Segmenting motivation: an analysis of fantasy baseball motives and mediated sport consumption.

Dwyer, Brendan ; Shapiro, Stephen L. ; Drayer, Joris 等

Fantasy sport participation is one of the fastest growing and most immersive activities on the Internet. Research estimates participants spend an average of five to seven hours per week engaged in activities related to their fantasy team (Weiss, 2007). In addition, the average fantasy sport participant represents corporate America's most-coveted demographic with regard to consumption habits and discretionary income (Dwyer & Drayer, 2010; Fisher, 2008). As a result, professional leagues and teams, media companies, and corporate partners are aggressively looking for effective strategies to extend this online behavior to other forms of consumption. However, two challenges are associated with the fantasy sport market--its size ([approximately equal to] 30 million participants) and its demographic homogeneity (Fantasy Sport Trade Association, 2008b). Therefore, in order to extrapolate differences in this population to create more effective and streamlined marketing communications, there is a need for additional and creative means of market segmentation.

One way to gain insight into this market is to quantify why fantasy sport participants engage in the activity and understand how their consumption of the general sport product is related. In other words, exploring motivational theory and consumption habits related to fantasy sport will offer sport marketers and managers valuable information to more properly package products and services to meet the unique needs and wants of this lucrative population. Therefore, the aim of this study was to explore fantasy baseball motives and mediated sport consumption as a means of advanced, psychographic sport market segmentation.

Market Segmentation in Sport

Market segmentation is the process of dividing a large population into smaller, more uniform groups based on unique similarities. For marketers, segmentation is required in addition to product differentiation as a means to achieve successful marketing objectives (Smith, 1956). It is also a fundamental process for developing promotional strategies and understanding consumer demand and decision making (Haley, 1968). More recently, sport management researchers Fullerton and Dodge (1995) determined that an integration of the following areas is suggested in order to provide the best opportunity to identify unique segments of sport fans: consumer demographics, consumer psychographics, and product-related variables.

In the sport marketing literature, there is little research which has specifically focused on segmentation strategies. There are, however, many studies which have examined behavioral and attitudinal differences between smaller subgroups, often based on common demographic variables (Snipes & Ingram, 2007) such as gender (James & Ridinger, 2002), education level (Zhang, Pease, Hui, & Michaud, 1995), and season ticket status (Lee, Trail, & Anderson, 2009). These findings can certainly be used by marketers to specifically identify differences within their current target market or expand into new markets; however, none of these studies explicitly utilized an a priori market segmentation framework.

In 2002, Giulianotti created a taxonomy of European Football fans to better understand how fans identify with football in today's commoditized sport environment. Specifically, the author created four categories of spectator identities underpinned by two opposing attitudes and behaviors: loyal/non-loyal and local/market-based consumption. The author then examined the impact of commodification levels across the taxonomy of spectator identities and concluded that the trends of corporate-style commoditization and the massive growth of mediated consumption have diluted identification with the local team. This led to a new classification of spectators with unique orientations and sport drivers.

Within the context of fantasy sports, Dwyer and Drayer (2010) suggested that among fantasy football participants there existed several subgroups who consumed more or less of the NFL based on varying levels of interest in their fantasy team and their favorite NFL team. Ultimately, despite the lack of diversity in the demographic profile of fantasy participants, this population still contains significant segments with differing attitudinal and behavioral responses which can help sport marketers devise appropriate segmentation strategies. Heeding this suggestion and understanding the importance of assessing consumer demand, motivation theory was explored.

Consumer Motivation

Consumer motivation has often been considered one of the catalysts within the decision making process and therefore represents a core principle within the study of consumer behavior (Simon, 1959). Regarded as a key psychographic indicator, motivational theory refers to an activated state within a person that leads to goal-oriented behavior (Mowen & Minor, 1998). Sport management researchers have examined consumer motivation in two distinct ways. First, by quantitatively examining the factors that influence consumer demand of sporting events (attendance), researchers can learn about what factors appear to be motivating patrons to attend events. For example, Forrest and Simmons (2002) found games in which the outcome was less certain (i.e., the game was played between two similarly successful teams) generated higher levels of demand. In a later study, the same authors (2006) found that demand for televised, midweek matches was significantly lower. Several years later, Buraimo and Simmons (2009) determined that increases in population and population density were related to higher levels of demand. Finally, Leeds and Sakata (2011) found that games in domed stadiums or between teams in different leagues also generated higher levels of demand.

