, and reachability) suggested by various researchers (Jang et al., 2002; Kotler, 1991; Loker & Perdue, 1992; Morrison, 1996). Profitability was evaluated by calculating each cluster’s average travel party size, expenditure per travel party, expenditure per person, and expenditure on lodging, food, shopping, transportation, and other areas. Accessibility was assessed by investigating the demographic characteristics, type of travel information sources used, and geographic origins of respondents. Reachability in this study was measured by examining respondents’ primary travel purpose, the helpfulness of information obtained at TIC, and the average number of activities that respondents participated in while in the state. Unlike Loker and Perdue’s (1992) study that used subjective ranking in the evaluation of these three dimensions, this study employed chi-square analyses and a series of analysis of variance (ANOVA) to objectively assess the performance of each cluster on the three criteria to increase the validity of the evaluation results. RESULTS Demographic Profile

Of the 553 respondents, only 325 (58.8%) considered themselves traveling as a family unit, and 297 completed all family decision-making questions in the questionnaire. Therefore, 297 responses were used in further data analyses. More than half (56.2%) of the respondents were women (see Table 1) and reported $50,000 or more as the annual household income (57.5%). The majority of respondents (82.4%) were 45 years old or older, and currently married or in a long-term marriage-like relationship (89.6%). Approximately one half (49.8%)


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of the respondents were employed, and 46.9% reported that their spouse/partner was employed. Nearly one half of them (47.6%) attained a 4-year college degree or a graduate/professional degree, and only 16.9% had child(ren) younger than the age of 18 living at home. The average age of the youngest child under the age of 18 was 8, with a mode of 2. Family Decision-Making Pattern

Couple joint decision was the most prevalent for all 10 decisions (see Figure 1). Eight decisions were made jointly by couples in more than one half of the families, with determination of destination as the most commonly made couple joint decision, by 87.7% of the families surveyed. The other two decisions—information collection and travel arrangement—were the least jointly made by couples in 41.9% and 43.6% of the families, respectively. These results were consistent with findings from previous studies that vacation-related decisions were mostly made by both spouses (Filiatrault & Ritchie, 1980; Fodness, 1992; Jenkins, 1978; Nichols & Snepenger, 1988; Stafford, Ganesh, & Garland, 1996). Besides the couple joint decision, individual dominant decision by the respondents themselves or their partner appeared to be the second most popular mode of decision making. Women were the more predominant decision makers, compared to men, in the stage of information evaluation. Travel arrangement, or actual purchasing, was equally made by male and female respondents unless it was jointly carried out by spouses. Accommodation selection was the only area where women exerted slightly more influence than their counterparts among the six subdecisions. Of those who indicated either male-dominant or female-dominant as the decision pattern, responses were compared by the gender of respondents. According to chi-square results, male respondents indicated that they had exerted more influence than their partners; female respondents also reported that they had more influence than their partners. Significant discrepancies were found on 7 of the 10 decision areas (see Table 2). This may show that incongruence existed on perceptions between male and female respondents in evaluating their relative influence of vacation-related decisions. Little influence of children or the partnership between respondents and their child(ren) was observed in the study. This may be attributed by the small number (16.9%) of respondents indicating the presence of children under the age of 18 living at home. Segmentation with Cluster Analysis

Before cluster analysis, comparisons were made between overnight tourists and day excursionists in terms of their demographic characteristics, family decision-making patterns, and other travel-related behaviors to examine whether the duration of travel affected respondents’ behavior patterns. No significant differences were found between the two groups; therefore, all respondents were included in the cluster analysis. The hierarchical cluster analysis with Ward’s method suggested that three clusters were appropriate when segregating respondents into mutually exclusive groups using decision-making modes. Consequently, a nonhierarchical cluster



