Testing models of motives and points of attachment among spectators in college football.
|Abstract:||As the spectator sport market has become large and competition for consumers has increased, the need for understanding spectators' motives and points of attachment has become important for developing effective marketing strategies. The purpose of the study was to examine four different models that explain the relationships among motives and points of attachment and determine a model that explains the most variance in the referent variables. A total of 501 college students responded to the Motivation Scale for Sport Consumption (MSSC) and the Points of Attachment Index (PAI). The results showed that motives can be divided into fan motives and spectator motives, and these motives were related to different sets of points of attachment: organizational identification and sport identification.|
Consumer behavior (Models)
Attachment behavior (Analysis)
Trail, Galen T.
Kwon, Hyungil Harry
|Publication:||Name: Sport Marketing Quarterly Publisher: Fitness Information Technology Inc. Audience: Academic Format: Magazine/Journal Subject: Sports and fitness Copyright: COPYRIGHT 2009 Fitness Information Technology Inc. ISSN: 1061-6934|
|Issue:||Date: March, 2009 Source Volume: 18 Source Issue: 1|
|Topic:||Canadian Subject Form: Consumer behaviour|
|Product:||Product Code: 9914412 Consumer Behavior|
|Geographic:||Geographic Scope: United States Geographic Code: 1USA United States|
Testing Models of Motives and Points of Attachment among Spectators
in College Football
Sport spectating is a popular leisure activity in the United States. In fact, attendance and revenues for spectator sports have steadily increased for the past decade. According to the U.S. Census Bureau (2008), in 2005, it was estimated that revenue from spectator sports was approximately $24 billion, which is 3.5% higher than the previous year and 26.6% more than in 2000 (U.S. Census Bureau, 2008). It was also estimated that people spent approximately $15.9 billion in 2005 for admission to professional and amateur athletic events. Attending college football games is certainly a substantial part of spectating at amateur athletic events activity as it was estimated that over 48 million people attended college football games in 2007 (NCAA, 2008). This is a 30% increase from 1990 and a 10% increase compared with 2005.
As the spectator sport market has become large and competition for consumers has increased, sport marketers' interests in searching for more effective marketing strategies to increase attendance have also increased. In general, dividing the market into different segmentations and applying different strategies for each segment based on their characteristics and needs is crucial in order to come up with effective marketing plans. Traditionally, researchers have examined different demographic factors, such as gender, age, education level, and household income to determine whether segmenting the market based on these characteristics is beneficial (e.g., Dietz-Uhler, Harrick, End, & Jacquemotte, 2002; Fink, Trail, & Anderson, 2002b; Kahle, Kambara, & Rose, 1996; Robinson, Trail, & Kwon, 2004; Swanson, Gwinner, Larson, & Janda, 2003; Zhang et al., 2001). However, recently, researchers have studied other factors, such as social values (Kahle, Duncan, Vassilis, & Aiken, 2001), motivations (Kahle et al., 1996; Swanson et al., 2003), and brand associations (Ross, 2007) as ways to segment markets.
Yet, the areas of spectators' motives and points of attachment need more attention as potential variables with which to segment consumers. Some researchers have investigated various motives as important factors that influence people's decisions to spectate at sports events (Trail, Anderson, & Fink, 2000; Trail, Fink, & Anderson, 2003; Trail & James, 2001; Wann, 1996; Won & Kitamura, 2007). Other researchers have suggested that there may be other factors that influence people's decisions concerning sport consumer behaviors. Kwon, Trail, and Anderson (2005) suggested that some people watch sports events because they have a strong social-psychological connection with a team, coach, player, university, community, level of sport, and/or type of sport. They call these connections points of attachment. As the concepts of motives and points of attachment have become popular, researchers have been examining relationships among motives and points of attachment (Fink, Trail, & Anderson, 2002a; Robinson & Trail, 2005; Robinson, Trail, & Kwon, 2004; Trail, Robinson, Dick, & Gillentine, 2003). These researchers claimed that certain spectators' motives may lead to strengthening certain points of attachment.
However, studies on the relationships among different motives and points of attachment are still limited. Although many researchers use different motives when examining spectators' consumption behaviors, most researchers have only focused on team identification when discussing points of attachment (e.g., James & Ross, 2002; Trail & James, 2001; Fink et al., 2002a). Also, research that deals with multiple motives and multiple points of attachments has shown conflicting results. Therefore, it appears important to extend previous research that examines the relationships among motives and points of attachment.