However, realizing that fans do not typically make decisions based on small differences in population density, television schedule, stadium type, and outcome uncertainty, researchers began exploring a survey-based approach to understanding what motivates people to support their favorite teams. In 1995, Wann was the first to empirically develop a scale to measure spectator motivations and come up with eight motivational subscales: eustress, self-esteem, escape, entertainment, economic, aesthetic, group affiliation, and family. Within the last ten years, several studies have attempted to further understand consumer motivations in various contexts in an effort to gain a more holistic view of the topic. The studies have examined motivational differences between genders (James & Ridinger, 2002), at the collegiate sport level between a variety of demographic characteristics (Snipes & Ingram, 2007), between individuals with varying emotional attachment to their team (Koo & Hardin, 2008), and between fans of different sports (Robinson & Trail, 2005).

However, all of the aforementioned studies on consumer motivation have focused on consumers' motivations to attend events or their motivations to be a fan of a particular team or sport. None of these studies examined motivations with respect to mediated consumption or an ancillary sport activity, such as fantasy sport. Only Trail and James (2001) examined the relationship between consumer motivation and media consumption. However, the importance of media consumption as an outcome was not the focal point of that study as the authors surveyed season ticket holders who are less engaged in watching games through a mediated source. Additionally, the study was published a decade ago when the prevalence of mediated sport was substantially lower than it is today.

Given the increase in the quantity and quality of mediated sport, the media-dominant sport consumer has become increasingly important. Indeed, Pritchard and Funk (2006) highlighted the importance of this understudied segment in a study of professional baseball fans. The authors found that the media-dominant consumers purchased more team-related merchandise, viewed more advertisements and promotional activities, and had an elevated level of involvement with the sport. The authors concluded that "trends of escalating consumption via media continue to indicate attendance is becoming less central to an organization's profitability" (Pritchard & Funk, 2006, p. 316).

A review of sport motivation literature shows that understanding fan motives is fundamental for implementing effective market segmentation and targeting strategies as well as conducting successful promotions and advertising campaigns. However, the segmentation of sport consumers by motives has yet to be conducted. As a result, the current study focused on sport consumer motives and product usage (media consumption) as a means to segment fantasy baseball participants.

Fantasy Sport and Fantasy Sport Participants

Fantasy sport participation is primarily an online activity that is completely customizable, interactive, and involves nearly every major professional sport from Major League Baseball (MLB) to bass fishing. Recently, the pastime has grown into a highly-popular activity for all types of sports fans. According to the Fantasy Sport Trade Association (FSTA), nearly 30 million people play fantasy sports within the United States and Canada (2008b). In addition, the FSTA estimates $800 million is spent annually directly on fantasy sport products and services while an additional $3.5 billion is spent on related media products and services.

The typical fantasy participant is male, between the ages of 18-45, with above average levels of income and education (Van Riper, 2008). According to the FSTA (2008b), the average fantasy participant has played for approximately 10 years, owns 6 teams, and spends around $500 annually on fantasy related products and services. Levy (2005) found that two-thirds of participants in his investigation spent five hours per week managing their fantasy teams, with one-third spending 10 or more hours. Fantasy participants also tend to watch more sports on television and spend more money attending sporting events (Drayer, Shapiro, Dwyer, Morse, & White, 2010; Nesbitt & King, 2010). In addition, a survey of participants conducted by Ipsos Public Affairs indicated that fantasy players are stronger consumers of the major product and service categories than the average sport fan and the general population, as a whole (Fisher, 2008). This information has substantial marketing benefits as participation in fantasy sports continues to grow and the typical participants are highly active consumers.

The FSTA (2008a) estimated that over 9 million individuals participated in fantasy baseball in 2007. These participants were more affluent than the average Internet user with 29% having a household income greater than $100,000 per year and were much younger than the average Internet user. In addition, fantasy baseball participants more closely resemble their fantasy football brethren than any other fantasy sport participants. That is, they were more likely to spend the same amount of time online engaged with the activity per week, they are more likely to follow their fantasy team across multiple formats (e.g., TV, Internet, cell phone, radio, & newspaper), and the activity has witnessed a somewhat similar growth rate over the last decade (FSTA, 2008a). Despite the similarities in demographics, involvement, and growth potential, fantasy baseball as an activity has received minimal attention from sport consumer behaviorists, as most of the research has been relegated to fantasy football. This study, however, attempted to develop a typology of fantasy baseball participants in order to package and deliver sport products more effectively to this untapped, yet emerging market.

