Management Tools. Stacy Warner ... with an additional tool in their efforts to recruit and retain volunteers by prioritizing features that will most ..... determine how best to satisfy volunteers to retain their ...... A profile of community sport volunteers.
Journal of Sport Management, 2011, 25, 391-407 © 2011 Human Kinetics, Inc.
More than Motivation: Reconsidering Volunteer Management Tools Stacy Warner East Carolina University
Brianna L. Newland Victoria University
B. Christine Green University of Texas at Austin Volunteers provide an essential human resource to sport organizations. Yet measures of motivation and satisfaction have had limited impact on an organization’s ability to improve their volunteer systems. This study applied the Kano Method to categorize volunteers’ perceptions of their experience into four dimensions of satisfaction: Attractive (or Satisfiers), Must-Be’s (or Dissatisfiers), One-Dimensional, and Indifferent. Four types of volunteers (44 sport continuous, 47 sport episodic, 49 nonsport continuous, 176 nonsport episodic) completed a web questionnaire including 26-paired features of their experience, 26 motives, and five key outcome measures. Although motives were deemed important, alone they were poor predictors of key outcomes and were unrelated to satisfaction. Volunteers in the four contexts classified the 26 features in different ways. No Must-Be’s (dissatisfiers) were identified by any group. Although most features were identified as Attractive, the distribution of One-Dimensional and Indifferent features varied by context. One-dimensional items were only identified among features categorized as Supportive Culture, Clear Direction, and Contribution. These features should be prioritized as managers improve volunteer management systems. The Kano Method extends our understanding of the volunteer experience by providing researchers with a tool to distinguish the way volunteers conceptualize their experience. From a practical standpoint, it provides volunteer managers with an additional tool in their efforts to recruit and retain volunteers by prioritizing features that will most immediately impact volunteers. The importance of volunteers as an essential and somewhat unique human resource in sport has been well established (Chelladurai, 2006; Cuskelly, McIntyre, & Boag, 1998; Doherty, 1998, 2005, 2006; Doherty & Carron, 2003; Elstad, 1997; Green & Chalip, 1998). In fact, many sport organizations could not survive without the support of volunteers (Costa, Chalip, Green, & Simes, 2006; Doherty & Carron, 2003; Finkelstein, 2008). While a heavy reliance on volunteers is not surprising in the nonprofit sector of the sport industry, for-profit sport organizations are also heavily reliant on volunteers. For example, Super Bowl XLII recruited 10,000 volunteers in 2008 (Arizona Super Bowl XLII Host Committee, 2008). Volunteers are also integral to staging the NBA Warner is with with the Dept. of Kinesiology, East Carolina University, Greenville, NC. Newland is with the School of International Business, Victoria University, Melbourne, VIC, Australia. Green is with the Dept. of Kinesiology and Health Education, University of Texas at Austin, Austin, TX.
All-Star Week, the Daytona 500, the US Tennis Open, and most other major sport events. Many youth and community sport organizations and leagues rely heavily on volunteer labor. Volunteers contribute invaluable time and cost savings to an organization; consequently, many sport organizations and events would not be financially viable without volunteers. Although volunteers are not without cost to the organization, they consistently provide a significant return on investment (Segal & Weisbrod, 2002; Whitley, Everhart, & Wright, 2006). Volunteers often provide the most basic of labor (e.g., handing out water, stuffing bags, set-up and clean-up), yet volunteers can also be a source of expertise needed by sport organizations (Chelladurai & Madella, 2006). Volunteers with the appropriate expertise or qualifications can provide sport organizations with legal advice, financial and tax accounting, marketing assistance, and a host of other skills. It is easy to see the value of volunteers as an organizational resource, and the heavy reliance that many sport organizations have on volunteer resources. 391
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Volunteer resources are limited; in fact, volunteers are becoming an increasingly scarce resource. The Australian Bureau of Statistics (ABS) reported a 3% increase in volunteers from 2000 to 2006, however, Australia has shown a decline in the number of hours volunteers contributed (ABS, 2008). Similar trends indicating that volunteers are becoming a scare resource are true in North America. Statistics Canada (2000) found a 13% decrease in the number of Canadians who volunteered in 2000 compared with 1997, while the number of hours contributed by each volunteer increased. This indicates that fewer volunteers are working longer hours to fulfill a need by organizations. Likewise, in the United States, 2007 data revealed a decline in both the number of volunteers and the volunteer rate from the previous year (US Department of Labor, 2007). To make matters worse, the number of sport