In addition, with exception of Trail, Robinson, et al.'s (2003) study, most studies do not make distinctions between spectators and fans. This distinction may be valuable when studying sport consumer behavior. According to Sloan (1989), two different types of sport spectators may exist in a game: observers and fans. Those who attend the games as observers do not have a strong social-psychological attachment to team entities, such as the team, the player(s), or the coach, while fans are more likely to be highly attached to one or more of these potential connections. For our purposes, observers will be referred to as spectators. According to Trail, Robinson, et al. (2003), spectators and fans may have different motives, and these motives may lead to different points of attachment. Thus, the purpose of the current study was to extend
Trail, Robinson, et al.'s (2003) study on the relationships among motives and points of attachment while making a distinction between fan and spectator motives in the context of college football. We propose four models that examine possible relationships among the variables in order to find a model that explains the most variance. A better understanding of why people attend sports events can greatly help sport marketers develop more effective marketing strategies that can enhance spectators' and fans' level of satisfaction with the sport consumer experience. As a result, sport marketers would be able to increase game attendance, media consumption, and merchandise consumption as suggested in the previous literature (e.g., Andrew & Todd, 2007; Fisher & Wakefield, 1998; Swanson et al., 2003; Trail & James, 2001). For example, Fisher and Wakefield (1998) discovered that group identification, which was motivated by domain involvement, perceived group performance, and group member attractiveness, was a significant predictor of attendance and the purchase of licensed products. In addition, Swanson et al. demonstrated that team identification, eustress, group affiliation, and self-esteem enhancement were closely related to game attendance.
Understanding sport consumer behavior such as event attendance is important for the financial success of sport. According to Trail et al. (2000), at least nine different motives may exist among sport spectators. These individual motives include vicarious achievement, acquisition of knowledge, aesthetic qualities of the game/sport, social interaction, drama, escape, family, physical attractiveness, and appreciation of physical skills. Previous studies have shown that these motives are highly correlated with each other indicating that they can be grouped into one category representing the same factor 'motive' (Robinson & Trail, 2005; Trail, Fink, et al., 2003; Trail & James, 2001).
Other researchers suggested that these motives should be categorized into different groups (Robinson & Trail, 2005; Trail, Robinson, et al., 2003). Trail, Robinson, et al. (2003) grouped motives based on whether an individual is a fan or a spectator as suggested by Sloan (1989). They suggested that the reasons fans attend games and the reasons spectators attend games may be quite different, although some of the motives are applicable to both fans and spectators. They categorized vicarious achievement as a motive for fans of successful teams, and physical skill, aesthetic, drama, and knowledge as motives for fans of unsuccessful teams and for spectators. In addition, social and escape motives were classified as overarching motives that could be applied to both groups.
Points of Attachments
Although motives and points of attachment are closely related constructs, researchers have made a clear distinction between these two constructs. Motives are thought to be related to basic human "needs" (McDonald, Milne, & Hong, 2002; Robinson & Trail, 2005), whereas points of attachment reflect a "psychological connection" toward a certain entity (Kwon & Armstrong, 2004).
Traditionally, many studies have solely focused on team identification when dealing with social psychological connection rather than including other points of attachments (Wann, 1996; Wann, Dolan, McGeorge, & Allison, 1994; Wann & Robinson, 2002). However, many researchers now suggest that multiple points of attachment, rather than a unidimensional attachment-to-the-team, may exist (Funk, Mahony, Nakazawa, & Hirakawa, 2001; Kwon, Trail, & Anderson, 2005, 2006; Murrell & Dietz, 1992; Robinson et al., 2005; Robinson & Trail, 2005; Trail, Robinson, et al., 2003). For example, Murrell and Dietz's (1992) research suggested that identification with the university as an important point of attachment while Funk et al. (2001) suggested that fans' attachment to the sport and players may also be of importance. Recognizing the possible existence of different points of attachment, Robinson and Trail (2005) proposed that an individual could be attached to a player, coach, community, university, level of sport, or sport itself, in addition to the team.
[FIGURE 1 OMITTED]
Relationships between Motives and Points of Attachment
Whereas much research has frequently examined the relationships between different motives and team identification (Fink et al., 2002a; Fisher & Wakefield, 1998; Trail & James, 2001; Trail, Fink, et al., 2003; Wann, Royalty, & Rochelle, 2002), several studies have examined the relationships among multiple motives and multiple points of attachment (Funk, Mahony, & Ridinger, 2002; Robinson & Trail, 2005; Robinson, Trail, & Kwon, 2004; Trail, Robinson, et al., 2003). Some of these results suggest that certain motives may be good indicators of specific points of attachment. For example, Funk, et al. (2002) showed that, in a women's soccer game, aesthetics (r = .75) and excitement (r = .78) motives were associated with attachment to the sport of soccer, while social interaction was correlated with attachment to the player (r = .29). Trail, Robinson, et al. (2003) showed that vicarious achievement, a fan motive, was related to identification with team, player, university, community, and coach while spectator motives (skill, aesthetics, knowledge, and drama) were associated with attachment to the type of sport and to the level of sport.