In addition to the practical impact of the activity, fantasy sport participation also has the potential to influence several well-researched constructs within the sport consumer behavior literature. For instance, utilizing an adapted attitude-behavior framework, Drayer et al. (2010) determined that fantasy football participation activated additional attitudes and perceptions with regard to the National Football League (NFL) product that, combined with traditional sport fandom, resulted in additional mediated consumption of the NFL.

Previous fantasy sport motive research has also been limited to fantasy football users and has suggested that the activity is a site wherein participants seek to satisfy enhanced sport fandom desires (Dwyer & Kim, 2011; Farquhar & Meeds, 2007; Spinda & Harokids, 2008). While attempting to develop fantasy sport typology, Farquhar and Meeds (2007) identified a set of common underlying motives for fantasy football participation. Specifically, the authors uncovered the following five motivational dimensions using a Q-methodology: surveillance, arousal, entertainment, escape, and social interaction.

Spinda and Haridakis (2008) also sought to explore the motives of fantasy participants, and discovered the following motivational dimensions: ownership, achievement/self-esteem, escape/pass time, socialization, bragging rights, and amusement. The authors suggested that the activity of fantasy sports is "a purposive, instrumental, and active media-use endeavor" (p. 196). Lastly, Dwyer and Kim (2011) developed the three-dimensional Motivational Scale for Fantasy Football Participation (MSFFP) that included the motives of social interaction, entertainment/escape, and competition. Most importantly, the researchers were the first to explore a gambling motive, and while it was found to be a noteworthy motive for some participants with high eigenvalue scores and factor scores that were reliable and valid (convergent and discriminant), it resulted in poor predictive validity factor scores with respect to consumption, participation level, and competitiveness. That is, those with high scores on the gambling items also had lower levels of fantasy sport-related consumption, owned fewer teams, spent less time participating, and considered themselves less competitive. Thus, the factor was dropped from the final motivational scale, but the authors recommended its use in studies looking to investigate and segment fantasy participants based on gambling intentions.

In summary, the review of literature has highlighted the importance of advanced market segmentation strategies, understanding consumer motivational theory, and the need for additional information regarding both the fantasy baseball population and sport mediated consumption patterns. Thus, the purpose of this study was three-fold: (1) examine the possible motives of fantasy baseball participants through the adaptation of the MSFFP, (2) develop a motive-based taxonomy of fantasy baseball participants, and (3) explore possible differences in mediated sport consumption based on distinct fantasy baseball segments.

Methods

Sample and Instrumentation

The target population for this study was fantasy baseball participants over the age of 18 who currently participate in the activity. A sample of 1,500 potential respondents was randomly selected from a group of 3,400 FSTA fantasy members. The FSTA represents more than 125 member companies in the fantasy sport industry, and has an estimated five to seven million unique participants. Respondents were randomly selected for participation in this study and were emailed a link to the questionnaire. A total of 303 respondents began the survey with 47 discontinuing and three reporting an age under the study's requirement. The resulting sample size was 253 (16.9%). Sample demographics are available in Table 1.

Questions regarding fantasy baseball motivations were adapted from Dwyer and Kim's (2011) MSFFP. The 12 item scale consisted of the following three motives: social interaction, competition, and entertainment/escape. However, given historical wagering associations and severe implications of potential federal legislation, an additional sub-dimension (gambling) was added (Boswell, 2008). This factor has shown evidence of reliability in previous literature and was added to more completely segment the sample of fantasy sport participants (Dwyer & Kim, 2011). In all, a 16 item scale was utilized.

In addition, eight behavioral intention items asked respondents the likelihood of their consuming mediated professional baseball during a given day during the MLB season. The intentions used in this study included: the use of the Internet to follow one's (1) favorite MLB and one's (2) fantasy team, television viewership of one's (3) favorite MLB and (4) fantasy team, the use of a cell phone to follow one's (5) favorite MLB and (6) fantasy team, (7) reading a fantasy baseball article via the Internet, and lastly, (8) watching a baseball highlight show on television. Intentions to consume were measured on an eleven-point Juster scale where 0 represented no chance and 10 represented certain (Juster, 1966). The set of consumption items, which were selected by the researchers based on the review of literature and industry suggestions, provided a cross section of fantasy and favorite MLB team orientations as well as emerging and technologically interactive forms of professional sport consumption.