organizations needing volunteers continues to grow. As a result, organizations compete for these scarce resources (i.e., volunteers), and cautiously guard the resources they already have. Consequently, volunteer managers face dual challenges: (1) recruiting volunteers in a highly competitive marketplace, and (2) retaining volunteers to maximize their investment in those volunteers. Traditionally, volunteer management has addressed these challenges from a human resource management perspective (e.g., Cuskelly, Taylor, Hoye, & Darcy, 2006). Since volunteers fulfill human resource needs for sport organizations, it is not surprising that volunteer researchers have also taken this perspective. However, volunteers, unlike paid workers, construct their volunteering as a leisure activity (Stebbins, 1982; Williams, Dossa, & Tompkins, 1995). Consequently, the volunteer experience, though it may include instrumental tasks, is part of a broader expressive experience (Chalip, Kellett, & Green, 2001). The shift toward understanding volunteering as a leisure choice has led researchers to try to understand volunteers as consumers (Laverie & McDonald, 2007) rather than merely unpaid workers. As a result, the dual challenges faced by volunteer managers sit clearly in the realm of consumer behavior. The first, recruitment of volunteers, suggests the need to understand motives for volunteering. The second, retention of volunteers, suggests the need to determine volunteers’ satisfaction. In essence, effective recruitment depends on identifying and structuring volunteer systems to provide the benefits sought by potential volunteers, and retention is a function of ensuring that volunteers obtain the benefits they expect. Researchers have examined volunteer recruiting and retention largely using quantitative, survey based research designs (e.g., Costa et al., 2006; Cuskelly & Boag, 2001; Farrell, Johnston, & Twynam, 1998). Motives are often measured via survey to determine ways to attract volunteers to an organization and satisfaction levels have been measured to understand ways to retain existing volunteers. These measures consistently use Likert or Likerttype scales to measure motives (cf., Green & Chalip, 2004; Eley & Kirk, 2002; Farrell et al., 1998). However, as Green and Chalip (1998) have pointed out, this is a
simplistic way of evaluating volunteer management systems and has not provided the necessary clarification to improve such systems. Thus, the purpose of this study is to examine the potential benefits of supplementing traditional measures of motivation and satisfaction to further extend our understanding of volunteers and to improve our volunteer management systems.
Literature Review Motivation Due to the sheer number of potential motives, it is no wonder that numerous volunteer motives have been identified in the literature. After extensively reviewing numerous volunteer studies, Cnaan and Goldberg-Glen (1991) identified 28 volunteer motives. In addition, they determined that 22 of those 28 motives loaded on a single factor, consequently indicating that volunteer motivation is a one-dimensional item. This is contrary to Clary et al.’s (1998) findings, which determined that volunteer motives are multifaceted. In fact, Clary and his colleagues identified six categories, which they found to encompass a majority of the volunteer motives. These six main motives for volunteering were: Values (humanitarianism and altruistic reasons), Understanding (skill development), Ego Enhancement (psychological growth), Career (gaining experience that will advance one’s career), Social (improving social relationships), and Ego Protective (providing an escape from negative feelings and personal problems). Utilizing a functionalist approach, Clary et al. operationalized these motive categories into subscales in the development of the Volunteer Functions Inventory (VFI). Although Allison, Okun, and Dutridge’s (2002) work, which used open-ended probes to determine volunteer motives, supported the use of the VFI and its categories for predicting frequency of volunteering, their study also identified enjoyment, religiosity, and team building as additional motives for volunteering. Overall, the literature supports the multifaceted nature of volunteer motivation (Gerstein, Anderson, & Wilkeson, 2004; Okun, Barr, & Herzog, 1998) Recruitment and retention of volunteers is more effective when multiple motives are used to target persuasive communications, assign tasks, and structure experiences to specific volunteers (Clary et al., 1998; Clary, Snyder, Ridge, Miene, & Haugen, 1994). Thus, volunteers highly motivated by social opportunities and career advancement could be recruited with brochures highlighting the people one can meet while volunteering, particularly people who could advance one’s career. Similarly, training sessions for these volunteers should be conducted in a group setting, and team-oriented tasks assigned to this volunteer type. However, motives can change over time (Boling, 2006; Gidron, 1984). In fact, what may initially compel people to volunteer will not necessarily motivate them to continue volunteering (Finklestein, 2008; Okun & Schultz, 2003; Starnes & Wymer, 2001). This is also true of other leisure activities.