As mentioned earlier, the primary purpose of the current study was to extend Trail, Robinson, et al.'s (2003) study. Two models that showed an equal model fit (RMSEA = .061, CI = .059 - .064, ECVI = 4.19) in the original study are also examined in the current study. In addition, two alternative models are included. Further, these models will be examined based on whether an individual is categorized as a fan or a spectator. The four models included in this study are explained in the following section.
Model A (Figure 1) is almost identical with Model C in Trail, Robinson, et al.'s (2003) original study. Three different sets of spectator motives are included in the model: vicarious achievement, spectator motives, and overarching motives. Vicarious achievement is considered to be a motive for fans, whereas spectator motives refer to motives for spectators, and include the motives of skill, aesthetics, drama, and knowledge. Additionally, the second order latent variable labeled Overarching Motives, which includes escape and social interaction, refers to motives relevant to both fans and spectators.
Research has shown that vicarious achievement is a motivator for fans (End, Dietz-Uhler, Harrick, & Jacquemotte, 2002; Fisher & Wakefield, 1998; Trail, Robinson, et al., 2003). End et al. (2002) discovered that people tended to identify more with successful teams, suggesting that fans who are highly identified value winning more because their motivation to attend the game is to satisfy the need for achievement through the association with a successful other. The tendency to want to associate with successful others is called basking-in-reflected-glory (BIRGing; Wann, & Branscombe, 1990). Fisher and Wakefield (1998) showed that perceived success of the team was only important for fans of successful teams for developing a group identity. For fans of unsuccessful teams, perceived success of the team was less important because these fans tended to focus on other motives, such as an athlete's skills, aesthetic aspects of the performance, drama of the games, and obtaining knowledge about the sport or the skills. In other words, fans of successful teams appear to be motivated to associate themselves with successful others, so they may feel vicarious achievement when a team, player, or coach is successful. However, highly identified fans of unsuccessful teams may not be as motivated by vicarious achievement because their team does not win as much. Instead, their motives might be more similar to motives of spectators.
In contrast, escape and social motives may be applied to both fans and spectators. According to Trail, Robinson, et al. (2003) a fan can escape from daily life and go to the game to feel vicariously successful, whereas a spectator can escape from daily life and go to the game to appreciate the aesthetic qualities of the play. The fan might be motivated by the social interaction experienced with other fans when their team wins by BIRGing together, whereas a spectator may enjoy the social interaction experienced with other people at the game through sharing their knowledge together.
In addition, in this model, it was hypothesized that there is an intercorrelation among vicarious achievement, spectator motives, and overarching motives. Previous studies have shown that motives are correlated to each other although some motives are more closely correlated with each other than the others (Robinson & Trail, 2005; Trail & James, 2001; Wann; 1995; Won & Kitamura, 2007).
Points of attachment were classified into two dimensions: organizational identification and sport identification as suggested by Trail, Robinson, et al. (2003). Organizational identification included identification with the team, coach, university, and player whereas sport identification included level of sport, and sport. Different from Trail, Robinson, et al.'s original study, identification with the community was not included because the community where the study was conducted was considered a university town. Therefore, it was expected that the identification with university and the identification with community would be highly correlated. In fact, the previous studies with students at this university showed a very high correlation between these two constructs. Thus, the identification with community construct was eliminated in the current study.
As indicated in Trail, Robinson, et al.'s (2003) study, it was speculated that a fan motive of vicarious achievement leads to organizational identification, and spectator motives lead to sport identification. Individuals who are motivated by vicarious achievement need a social-psychological connection to an entity which will allow them to identify with successful others. These points of attachment could be a team, player, coach, or a university. Studies suggest that vicarious achievement is highly associated with team identification (Fink et al., 2002a; Robinson & Trail, 2005; Wann, 1995), player identification (Robinson et al., 2005), and university identification (Wann & Robinson, 2002). Furthermore, Trail, Robinson, et al. (2003) found a relationship ([beta] = .853) between vicarious achievement and organizational identification. On the other hand, if spectators do not care about the wins and losses of the team because they are not identified with the organizational entities, their points of attachment might be the sport itself or level of sport. Funk, Mahony, and Ridinger's (2002) research provided some support for this notion. In their study, motives of drama (r = .46) and aesthetics were highly related (r = .75) to interest in soccer. Also, Trail, Robinson, et al. found a path coefficient of .711 between spectator motives and sport attachment.