Data Analysis

Given the unique differences between fantasy baseball and fantasy football noted above and the additional gambling items, a principal component analysis (PCA) with promax rotation of the motivational items was conducted to explore the factor structure of the adapted MSFFP. The total number of dimensions was determined by the following criteria: the Kaiser Criterion, or eigenvalues greater than 1.0, factor loadings above .4, at least two items per factor, and ultimately, interpretability of the dimensions (Tabachnick & Fidell, 2007). In addition, descriptive statistics, reliability, and convergent validity analyses were interpreted.

The sample was then segmented based on the resulting factor motives for fantasy baseball participation. The Ward's cluster algorithm was used for this study in an exploratory hierarchical cluster analysis to assist in selecting the number of clusters (segments) for a subsequent K-means analysis. Cluster analysis is often used as a means for market segmentation when researchers do not know the number of groups in advance but wish to establish groups and then analyze group membership (Kaufman & Rousseeuw, 2005). Further, it is commonly used for attitudinal research that seeks to understand commonalities in opinion and distinct differences between groups of consumers (Kaufman & Rousseeuw, 2005).

Following the segmentation of the sample, the data were then analyzed using a MANOVA to ascertain whether statistically significant differences could be identified between the motive-based segments, based on the behavioral intentions in relation to each form of mediated sport consumption. A Pillai's Trace statistic was used to determine a main effects difference because a Box's M test showed a violation of homogeneity of variance/co-variance matrices. Pillai's Trace statistic is more conservative and robust to violations of equal variance (Tabachnick & Fidell, 2007). A significance level of .05 was set for the MANOVA procedures.

Two post hoc procedures were used once main effect differences were found. First, in order to identify which of the four motive-based segments significantly differed, a Tamhane's post-hoc procedure was conducted. A Tamhane's procedure was used because it is a more robust procedure that takes into account violations of equality of variance (Tabachnick & Fidell, 2007). Second, a descriptive discriminant analysis (DDA) was used as a post hoc procedure to identify which dependent variable was the primary source of segment separation (Duarte Silva & Stam, 1995). An analysis of the structure matrix in DDA provided specific information regarding which dependent variable correlated highest with the linear combination of dependent variables (Tabachnick & Fidell, 2007).

Results

The PCA identified a four factor solution with 15 items. One item was deleted due to loading and interpretation issues. The resulting model explained 63.9% of the variance, and the primary factor loadings from the pattern matrix for the 15 items ranged from .587 to .857. The factors identified were competition (4 items; eigenvalue = 3.788), social interaction (4 items; eigen-value = 2.300), gambling (4 items; eigenvalue = 1.755), and entertainment (3 items; eigenvalue = 1.280). Table 2 provides mean interitem correlations, Cronbach's alpha scores, and Average Variance Extracted scores for each dimension. Reliability and convergent validity were found to be satisfactory based on the current sample scores.

Seven possible cluster solutions were subsequently examined based on the exploratory hierarchical cluster analysis. A four-cluster solution was considered to be the most appropriate, after analyzing solutions ranging from two to eight clusters. Table 3 displays the segment names and final cluster centers. Segment names were created by the researchers after a thorough interpretation of the final cluster scores. This interpretation led to the identification of a new motive-based taxonomy of fantasy baseball participants which included the hedonist, the opportunist, the moderate, and the advocate. The distinct differences between each segment are discussed later.

Table 4 presents the MANOVA and Tamhane's post hoc results using the four segments as independent variables, and the media consumption intentions as the dependent variables. The Pillai's Trace F statistic was significant at 1.994 (p < .001) indicating that behavioral intention differences across the segments existed. In fact, four of the eight behavioral intentions demonstrated statistically significant (p < .05) differences across the four segments. Each significant behavioral intention variable was related to a participant's fantasy team. None of the favorite team variables evidenced a significant difference. Lastly, the DDA post hoc results suggested that the participant's likelihood of reading a fantasy baseball-related article on the Internet was the primary source of segment separation (Structure Matrix Coefficient = .720).

Discussion

The popularity and growth of fantasy sport is well-documented. However, the knowledge base surrounding the distinct attitudes and behaviors of fantasy participants is underdeveloped. Specifically, further information surrounding the motives and behavior of fantasy baseball players is needed. In addition, the psychographic segmentation of this large and lucrative sport fan population will help sport marketers, league administrators, and web service providers more effectively communicate with this population. Thus, the purpose of this study was to understand why fantasy baseball participants engage in the activity, develop a motive-based taxonomy of participants, and quantify the differences in consumptive outcomes based on this taxonomy.