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Leisure researchers have shown that past experience with a leisure activity can influence perceptions of the resources available through the activity (Hammitt, Backlund, & Bixler, 2004; McFarlane, 2004). Often conceptualized in terms of past use experience and frequency of use, termed, “experience use history” or EUH (Schreyer, Lime, & Williams, 1984), experience with an activity or an organization/setting seemed to expand participants’ understanding of the potential benefits that can be obtained through participation (Kaplan & Kaplan, 1989). More experienced participants often report different motives than are reported by less experienced participants. This phenomenon holds true for outdoor recreationists (e.g., Williams, Schreyer, & Knopf, 1990) as well as for sport participants (e.g., Petrick, Backman, Bixler, & Norman, 2001). Like recreation and sport participation, volunteering is a leisure choice. Consequently, one would expect previous experience with an organization or as a volunteer to affect the benefits volunteers seek from their experience. Differences between experienced and inexperienced volunteers may be a function of the socialization process that occurs via interactions with the organization and the staff, volunteers, and constituents served by the organization. The socialization process is a powerful means of transferring organizational norms and values (Bandura, 1977), and can greatly impact volunteers’ experiences. As an individual interacts with an organization and its culture, it is likely that the individual may be socialized to value different benefits or may even discover new benefits. Consequently, a volunteer may be motivated to continue volunteering for different reasons other than those that initiated his or her volunteering. Contact with the organization and with more experienced volunteers may shed light on new benefits that were originally unknown to prospective volunteers. For example, an individual might initially volunteer with an organization for career-related motives, such as making career contacts. During his or her volunteer experience the individual may also discover the joy of helping others. Altruism may then drive further volunteering. Understanding volunteers’ motives can assist volunteer coordinators to better cater to the needs of volunteers by working to provide the benefits sought by volunteers (Fairley, Kellett, & Green, 2007; Green & Chalip, 2004). At first glance, this seems straightforward. Volunteer coordinators could design communications to attract volunteers by highlighting desired benefits of volunteering, and could match volunteers to tasks based on the benefits sought. Alas, it is rarely this straightforward. To design communications to attract volunteers, it is necessary to determine their motives before they volunteer (or at least before they have any interactions with the organization). Although it is possible for organizations to include a survey of motives in volunteer application packets, few organizations have the time or expertise to effectively analyze the survey data and use it to select and assign volunteers. In fact, with rare exception (e.g., Eley & Kirk, 2002), most volunteer motivation research
has been conducted with volunteers after they have had significant interactions with the organization (e.g., Costa et al., 2006; Farrell et al., 1998; Green & Chalip, 2004). Thus, the motives identified may have already been affected by socialization processes. As a result, these motives may not map directly to the original motives that spurred volunteering. To complicate things further, volunteers are driven by multiple motives. In studies of volunteer motivation, nearly all motives are identified as important (e.g., Eley & Kirk, 2002). Volunteer coordinators are then challenged to either (1) attempt to cater to all motives, which requires the investment of significant resources, or (2) choose the motives that will have the most impact. Although it would be a more efficient use of resources, it is difficult, if not impossible, for volunteer coordinators to select motives to cater to by ranking the mean scores for each motive since the variances nearly always overlap. The inability of motives to discriminate volunteers makes it difficult to create effective communications to attract volunteers, and even harder to match volunteers to particular tasks. One could speculate that changes in volunteers’ motives, particularly when those changes more closely align volunteers with the norms and values of the organization, may have the ability to create core, shared motives which are deemed important by a majority of volunteers. This would provide a potential starting point for winnowing the benefits emphasized by the volunteer program and its staff. However, this is mere speculation. Understanding volunteers’ motivations is an intuitively appealing way to build management systems to attract and retain volunteers. Yet, merely understanding and even catering to motives does not guarantee that volunteers will have a positive experience. This has led researchers and practitioners alike to focus on volunteers’ satisfaction.