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Table 1 Demographic Characteristics Characteristic Gender (n = 297) Male Female Income (n = 254) Less than $20,000 $20,000 to $29,999 $30,000 to $39,999 $40,000 to $49,999 $50,000 to $74,999 $75,000 or more Age (n = 297) 18 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years 55 to 64 years 65 or older Marital status (n = 297) Single Married or in a long-term marriage-like relationship Divorced Widowed Current employment (n = 295) Employed Unemployed Retired Spouse/partner’s employment (n = 277) Employed Unemployed Retired Education (n = 294) Grade school High school / GED Some college or 2-year degree 4-year college More than 4-year college degree Presence of child(ren) under 18 living at home (n = 295) Yes Age of the youngest child under 18 years (n = 52)

%

43.8 56.2 2.8 10.6 16.1 13.0 27.6 29.9 1.7 4.7 11.1 22.3 31.4 28.7 5.7 89.6 2.7 2.0 49.8 4.7 45.4 46.9 5.4 47.3 2.0 19.4 31.0 18.7 28.9 16.9 Mean = 8.36; Mode = 2

analysis was performed to generate three clusters. The first cluster accounted for 10.4% (n = 31) of the respondents, the second cluster represented 25.6% (n = 76), and the third cluster included 64.0% (n = 190). Respondents in Cluster 1 had the largest travel party, with a mean of 4.2 people. More respondents in this group traveled with their parents (35.5%) or family members other than their spouse and children (36.3%); therefore, this cluster was named “intergenerational (ITG) travelers.” Cluster 2 was named “business-mixed-with-pleasure (BMP) travelers” because respondents in this cluster traveled mainly for business (48.2%), but they


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Figure 1 Decision-Making Modes in the Family Decision-Making Process Famly Decision-Making Modes by Stage 100 87.7

90 80

72.9

70

%

Couple joint decision

60 50

54.4 43.6

41.9

40

Male dominant

30 20

9.5

10

8.1

26.0

22.1

25.7 16.0 9.8

0 Need Recognition

25.7

15.2

Female 25.3 dominant Information Collection

Information Evaluation

Destination Selection/ Final Decision

Travel Arrangement/ Actual Purchasing

Decision Stages

Family Decision-Making Modes on Subdecisions 100 90

87.7

Couple joint decision

80 68.7

70

67.8

76.1 70.9 63.5

60

%

50 40 30 20

Male dominant Female dominant 15.2

11.4

10 0

13.6

14.7

9.8

10.1

11.2

9.9

General Destination

Particular Location

Length

Budget

14.0 5.5

15.7

5.5 Activity

Accommodation

Vacation Subdecisions

also enjoyed vacations with their partner while on business trips. More than one half of the members in Cluster 3 were retired (66.1%) and traveled with their partner (91.1%), having a primary purpose of visiting friends and relatives (64.3%). Therefore, Cluster 3 was labeled as “visiting friends and relatives (VFR) travelers.” When the primary decision-making modes were compared, significant differences were found in all 10 decisions among the three clusters (see Table 3). Members of the ITG cluster used “other joint” decision-making type in 7 out of the 10 decisions. This indicated the complexity of the decision-making process when multiple individuals and intergenerational travel companions are involved. BMP



Table 2 Gender Differences on Decision-Making Mode (in percentages) Decision-Making Stages and Subdecisions Need Recognition (n = 52) Gender of Respondent Male Female

Male Dominant

Female Dominant

68.2 31.8 43.3 56.7 χ2 = 3.15 p = .067

Information Collection (n = 152) Male Dominant

Female Dominant

82.5 17.5 28.1 71.9 2 χ = 43.75 p = .000

Information Evaluation (n = 112) Male Dominant

Female Dominant

83.3 16.7 17.1 82.9 2 χ = 47.22 p = .000

General Destination (n = 74) Male Dominant 78.1 47.6

Female Dominant

21.9 52.4 2 χ = 7.09 p = .007

Decision-Making Stages and Subdecisions Particular Location (n = 64) Gender of Respondent Male Female