The cluster analysis and MANOVA results suggest that the differing segments with distinct motives exist within the fantasy baseball population, and between each of these segments, media consumption intentions differed. With regard to the motive-based taxonomy, the results paralleled previous psychographic sport fan research (Giulianotti, 2002) and fantasy sport research (Dwyer & Drayer, 2010; Farquhar & Meeds, 2007) that explored attitudinal, behavioral, and social situational differences across varying levels of sport fandom. Similarly to Giulianotti's (2002) investigation of local team identifications and commodification, the abundance of media consumption opportunities currently available to the contemporary sport fan appears to serve as further means to differentiate between groups of fans.

For instance, with a high entertainment motive score and sub-par scores for the other motives, the first cluster (n = 53), termed the Hedonist, best characterized participants that seek the pleasurable attributes of fantasy baseball, specifically as a means to getting more entertainment value out of the MLB product. However, this group's consumption intentions were for the most part moderate. The most obvious difference of the second segment, the Opportunist (n = 70), was the inflated gambling motive score. While still considered to be a below-average motive at best, this cluster indicated a stronger attachment to the possible financial benefits of participating in fantasy baseball. The behavioral intention scores for this group, however, were above average.

The third cluster, the Moderate (n = 46), indicated the most below average mean scores for each motive and appeared to be less engaged with the activity as most of the statistically significant differences between the groups stemmed from this group's lower behavioral intention mean scores. Lastly, the Advocate (n = 84) represented the most highly active group of fantasy baseball participants as each motive score was elevated, as were the behavioral intention mean scores. Based on these results, it is likely this group will evangelize the positive elements of the activity and consume all forms of professional baseball across various media (TV, Internet, cell phone).

With respect to understanding the motives of fantasy baseball participants, the PCA results clearly identified a four factor, 15 item structure of fantasy baseball motives. This structure differed slightly from Dwyer and Kim's (2011) investigation with fantasy football participants. The item deleted from the model accounted for the escape portion of the Dwyer and Kim's entertainment/escape factor. The elimination, while different, is logical when one considers the participatory differences between the two activities. With six months of daily fantasy baseball activity as opposed to four months of weekly fantasy football competition, it is reasonable to understand why the escape qualities would be different. Fantasy baseball requires much more attention to detail, as games occur six, sometimes seven, days per week, and thus, the routine could be considered similar to a work pattern as opposed to diversion.

With that said, the scale scores for the model provided evidence of reliability and convergently validity. In addition, the segment that scored on average the highest and lowest for each MSFFP motive, the Advocate and Moderate respectively, also indicated corresponding scores for each form of mediated fantasy baseball consumption. This provides preliminary confirmation of the MSFFP scores, as those with the highest level of motivation to participate in fantasy baseball intended to consume the most of their fantasy baseball team and vice versa. Therefore, it appears that the underlying motivational dimensions of fantasy baseball are fairly similar to those of fantasy football. However, to more completely understand the phenomenon of fantasy baseball participation, it is the researchers' opinion that a fantasy baseball-specific motivational scale should be developed and validated.

Once again, similarly, to Dwyer and Kim's (2011) findings, the gambling dimension resulted in satisfactory reliability and convergent validity. However, the total sample mean was low (2.669) on a seven point scale and the segment indicating the highest consumption levels (Advocates) had a gambling mean score of 2.330. Thus, it appears that participation driven by the opportunity to make money is not a predictively valid motivation for this cross-section of fantasy baseball participants. With that said, the segment with the highest gambling mean score (Opportunists) also indicated relatively high consumption intention scores of their fantasy baseball team--not the highest, but also not the lowest. Therefore, the gambling factor for some participants appears to result in elevated fantasy baseball consumption intentions. This contradicts Dwyer and Kim's findings and may speak to differences between fantasy baseball and football. However, the gambling mean score for the Opportunists was only slightly above average (4.171). In all, further research is required with regard to gambling intentions and fantasy sport.

Interestingly, the behavioral intention differences between the segments were limited to fantasy team-oriented activities across all media formats. For team and league managers, this may signify that fantasy baseball participation does not inhibit nor enhance traditional MLB fandom, and should be viewed as a complimentary means of consuming the MLB team product as opposed to a competitive means. This parallels recent Australian Rules Football research by Karg and McDonald (in press), but further research in this area is advised.