Satisfaction Satisfaction has been defined as the difference between what one wants and what one obtains from one’s job (Chelladurai, 2006). Customer satisfaction has long been shown to be associated with repeat purchase (e.g., Zeithaml, Berry, & Parasuraman, 1996) and consumer loyalty (Fitzell, 1998; Fornell, 1992; Reynolds & Beatty, 1999; Zeithaml et al., 1996). Similarly, job satisfaction is associated with decreased turnover (Galindo-Kuhn & Guzley, 2001) and increased commitment to the organization (Güleryüz, Güney, Aydın, & Asan, 2008; Schwepker, 2001). With fewer people volunteering and the increased demand for volunteer support, retaining motivated and skilled volunteers is critical. Like other consumers, volunteers are more likely to want to continue to volunteer if the experience has been a satisfying one (Green & Chalip, 2004). Thus, volunteer managers must determine how best to satisfy volunteers to retain their services. Understanding the extent to which volunteers’ needs were met by the organization and the identification of such facets has been shown to predict retention and
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turnover (Galindo-Kuhn & Guzley, 2001). The ability to limit turnover can spare the organization from diverting resources to recruit and train rather than furthering organizational objectives (Cuskelly & Boag, 2001; GalindoKuhn & Guzley, 2001). Cuskelly and Boag (2001) measured organizational commitment as a predictor for turnover and found that volunteers who showed higher levels of commitment were less likely to leave the organization. Volunteers who are committed to the organization volunteer for longer periods, show higher intentions to return (Finkelstein, 2008), and repeat their service (Coyne & Coyne, 2001). In their study of volunteers at a golf tournament, Coyne and Coyne (2001) found that volunteers were more likely to return because of the “love of the game” and because they felt as though they were “getting something out of it,” such as a free round of golf or other reward. Research suggests that satisfaction with a specific volunteer episode is associated with positive perceptions of volunteering in general (Coyne & Coyne, 2001; Farrell et al., 1998). If volunteers are more satisfied, they are more likely to enjoy their experience, repeat volunteer, and inspire others to volunteer (Coyne & Coyne, 2001; Doherty, 2005; Finkelstein, 2008). Volunteers also reported satisfaction if the organization was able to fulfill the needs of the individual (Farrell et al., 1998; Finkelstein, 2008; Kim, Chelladurai, & Trail, 2007). For example, volunteers who discussed the degree to which their expectations were met by the organization were more satisfied (Silverberg, Marshall, & Ellis, 2001). Matching tasks with volunteers’ interests and providing the opportunity to learn new skills also helped in meeting the expectations of satisfied volunteers (Elstad, 1997; Finkelstein, 2008; Malenfant, 1987; Rail, 1987). Lastly, satisfied volunteers are more likely to recruit other volunteers to assist the organization. Coyne and Coyne (2001) found that satisfied volunteers were more likely to return to assist the organization and to refer friends and family. The satisfaction literature is largely focused on the retention of volunteers; however, utilizing the satisfied volunteers as a vehicle for the recruitment of volunteers is often overlooked. Research has suggested that the key instrument for recruiting ‘new’ volunteers is the existing corps of prior-service veterans (i.e., the continuing support) or the first-time volunteer’s ‘friend’ or the ‘friend of a friend’ (Coyne & Coyne, 2001; Doherty, 2005). The satisfaction research to date indicates that a large majority of sport volunteers are very satisfied with their experience and would volunteer again (Doherty, 2005; Elstad, 1997; Green & Chalip, 2004). Interestingly, it is quite rare to find dissatisfied volunteers. Perhaps this is a function of the timing of most volunteer research. Dissatisfied volunteers may not be around long enough to complete a survey. Or, like other experiences, high levels of satisfaction may be due to a halo effect, meaning as time passes the volunteer recollects only the positive experiences and lets the negative experiences fade away (e.g., Reeser, Berg, & Willick, 2005).