Male Dominant

Female Dominant

75.0 25.0 36.1 63.9 χ2 = 9.56 p = .002

Length of Trip (n = 73) Male Dominant

Female Dominant

63.6 36.4 47.5 52.5 χ2 = 1.90 p = .127

Budget (n = 72) Male Dominant

Female Dominant

72.7 27.3 48.7 51.3 χ2 = 4.25 p = .033

Activity (n = 32) Male Dominant 64.7 33.3

Female Dominant

35.3 66.7 χ2 = 3.14 p = .078

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(continued)

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Table 2 (continued) Decision-Making Stages and Subdecisions


Accommodation (n = 87) Gender of Respondent Male Female

Male Dominant

Female Dominant

76.5 23.5 28.3 71.7 χ = 19.29 p = .000

Travel Arrangement (n = 152) Male Dominant

Female Dominant

80.0 20.0 30.4 69.6 χ2 = 35.69 p = .000


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travelers reported that male dominant was the most common decision-making mode, except for the need arousal and determination of activities to participate in, which were decided jointly by the couple. The male dominance may be due to the fact that over half (65.8%) of the respondents in this cluster were men, and the vacation plan had to accommodate the itinerary and arrangement of the business trips. However, the spouse did have input on whether to go along and which activities to engage in during the trip. For VFR travelers, couple joint decision making was the most prevalent for all 10 vacation-related decisions, mirroring their travel characteristics that both partners traveled together. Assessments of Clusters as Feasible Segments

Profitability of each cluster was evaluated by reviewing the average travel party size, expenditure per travel party, expenditure per person, and expenditure on lodging, food, shopping, transportation, and other areas. Three clusters were found to be significantly different on average travel party size, expenditure per person, and shopping expenditure per travel party (see Table 4). The ITG segment had the largest travel party with a mean of 4.2 people, whereas the other two had fewer than 3 people. As for travel-related expenditures, VFR travelers spent the most with an average of $54.11 per person, whereas ITG travelers reported the lowest average of $21.13 per person. However, due to their large travel party, ITG travelers’ average expenditure per party was not significantly different from that of the other two groups. Although no significant differences were found on lodging, transportation, and other spending, VFR travelers spent significantly more on shopping compared to ITG and BMP travelers. Information sources, geographic origins, demographic characteristics, and travel companion were used to evaluate the markets’ accessibility. Specifically, information used in trip planning included the Internet; personal experience and word of mouth; travel information and collaterals available at TICs, local hotels, attractions, and state tourism offices; and mass media such as newspapers, magazines, radio, and TV. Only travel information obtained from TICs, hotels, attractions, and state tourism offices was used differently by the three clusters. Particularly, 66.9% of VFR travelers used this category of information for their travel planning, whereas only 8.9% of ITG and 21.1% of BMP travelers obtained travel information from this source (χ2 = 11.47, p < .05). Geographic origin was categorized into three areas based on respondents’ residing states: neighboring states (i.e., six states surrounding Kansas), moderately distant states (i.e., the states surrounding the neighboring states), and remote states. Members of the three clusters were not significantly different in terms of their geographic origin. Members of the three segments were further compared on their demographic and travel party composition characteristics. Each of the three clusters showed distinctive demographic characteristics in terms of age, gender, retirement status, marital status, and travel companions (see Table 5). ITG travelers were the youngest, with the smallest proportion (35.8%) of people aged 55 or older and the smallest percentage (27.2%) of people retired. This group also reported the lowest percentage of being married or in a long-term marriage-like relationship (49.9%).