Intentions to consume via differing media platforms were also obvious as Internet and television scores were much higher than cell phone scores. The Advocate group, however, appeared to utilize the cell phone for fantasy purposes at a much higher rate than the other segments. In terms of product adoption, this may indicate that fantasy-related cell phone applications are still lagging, and an opportunity for fantasy sport providers to capitalize on a potential growth market may exist. Lastly, the likelihood to read a fantasy baseball-related Internet article was the dependent variable most responsible for group separation. Internet articles are the standard source for fantasy news and analysis and a popular vehicle for participants looking to gain an edge over their competitors. This DDA result may suggest that the search for additional advantages via Internet articles is perhaps strongly related to one's motives for participating.

In summary, the present study provides support for the value of the psychographic analysis of sport fans, notably fantasy baseball players, with regard to consumer motivation as a means by which to identify differences in the mediated consumption of fantasy and favorite team products across multiple media formats. The identification of this new taxonomy of fantasy baseball participants should allow marketers, managers, and media professionals to design better oriented and more effective marketing strategies. Additionally, the results provide insight into the multifaceted decision making process of fantasy sport participants, further indicating the distinctive psychology of participation and the elevated consumption potential of users (Drayer et al., 2010; Dwyer, in press; Dwyer & Drayer, 2010).

Limitations of the current study certainly exist. First, this sample only represented a cross-section of the fantasy baseball participants, and it would be beneficial to compare and contrast fantasy and non-fantasy playing MLB fans with respect to general MLB fandom. Second, research suggests that it is often viewed more positively to be a fervent supporter of one's favorite team than a casual fan (Branscombe & Wann, 1991). Therefore, the results with regard to favorite MLB behavioral intentions may be elevated due to social desirability. Lastly, the MSFFP was developed specifically for fantasy football using three fantasy football-only samples. While the current study's scale scores provide evidence of both reliability and validity with respect to fantasy baseball participation, the findings should be slightly tempered.

As for future research, alternative psychographic measures including utility, hedonism, lifestyle, compulsiveness, and risk are worthy of further inquiry as the activity of fantasy baseball has the potential to pull baseball fans in several directions. In addition, the element of sport wagering will continue to be a divisive topic among participants, service providers, league administrators, and legislators, so additional consumer inquiry into the gambling component is necessary. Researchers are also encouraged to apply this motive-based taxonomy to larger, more aggregate samples and to differing fantasy sport activities such as football, basketball, hockey, golf, and NASCAR. Furthermore, as the number and forms of sport media outlets continue to grow, further investigation into the inputs and outcomes of sport media consumption is advised.

References

Boswell, J. (2008). Fantasy sports: A game of skill that is implicitly legal under state law, and now explicitly legal under federal law. Cardoza Arts & Entertainment Law Journal, 25, 1257-1277.

Branscombe, N. R., & Wann, D. L. (1991). The positive social and self concept consequences of sports team identification. Journal of Sport and Social Issues, 15, 115-127.

Buraimo, B., & Simmons, R. (2009). Market size and attendance in English Premier League football. International Journal of Sport Management and Marketing, 6, 200-214.

Drayer, J., Shapiro, S., Dwyer, B., Morse, A., & White, J. (2010). The effects of fantasy football participation on NFL consumption: A qualitative analysis. Sport Management Review, 13, 129-141.

Duarte Silva, A. P., & Stam, A. (1995). Discriminant analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 277-318). Washington, DC: American Psychological Association.

Dwyer, B. (in press). Divided loyalty? An analysis of fantasy football involvement and attitudinal loyalty to individual National Football League (NFL) teams. Journal of Sport Management, 25(5).

Dwyer, B., & Drayer, J. (2010). Fantasy sport consumer segmentation: An investigation into the differing consumption modes of fantasy football participants. Sport Marketing Quarterly, 19, 207-216.

Dwyer, B., & Kim, Y. (2011). For love or money: Developing and validating a motivational scale for fantasy football participation. Journal of Sport Management, 25(1).

Fantasy Sports Trade Association. (2008a, February 11). Fantasy sport consumer spending: Fantasy sports products and services. Proceedings from the 2008 FSTA Winter Business Conference, Denver, CO.