A closer look at the items used to measure volunteers’ satisfaction suggests a focus on aspects that have the potential to satisfy volunteers (e.g., Farrell et al., 1998; Finkelstein, 2008; Green & Chalip, 2004). Rarely do we consider what Herzberg (1966) calls, “hygiene factors,” those elements that do not enhance satisfaction, but have the ability to dissatisfy. While there has been much work that has contributed to our understanding of volunteer satisfaction and retention, the current satisfaction research is one-dimensional. Either volunteers are asked to report their global satisfaction with the experience, or they are asked to respond to a series of items measuring their satisfaction with a variety of elements often gleaned from the job satisfaction literature. Current satisfaction measures typically provide the volunteer with an element and ask how satisfied this makes them based on a scale ranging from ‘dissatisfied’ at one end of the continuum to ‘satisfied’ at the other. Thus, satisfaction alone (at least as it is usually measured) does not provide an adequate picture of the volunteer experience. Although more useful than motivation for structuring and improving volunteer management systems, satisfaction measures provide limited information for managers to improve the quality of the experience for their volunteers. The need for continuous improvement is not unique to volunteer management. Rather, managers from a full range of industries face a similar challenge to improve the quality of their systems, products, and services, and (like volunteer managers) customer experiences. Total quality management (TQM), with its emphasis on continuous improvement and satisfaction is a useful framework for considering volunteer management systems. Miller (1996) defined TQM as: an ongoing process whereby top management takes whatever steps necessary to enable everyone in the organization in the course of performing all duties to establish and achieve standards which meet or exceed the needs and expectations of their customers, both external and internal. (p. 157) Although we have argued that volunteers differ from employees or customers, the commonality of trying to motivate and satisfy “everyone within the organization” would surely encompass an organization’s volunteers. Accordingly, a volunteer can be considered both an external and an internal customer of the organization. This puts volunteers squarely within a TQM framework. Although there have been numerous studies of service quality in sport settings (e.g., Chelladurai & Chang, 2000; Howat, Absher, Crilley, & Milne, 1996; Kim & Kim, 1995), and several that support the use of TQM as an appropriate tool for managing service quality in sport (e.g., Lentell & Morris, 1995; Van Hoecke & De Knop, 2006), none have considered volunteers as an integral part of their model. Perhaps this is due to the complexity of the place of volunteers in most sport organizations; volunteers are both providers of sport services and customers of sport
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services. Thus volunteers defy categorization and increase the complexity in many of our customer service models. The Kano Method is a well-known tool within TQM that has been successfully used to understand the complexities of customer satisfaction in numerous settings. The method was developed by the Japanese quality expert, Dr. Noriaki Kano, in the early 1980s and has since been used primarily as a quality management and marketing tool. The Kano Method is equally effective in evaluating internal and external customer experiences. Given the focus of the Kano Method on customers’ experiences, it would seem to be particularly applicable to understanding the leisure experiences of volunteers. As such, it may provide a new window into the experiences of volunteers, one that our current measures of motivation and satisfaction do not adequately capture.
Evolution of Kano’s Method After extensively interviewing over 200 employees, Herzberg (1966) proposed the Two-Factor Theory, also known as Motivation-Hygiene Theory, to explain employee satisfaction. Herzberg’s theory posited that job factors related to satisfaction were distinct from job factors related to dissatisfaction. That is, job factors related to satisfaction would only be associated with levels of satisfaction. Their absence would not be associated with dissatisfaction (i.e., the opposite of satisfaction is no satisfaction). Factors associated with job dissatisfaction would, likewise, have no effect on satisfaction (i.e., the opposite of dissatisfaction is no dissatisfaction). . The Two-Factor Theory, more specifically, revealed that
Figure 1 — Kano’s Model (adapted from Kano et al. (1984)).