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Table 3 Primary Decision-Making Modes Among Three Clusters Decision-Making Stages and Subdecisions Cluster ITG BMP VFR

Need Recognition (%)

Information Collection (%)

Information Evaluation (%)

General Destination (%)

Couple joint (44.8) Couple joint (46.1) Couple joint (87.9) χ2 = 169.28 p = .000

Other (23.3)

Other (32.1)

Other (29.0)

Male dominant (68.4) Couple joint (61.1) 2 χ = 195.39 p = .000



Decision-Making Stages and Subdecisions Cluster ITG BMP VFR

Particular Location (%) Couple joint (41.9) Male dominant (39.5) Couple joint (84.7) χ2 = 169.23 p = .000

Length of Trip (%) Other (31.0) Male dominant (40.8) Couple joint (86.3) 2 χ = 204.34 p = .000

Budget (%) Couple joint (46.2) Male dominant (42.1) Couple joint (85.8) 2 χ = 161.83 p = .000

Activity (%) Other (33.3) Couple joint (56.6) Couple joint (90.0) 2 χ = 153.23 p = .000

Decision-Making Stages and Subdecisions Cluster

Accommodation (%)

Travel Arrangement (%)

ITG BMP VFR

Other (33.3) Male dominant (43.4) Couple joint (82.6) χ2 = 224.43 p = .000

Other (33.3) Male dominant (63.2) Couple joint (61.1) 2 χ = 226.72 p = .000

Note: ITG = intergenerational travelers; BMP = business-mixed-pleasure travelers; VFR = visiting friends and relatives travelers.

As for travel companions, ITG was more likely to travel with their parents (35.5%) and other members of the family (36.3%) and less likely to travel with their spouse/partner (57.9%), compared to the other two markets. Approximately two thirds of BMP travelers were men (65.8%) and aged 55 or older (66.7%). Compared to ITG, BMP travelers were more likely to be married or in a long-term marriage-like relationship (85.5%) and traveled with their spouse/partner (83.8%). Members of the VFR market were the most likely to be married or in a long-term marriage-like relationship (93.7%), retired (66.1%), and traveled with their spouse/partner (91.1%). They were the least likely to travel with their parents (3.7%) or other members of the family (9.6%).



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Table 4 Profitability Assessment

Cluster ITG BMP VFR

Average Travel Party Size

Average Expenditure per Person

4.21 1.99 2.47 F = 6.18 p = .006

Average Expenditure per Travel Party for Shopping

$21.13 $42.48 $54.11 F = 12.75 p = .000

$22.18 $40.24 $62.54 F = 4.69 p = .009

Note: ITG = intergenerational travelers, BMP = business-mixed-pleasure travelers; VFR = visiting friends and relatives travelers.

Table 5 Accessibility Assessment (in percentages)

Cluster ITG BMP VFR

ITG BMP VFR

Aged 55 or Older

Female

Retired

Married or in a LongTerm MarriageLike Relationship

35.8 66.7 61.6 χ2 = 22.05 p = .017

71.0 34.2 57.9 2 χ = 6.15 p = .046

27.2 48.7 66.1 2 χ = 11.60 p = .007

49.9 85.5 93.7 2 χ = 18.21 p = .009

Spouse/Partner Accompanied

Parents Accompanied

Others Accompanied

57.9 83.8 91.1 χ2 = 20.17 p = .000

35.5 4.1 3.7 2 χ = 41.79 p = .000

36.3 10.8 9.6 2 χ = 6.74 p = .026

Note: ITG: intergenerational travelers, BMP: business-mixed-pleasure travelers; VFR: visiting friends and relatives travelers.

The reachability of each cluster was assessed by investigating their primary travel purpose, the usefulness of information received at TIC, and the average number of activities participated in while in the state. Members of each segment traveled for different purposes (χ2 = 23.71, p < .05), with ITG travelers mainly for vacations (53.2%), BMP for business (48.2%), and VFR for visiting friends and relatives (64.8%). As for the usefulness of information obtained from TIC, 30.1% of VFR travelers found the information regarding special events and festivals useful in planning their vacations, compared to 12.6% of ITG and 7.5% of BMP travelers who valued the information (␹2 = 8.71, p < .05). No significant differences were found among segments on the average number of activities participated in, with all groups partaking in one or two activities while in the state.