Fantasy Sports Trade Association. (2008b, July 7). Fantasy sports industry grows to a $800 million industry with 29.9 million players. Retrieved from http://www.fsta.org/

Farquhar, L. K., & Meeds, R. (2007). Types of fantasy sports users and their motivations. Journal of Computer-Mediated Communication, 12, 1208-1228.

Fisher, E. (2008, November 17). Study: Fantasy players spend big. Street & Smith's SportsBusiness Journal, 11, 1-2.

Forrest, D., & Simmons, R. (2002). Outcome uncertainty and attendance demand in sport: The case of English soccer. The Statistician, 51, 229-241.

Forrest, D., & Simmons, R. (2006). New issues in attendance demand: The case of the English Football League. Journal of Sports Economics, 7, 247-266.

Fullerton, S., & Dodge, R. H. (1995). An application of market segmentation in a sports marketing arena: We all can't be Greg Norman. Sport Marketing Quarterly, 4, 43-61.

Giulianotti, R. (2002). Supporters, followers, fans, and flaneurs. Journal of Sport & Social Issues, 26, 25-46.

Haley, R. I. (1968). Benefit segmentation: A decision-oriented research tool. Journal of Marketing, 32, 30-35.

James, J. D., & Ridinger, L. L. (2002). Female and male sport fans: A comparison of sport consumption motives. Journal of Sport Behavior, 25, 260-278.

Juster, F. T. (1966). Consumer buying intentions and purchase probability. Journal of the American Statistical Association, 61, 658-696.

Karg, A. J., & McDonald, H. (in press). Fantasy sport participation as a complement to traditional sport consumption. Sport Management Review.

Kaufman, L., & Rousseeuw P. J. (2005). Finding groups in data: An introduction to cluster analysis. NY: Wiley-Interscience.

Koo, G., & Hardin, R. (2008). Difference in interrelationship between spectators' motives and behavioral intentions based on emotional attachment. Sport Marketing Quarterly, 17, 30-43.

Lee, D., Trail, G. T., & Anderson, D. F. (2009). Differences in motives and points of attachment by season ticket status: A case study of ACHA. International Journal of Sport Management and Marketing, 5, 132-150.

Leeds, M. A., & Sakata, S. (2011). Take me out to the yakyushiai: Determinants of attendance at Nippon professional baseball games. Journal of Sports Economics. Advance online publication.

Levy, D. (2005). Sports fanship habitus: An investigation of the active consumption of sport, its effects and social implications through the lives of fantasy sport enthusiasts (Unpublished doctoral dissertation). University of Connecticut: Storrs, CT.

Mowen, J. C., & Minor, M. (1998). Consumer behavior (5th ed.). London: Prentice-Hall.

Nesbit, T. M., & King, K. A. (2009). The impact of fantasy sports on television viewership. Journal of Media Economics, 23, 24-41.

Pritchard, M. P., & Funk, D. C. (2006). Symbiosis and substitution in spectator sport. Journal of Sport Management, 20, 299-321.

Robinson, M. J., & Trail, G. T. (2005). Relationship among spectator gender, motives, points of attachment, and sport preference. Journal of Sport Management, 19, 58-80.

Simon, H. A. (1959). Theories of decision-making in economics and behavioral science. The American Economic Review, 49, 253-283.

Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21, 3-8.

Snipes, R. L., & Ingram, R. (2007). Motivators of collegiate sport attendance: A comparison across demographic groups. Innovative Marketing, 3, 65-74.

Spinda, J. S. W., & Haridakis, P. M. (2008). Exploring the motives of fantasy sports: A uses-and-gratifications approach. In L. W. Hugenberg, P. M. Haridakis, and A.C. Earnheardt (Eds.), Sports mania: Essays on fandom and the media in the 21st century (pp. 187-202).

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education.

Trail, G. T., & James J. D. (2001). The motivation scale for sport consumption: Assessment of the scale's psychometric properties. Journal of Sport Behavior, 24, 108-127.

Van Riper, T. (December 1, 2008). Slow growth for fantasy sports. Forbes. Retrieved from http://www.forbes.com/

Wann, D. (1995). Preliminary validation of the sport fan motivation scale. Journal of Sport and Social Issues, 19, 377-396.

Weiss, T. R. (2007, August 24). Add fantasy football to list of top online time-wasters. Computerworld. Retrieved from http://www.pcworld.com/article/136374/

Zhang, J. J., Pease, D. G., Hui, S. C., & Michaud, T. J. (1995). Variables affecting the spectator decision to attend NBA games. Sport Marketing Quarterly, 4, 29-39.