intrinsic factors were associated with satisfaction while extrinsic factors were associated with dissatisfaction (Herzberg, 1966; 1968). More importantly, Herzberg’s work implied that satisfaction should not necessarily be measured on a single continuum or scale, but rather should acknowledge that Satisfiers and Dissatisfiers were distinct constructs. This idea that Satisfiers and Dissatisfiers were distinct constructs served as the basis for the model underlying Kano’s Method (Kano, Nobuhiko, Takahashi, & Shinichi, 1984). While Herzberg’s work was primarily intended to understand employee motivation and satisfaction, Kano applied the idea to create a model to aid in product development and serve as a tool to understand and evaluate customer satisfaction. Volunteer management systems, akin to product development in other industries, benefit from a better understanding of volunteers’ experiences and satisfaction with those experiences. Kano’s Model (Figure 1) was based on customer expectations and preferences. Kano’s Model refers to those elements that are associated only with satisfaction as Attractive Elements (represented by the function above the x-axis), while elements that are only associated with dissatisfaction are referred to as Must-Be elements (represented by the function below the x-axis). Kano’s Model is more advanced and dynamic than Herzberg’s, in that it acknowledges that some factors lead to neither satisfaction nor dissatisfaction (Indifferent element, not represented in Figure 1), and that some factors can lead to either satisfaction or dissatisfaction (One-Dimensional elements, represented by the diagonal line) depending on their functionality. In
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addition, unlike Herzberg’s Two-Factor Theory, Kano acknowledged that preferences vary depending on the industry or market segment and evolve and change over time (Matzler, Fuchs, & Schubert, 2004). The flexibility of Kano’s Method is evident. It was initially used to determine customer preferences regarding a product (Berger et al., 1993), but it has also been successfully applied to understand employee satisfaction (Dixon & Warner, 2011; Matzler et al., 2004). This flexibility is critical to building better volunteer management systems since volunteers occupy a hybrid role in most organizations as both customer and employee. The classification system provides managers with a clear method of prioritizing improvements based on their potential for negative impact. Highest priority is assigned to Must-Be elements. The absence of these elements can only result in dissatisfaction. One-Dimensional elements are the next priority, as they are the only other elements that can result in dissatisfaction. Attractors do not have the capacity to dissatisfy; rather, more of these features can only increase satisfaction. Indifferent elements do not matter and need no attention. Consequently, one would expect the model to be a useful tool for building effective volunteer systems. The purpose of this study is to examine the degree to which the Kano Method can provide volunteer managers with useful information that cannot be obtained via traditional motivation and satisfaction measures. By determining which elements serve as Attractive, Must-Be, One-Dimensional, or Indifferent items, volunteer managers can better prioritize elements in the volunteer management systems that will lead to continuous improvement. Thus, our research was expected to: (1) Determine volunteers’ motives and the degree to which those motives predict key outcomes (i.e., commitment to the organization, intention to repeat volunteer, intention to tell others; belief in the organizations values and goals); (2) Examine the relationship between volunteers’ satisfaction and key outcomes; (3) Understand the ways in which use of the Kano Method can supplement traditional motivation and satisfaction measures to prioritize improvements to volunteer management systems; and (4) Investigate potential differences in volunteer contexts (i.e., sport versus nonsport, continuing versus episodic).
Method Participants The population for the current study consisted of 316 adult volunteers providing services for both sport and nonsport organizations throughout the United States. Ninety-one respondents volunteered with organizations whose primary industry was sport, 225 volunteered
for a variety of nonsport organizations. Ninety-two organizations that depend largely on volunteer services were contacted to solicit support for the study and 24 organizations agreed to contact their volunteers to participate in the study. Three hundred sixteen volunteers completed the survey, of which 61.9% were female. The majority of participants where white (73.1%), but the sample included Hispanics (12.9%), African Americans (3.8%), and Asians (3.0%). The age of the volunteers varied greatly with 33% between 19–39 years, 25% between 40–59 years, and 22.2% over the age of 60 years. The remaining respondents did not reveal their age. Respondents were highly educated with 34% having completed some college, and 54.2% having earned a bachelor’s, master’s, or terminal degree. Thirty percent of the respondents reported volunteering on an ongoing basis (i.e., continuous volunteers). The remaining 70% reported volunteering on an as needed basis, such as at events (i.e., episodic volunteers).
Procedure Human subjects approval and approval from the volunteer organization were obtained before data collection. The director of volunteers for each organization was contacted via an e-mail soliciting interest in participating in the study. If the director agreed to allow volunteers to participate, a second e-mail was sent to the director to disseminate to their volunteers. The directors were more inclined to participate knowing that researchers required no direct access to the volunteers. The second e-mail sent via the director invited the volunteer to participate and contained a direct link to the web-based survey. An attachment containing a more detailed description of the study, as well as risks and benefits to the participant, was included in the e-mail. The participants were asked to complete the 20-min survey during their free time. Participant consent was obtained via voluntary participation of the survey.