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DISCUSSIONS AND CONCLUSIONS

This study segmented family travelers based on their vacation-related decisionmaking patterns and portrayed each segment with unique travel behaviors and demographic characteristics. Results of this study supported what was reported in previous studies (Filiatrault & Ritchie, 1980; Fodness, 1992; Jenkins, 1978; Nichols & Snepenger, 1988) that couple joint decision making was the most dominant mode in making vacation-related decisions in a family. Although previous studies indicated that women were more likely than men to be involved in travel information collection (Fodness, 1992; Myers & Moncrief, 1978; Nichols & Snepenger, 1988), findings of this study revealed that information collection was shared jointly between partners by 41.9% of the respondents and dominated by a man in 26.0% of the families. This may be attributed by the relatively large proportion of respondents aged 55 or older because as people get older they are more likely to make joint decisions (Fodness, 1992). Respondents of the study failed to reach a consensus on the relative influence of family members. Male and female respondents perceived they had more influence on family vacation decisions. Therefore, both genders are equally important from a marketer’s perspective. Promotional information that is free of gender bias and appeals to men and women should be presented to effectively persuade couples making decisions in each stage of the family vacation-planning process. The study’s purpose of extending the scope of family vacation decisionmaking research by profiling various family vacation segments based on decisionmaking types was accomplished. Findings of the study indicated that family travelers could be meaningfully segmented based on their decision-making patterns in that the three segments generated showed distinct travel behaviors and demographic characteristics. Although the three distinctive clusters can help provide differentiated marketing focus in accordance with the unique travel behaviors and demographic characteristics of each group, ITG and BMP segments may not be realistically pursuable from the Kansas state tourism promotion agency’s perspective. ITG travel is an emerging market in the tourism industry (Blum, 1996; Gardyn, 2001); however, ITG travelers in this study scored low on market size and profitability in terms of per-person expenditure and spending on shopping. Even though they may be accessible based on their demographic characteristics, various members of the ITG traveler families shared different decision-making tasks related to their vacations, thus targeting and communicating with specific members of the family could be a challenge. The BMP travelers, on the other hand, demonstrated moderate profitability performance; however, they were not unique in their travel information sources, geographic origins, and number of activities participated in at a destination. Besides the facts that they were middle-aged, male business travelers either married or in a long-term marriage-like relationship and liked to travel with their spouse/partner, more information about this group is needed to target this market effectively. VFR travelers—the largest segment identified in this study—are worth pursuing actively by the state tourism agency. Based on the requirements for a group to



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be a viable and actionable segment recommended by Kotler (1991) and Morrison (1996), VFR travelers were highly homogeneous in terms of their travel party composition, demographic characteristics, and decision-making modes. Being the largest group among visitors who stopped by TICs, they also found information on special events and festivals available at the TICs helpful in planning their trips. In addition, the majority of VFR travelers preferred to obtain information from TICs, hotels, attractions, and state tourism offices, which makes the promotion agency’s job easier because the state tourism office has direct control over the TICs and has a good working relationship with hotels and attractions. This segment also performed well on profitability measurements, having the largest number of travelers, the highest average expenditure per person, and the highest expenditure on shopping. To attract more travelers in this segment and to serve them better, tourism marketers should take advantage of advertising and promotion opportunities available at TICs, hotels, attractions, and state travel information offices where VFR travelers tended to obtain travel-related information. The state tourism promotion agency would want to allocate a significant amount of space in its travel guide to provide information on special events and festivals across the state because VFR travelers indicated that information about these particular occasions available at TICs was valuable in their travel planning. Destination marketers should also implement strategies to enhance local residents’ knowledge and perception of the state as a tourism destination. Because VFR travelers are likely to have contact with their friends and relatives while planning their vacations, referrals from local residents should generate fruitful outcomes by suggesting places to visit and activities to participate in, and create a positive impression of the state. Particularly, updating state residents about upcoming festivals and events and encouraging them to attend those special activities with their visiting friends and relatives will foster positive experience of those vacationers and gain their repeat visitations. Informing residents of various shopping locations for a variety of merchandise should also help maximize the already high shopping expenditure of the VFR segment. LIMITATIONS AND FUTURE RESEARCH