Brendan Dwyer, PhD, is the director of research and distance learning for the Center for Sport Leadership at Virginia Commonwealth University. His research interests include sport consumer behavior with a distinct focus on the media consumption habits of fantasy sport participants and issues pertaining to intercollegiate athletics.

Stephen L. Shapiro, PhD, is an assistant professor of sport management at Old Dominion University. His research focuses on financial management in college athletics, ticket pricing in college and professional sport, and consumer behavior. Joris Drayer, PhD, is an assistant professor of sport and recreation management at Temple University. His research interests include ticketing and pricing strategies in both the primary and secondary ticket markets, as well as consumer behavior, particularly in relation to fantasy sport participation.

Table 1.
Sample Demographics (n = 253)

Age
Mean 31.847
Median 30
St. Dev. 11.009
Range 18-69

Ethnicity
Asian/Pacific Islander 3.6%
Black/African American 1.2%
Caucasian/White 88.9%
Hispanic 2.4%
Other 3.9%

Gender
Male 97.2%
Female 2.8%

Household Income
Less than $25,000 11.6%
$25,000-$49,999 19.2%
$50,000-$74,999 22.8%
$75,000-$99,999 11.6%
$100,000-$124,999 9.6%
$125,000 or more 12.4%
Would rather not say 12.8%

Relationship Status
Married 41.9%
Single 50.2%
Other 7.9%

Education
High School 19.4%
Associates 13.1%
Bachelors 46.4%
Masters 13.1%
Doctoral 4.8%
Other 3.2%

Table 2.
Reliability and convergent validity testing for PCA

Factor Mean interitem Cronbach's Average Variance
 correlation alpha Extracted (AVE)

Social Interaction 0.469 0.778 0.554
Gambling 0.421 0.748 0.554
Competition 0.484 0.801 0.608
Entertainment 0.416 0.689 0.542

Table 3.
Final Cluster Centers

Cluster Number 1 2 3
Cluster label and profile Hedonist Opportunist Moderate

Social Interaction 3.561 4.900 4.516
Gambling 1.481 4.171 2.370
Competition 4.618 5.189 4.560
Entertainment 6.069 6.038 4.855
Percentage of the sample 20.9% 27.7% 18.2%

Cluster Number 4 Mean
Cluster label and profile Advocate

Social Interaction 5.747 4.831
Gambling 2.330 2.669
Competition 5.940 5.205
Entertainment 6.528 5.992
Percentage of the sample 33.2%

Note: Seven point Likert-type scale was used with 1 representing
Strongly Disagree and 7 representing Strongly Agree.

Table 4.
Mean Scores of the Behavioral Intentions
of Mediated Sport Consumption by Segment

Behavioral Intention Hedonist Opportunist

Fantasy Team--Internet * 9.113 (d) 9.586 (cd)
Fantasy Team--Television * 6.321 6.557
Fantasy Team--Cell Phone * 3.245 (d) 3.500 (d)
Favorite MLB Team--Television 7.396 7.529
Favorite MLB Team--Internet 7.302 7.200
Favorite MLB Team--Cell Phone 3.358 3.471
Fantasy-related Article--Internet * 7.528 (d) 7.686 (d)
Highlight Show--Television 6.623 7.271

Behavioral Intention Moderate Advocate

Fantasy Team--Internet * 8.804 (d) 9.929 (abc)
Fantasy Team--Television * 5.457 (d) 7.631 (c)
Fantasy Team--Cell Phone * 3.022 (d) 4.702 (abc)
Favorite MLB Team--Television 7.370 7.524
Favorite MLB Team--Internet 7.239 7.988
Favorite MLB Team--Cell Phone 3.370 4.524
Fantasy-related Article--Internet * 6.457 (d) 8.738 (abc)
Highlight Show--Television 6.935 7.714

Note: * Differences significant across behavioral intentions (p <
.05) using MANOVA. Eleven point Juster scale was used with 0
representing No Chance and 10 representing Certain.

(1) This dependent variable was the primary source of segment
separation according to the DDA results.

Post hoc Tamhane's procedure: a different (p < .05) from Hedonist
group mean; b different (p < .05) from Opportunist group mean; c
different (p < .05) from Moderate group mean; d different (p <
.05) from Advocate group mean.