Instrumentation A web-based survey was developed to measure the volunteer experience. The constructs measured in the questionnaire included 26-paired Kano measures, and 26-items designed to measure the importance of each of the features represented in the Kano pairs to motivate respondents’ volunteering. Five single-item outcome measures were also evaluated: global satisfaction, intention to repeat volunteer, intention to convince others to volunteer, commitment to the organization, and degree to which respondents’ shared the values of the organization. In addition, volunteers were asked about the type of volunteering (ongoing, episodic, or both), context of volunteering (sport or nonsport), age, gender, and ethnicity. Kano Measures. The Kano Method was used to unpack the volunteer experience. This method for determining
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satisfiers and dissatisfiers was grounded in the evaluation of customer preferences and expectations. Core to the Kano Method is a requirement that participants answer paired questions related to both the functional and dysfunctional inquiries of potential satisfiers and dissatisfiers. An example functional inquiry question was: “If you are provided with free food while volunteering, how does this make you feel?”. The dysfunctional form of the question would then follow: “If you are not provided with free food while volunteering, how does this make you feel?”. As per the Kano Method, the researchers determined which of the paired items would be considered functional and which would be considered dysfunctional. The participants responded using the Kano scale, which included 5 possible responses: “I like it that way,” “It must be that way,” “I am neutral,” “I can live with it that way,” and “I dislike it that way.” Seven categories of potential Kano measures were identified based on the volunteer literature: rewards, education/career, skills, organizational factors, service/ altruism, personal/social, and prestige. Specific items were created to represent each of the categories. An expert panel of four volunteer managers was asked to consider the list of thirty-two items for relevance, redundancy, and omissions. At this time, four of the items were considered redundant by all experts; two were considered irrelevant to the volunteer context. These items were removed, leaving 26 factors. Functional and dysfunctional items were developed for each factor to create 26 Kano pairings. An evaluation table (see Table 1) was then used to determine the classification of the element in question (cf. Berger et al., 1993). As recommended by Kano, classification was based on the highest frequency of responses to the paired functional and dysfunctional questions. Along with the four Kano classifications (Attractive, Indifferent, Must-Be, and One-Dimensional), two other
conclusions could be drawn, Questionable and Reverse. A Questionable outcome would indicate that the feature should be reevaluated. Questionable outcomes occur when respondents choose similar responses to each of the paired items. For example, it would be highly unlikely that a participant would respond “I like it that way” to being provided with food and to not being provided with food. Either the respondent did not understand the paired items, or s/he responded randomly without reading the items carefully. In either case, a high percentage of respondents answering in this way requires researchers to reevaluate the item pairing. A Reverse outcome would indicate that the researchers miscategorized the initial pairing’s functionality. That is, participants concluded that the functional form of the question (as determined by the researchers) should have been categorized as dysfunctional. Consequently, Reverse outcomes should be recategorized. After recategorization, the pairing should fit into one of the original Kano classifications. Neither outcome (Questionable or Reverse) is a Kano classification, rather each is an artifact of measurement. Motives. The motivation items were derived directly from the Kano question pairs. That is, the motivation variables matched the 26 identified variables, but were worded to determine the level of motivation for each variable. An example motivation question asked, “Think about your decision to volunteer. How important was it to be provided free food?” Responses included: “Not important at all,” “Somewhat important,” “Important,” and “Very important.” The responses were scored as 1 (not important at all) to 4 (very important). Outcomes. The outcome measures were chosen to
represent the critical outcomes sought by volunteer managers: volunteers’ satisfaction, intention to volunteer again, intention to recruit others, commitment to the
Table 1 Kano Classification Table Functional
Dysfunctional 1. Like
2. Expected
3. Neutral
4. Live With
5. Dislike
1. Like
Q
A
A
A
O
2. Expected
R
Q
I
I
M
3. Neutral
R
I
I
I
M
4. Live With
R
I
I
Q
M
5. Dislike
R
R
R
R
Q
Adapted from Berger et al., 1993 Note. A = Attractive (causes only satisfaction) I= Indifferent (does not cause satisfaction or dissatisfaction) M = Must-Be (causes only dissatisfaction) O= One-Dimensional (can lead to satisfaction or dissatisfaction) Q = Questionable (this indicates the question should be reevaluated) R= Reverse (this indicates the question was not written appropriately and that the participants concluded that the Functional form of the question should have been dysfunctional).