This study contributed to the family vacation literature not only on investigation of individual member’s influence in each decision-making stage but also on extending family decision-making research to use decision mode as a market segmentation criterion. Even though the data were collected in one U.S. state and the resulting segments may not be applicable to other regions, the segmentation approach and research method can be easily adopted by other destination marketers to understand their particular market’s decision-making characteristics and to segment their market into meaningful groups based on its feasibility and importance. As illustrated in this study, family travelers can be distinguished based on their decision-making attributes, and significant marketing implications can be derived.


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This study was only a first step in understanding the various family travel segments based on their decision-making patterns. Future research on each segment identified in this study could be conducted by investigating each group’s unique characteristics (including psychographics and travel motivation) to comprehend and serve each segment effectively. Woodside and Jacobs (1985) suggested that segmentation studies should be conducted periodically, once every 2 or 3 years, to identify positive and negative trends in benefits provided to visitors. Therefore, follow-up studies with TIC visitors in Kansas will aid the state tourism office and local businesses in monitoring any changing characteristics of visitors, thus adjusting their advertising messages and revising products and services offered. The majority of respondents in the study were mature travelers (60.1% aged 55 or older) without children under the age of 18 living at home (83.1%), which minimized the measure of children’s influence on vacation-related decisions. Future studies could be conducted with an expanded sample to understand younger families’ decision-making dynamics and to more comprehensively profile travelers’ family decision-making process. Findings of the study showed a discrepancy in perceptions on the extent of influences between male and female partners. Because researchers in this study had no control over who in the family completed the survey, the exact cause of this discrepancy cannot be identified. In addition, only one person in a travel party was asked to complete the questionnaire in assessing family members’influence; therefore, the extent of relative influence of multiple members in a travel party may not be an accurate reflection of the true family decision-making dynamics. Future research should be conducted to collection information from multiple members of the family to understand different individuals’perceptions to increase the validity of findings on relative influences. A limitation of the study was that no comparison between nonrespondents and respondents or between early respondents and late respondents was made due to logistic and time-related issues. Travelers who did not visit a TIC may also have different characteristics than those who visited a TIC. No respondent profile was available from previous similar research to determine the representativeness of respondents in this study among all visitors to Kansas. Similar studies with different sampling methods and larger sample sizes could be conducted to crossvalidate the results of this study. In addition, data from this study were collected from one U.S. state; therefore, results had limited generalizability to family travelers in other parts of the country or the world. REFERENCES Arimond, G., & Elfessi, A. (2001). A clustering method for categorical data in tourism market segmentation research. Journal of Travel Research, 39(4), 391-397. Assael, H. (1998). Household decision making. In H. Assael (Ed.), Consumer behavior and marketing action (5th ed., pp. 565-601). Cincinnati, OH: South-Western College Publishing. Blood, R. O., & Wolfe, D. M. (1960). Husbands and wives: The dynamics of married living. Glencoe, IL: The Free Press of Glencoe. Blum, E. (1996, March). Catering to families. Travel Weekly, 126, 9-10.



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Submitted February 15, 2002 First Revision Submitted July 18, 2002 Final Revision Submitted October 29, 2002 Accepted November 5, 2002 Refereed Anonymously Soo K. Kang (e-mail: [email protected]), Ph.D., is assistant professor in restaurant and resort management at Colorado State University. Cathy H.C. Hsu (e-mail: hmhsu@ polyu.edu.hk), professor and graduate programs director in the School of Hotel & Tourism Management at The Hong Kong Polytechnic University. Kara Wolfe (e-mail: [email protected]), Ph.D., assistant professor in the Department of Apparel, Design, Facility and Hospitality Management at North Dakota State University.


Research Journal of Hospitality & Tourism

Research Journal of Hospitality & Tourism

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