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organization, and goal alignment. These are also the outcomes most commonly measured in studies of volunteers (e.g., Costa et al., 2006; Coyne & Coyne, 2001; Cuskelly et al., 2006; Laverie & McDonald, 2007). Each of these outcomes is a clear consequence of the volunteer experience. Volunteers were asked to respond to the following items, “I am satisfied with my volunteer experience” (global satisfaction); “I will definitely volunteer with this organization again” (Intention to repeat volunteer); “ I will definitely encourage others to volunteer with this organization” (recruit others); “I am very committed to this organization” (commitment); and “I believe in the goals and values of this organization” (shared goals and values). Responses included, “strongly disagree”, “disagree”, “neutral”, “agree”, and “strongly agree”. The responses were scored 1 (strongly disagree) to 5 (strongly agree).
Analysis The means and standard deviations for the 26-motive items were calculated in aggregate and for each of the four volunteer contexts (sport continuous, sport episodic, nonsport continuous, and nonsport episodic). The 26 items were subjected to factor analysis with varimax rotations to reduce the number of motives used in the regression analyses. The outcomes (i.e., global satisfaction, intention to volunteer again, commitment to the organization, encouraging others, and belief in values of the organization) were then regressed on the factor scores for motives. The means and standard deviations for the satisfaction variables were calculated and examined. Correlation analyses were then used to determine whether satisfaction was associated with outcomes such as repeat volunteer intention and commitment to the organization. Finally, frequencies were used to evaluate features of each volunteer experience and to classify them as Attractive elements (i.e., Satisfiers), Must-Be elements (i.e., Dissatisfiers), One-Dimensional elements (i.e., can satisfy or dissatisfy depending on the level), or Indifferent elements (i.e., neither satisfy nor dissatisfy). Priorities were assigned based on Kano’s classifications in the following order: Must-Be, One-Dimensional, Attractive, and Indifferent. The pairings in Reverse Elements were relabeled and then recategorized. Elements categorized as Questionable retained that classification.
Results Benefits of Measuring Motivation One sample t tests (test value = 1.5, somewhat important) were conducted to determine which motives were important to volunteers. Bonferroni adjustment was used to determine significance (p < .002) while controlling for Type I error. In aggregate, volunteers reported that 23 of the 26 motives were at least somewhat important (p < .001). The three items that did not motivate their volunteering were provision of food, provision of a uniform, and prestige garnered as a result of volunteering
(see Table 2). When the same t tests were conducted for each of the four volunteer contexts, the general pattern holds (see Table 2). Given the loss of power due to the smaller sample sizes, one would expect fewer motives to be significant when the data are disaggregated. However, all groups report at least 22 of the 26 motives are at least somewhat important. Since so many motives were reported to be important, even with low statistical power, it would be difficult to prioritize some motives over others. Consequently, measures of volunteers’ motives alone may not meaningfully assist volunteer managers to design targeted campaigns to attract volunteers. Motives were expected to be related to four key outcomes (cf. Green & Chalip, 2004; Lammers, 1991): interest in repeat volunteering, likelihood of encouraging others to volunteer, commitment to the organization, and belief in the organization’s values and goals. The 26 motive items were factor analyzed to reduce the number of items and correspondingly increase the power of the regression analyses. Principal components analysis with varimax rotation was used. Six factors explaining 62.6% of variance were extracted (see Appendix A). The items represent the following factors: Experience, Supportive Culture, Clear Direction, Rewards, Convenience, and Contribution. Each outcome was regressed on the six factor scores for motives. Overall, the motives were poor predictors of key outcomes (see Table 3). When a Bonferroni adjustment is made (α = .05/20 tests = .003), results indicate that motives for volunteering were unable to predict overall satisfaction with the volunteer experience (Table 3). Interestingly, sport volunteers’ motives do not predict any outcomes that have been shown to be important for retention. Nonsport volunteers’ motives predicted the degree to which they believed in the goals and values of the organization, but not their commitment or intention to volunteer again. Further, no volunteer group’s motives predicted the likelihood of encouraging others to volunteer, a well-known method for recruiting new volunteers. In short, measuring motives does not seem to provide volunteer managers much useful information for enhancing recruitment or retention. Nor do motives provide us with insight for enhancing volunteer satisfaction.
Benefits of Measuring Satisfaction Although motives were not related to satisfaction (Table 3), all volunteers reported high levels of global satisfaction with the volunteer experience (4.26