[FIGURE 2 OMITTED]
Model B (Figure 2) also comes from Trail, Robinson, et al.'s (2003) study. It is almost identical to Model A in the present study, with one exception. Instead of assuming no relationship exists between organizational identification and sport identification, we hypothesize that there are some instances in which organizational identification will lead to sport identification, and there are some cases in which sport identification will lead to organizational identification (i.e., a reciprocal relationship). For example, if an individual is highly identified with college football, it might be impossible not to have a favorite team or player. Similarly, one who is highly identified with a specific team cannot just like the team without liking the sport itself. Therefore, a reciprocal relationship may exist between organizational identification and sport identification.
[FIGURE 3 OMITTED]
Model C (Figure 3) is quite similar to Model A, but the difference exists in overarching motives and fan motives. Model C depicts a second order latent variable labeled Fan Motives consisting of vicarious achievement and social interaction. The notion is that the social interaction motive would be stronger in highly identified fans than mere spectators. In other words, fans may socially interact with each other more than spectators because they consider themselves as an "in-group" which has a clear shared goal (winning) and show favoritism towards their in-group members (Branscombe & Wann, 1994). For example, when their team wins, the fans who cheer for the same team may feel a social bond because their goal was the same (i.e., their team winning), and it was achieved. In a game between rivals, we see this phenomenon more frequently. During and after the game, strangers may interact more freely because they share the same feelings and have the same sense of belonging (Anderson & Stone, 1981; Stone, 1981). After a win, they may be engaged in BIRGing behavior together, or after a loss they may be engaged in BLASTing behavior (blaming external sources for the loss) together (Bernache-Assollant, Lacassagne, & Braddock, 2007; Branscombe & Wann, 1994; Snyder, Lassegard, & Ford, 1986; Wann & Branscombe, 1990). This activity may be an important part of their spectating experience. Although no studies have proposed this categorization previously, indirect support exists. For example, Mahony, Howard, and Madrigal (2000) argued that self-esteem responses have social implications.
The only difference between Model C and Model D (Figure 4) is the reciprocal paths between organizational identification and sport identification. The reason for the inclusion of these paths is explained in the section on Model B.
Sample and Procedure
The participants were a convenience sample of 501 college students who were enrolled in the Department of Health and Human Performance at a large Midwestern university, whose football team competed in the Big 12 conference. The student population of the university was approximately 26,000, and the average attendance at the home football game at the year the data were collected was estimated to be 56,362. A wide range of classes from introductory classes to advanced classes were included in the study. The survey was distributed to the students in class at the beginning of the fall semester, before games for that collegiate football season had started. Football, as a sport, was chosen because the researchers anticipated that responses to questions about the university's football team would elicit a more normal distribution than any other sport. Among the participants, 46.7% (n = 234) were male and 53.1% (n = 266) were female. In addition, 96% (n = 481) were single and 3.4% (n = 17) were married. The majority of the participants were Caucasian (92.8%, n = 462). The age of the participants ranged from 17 to 49, but 89% (n = 445) fell in between the age of 18 and 22 years.
[FIGURE 4 OMITTED]
Two scales were used in this study: the Motivation Scale for Sport Consumption (MSSC) and the Points of Attachment Index (PAI). The MSSC as originally developed (Trail & James, 2001) had nine motives, but the current study only included seven: vicarious achievement, escape, social interaction, appreciation of physical skills of the athletes, aesthetics, drama, and knowledge. The motive of physical attraction was removed due to the request of the athletic department where the survey was conducted because they did not want to acknowledge that people might be motivated to attend games because of the attractiveness of the athletes. The motive of family was not included because studies have shown that family may not be a motive (Fink et al., 2002a; Robinson & Trail, 2005) having low correlations with other motives. In addition, it may not be applicable to college students because this dimension focuses on the ability to spend time with family and/or spouse. Each motive had three items, except the motive of drama (four items), for a total of 22 items. Previous studies have shown that the MSSC possessed good construct reliability, discriminant validity, criterion validity, and internal consistency (Fink et al., 2002a; Robinson & Trail, 2005; Robinson et al., 2004; Trail & James, 2001). For example, Robinson et al.'s (2004) study showed good construct reliability (AVE .67-.79) and good internal consistency ([alpha] = .86-.92) for the seven motives, and Robinson and Trail (2005) also showed good construct reliability (AVE .51-.76) and good internal consistency ([alpha] .75-.90) for the seven motives. In addition, Trail and James (2001) demonstrated that the MSSC showed good discriminant validity (the AVE values for each construct were all greater than squared correlation) and criterion validity (the motives were significantly correlated with team identification, general fanship, and attendance, which were used as criterion variables).
The Points of Attachment Index (PAI) was used to measure connection to various aspects relating to the team and including the team (Robinson & Trail, 2005). Six points of attachment were included in this study: team, coach, player, university, sport, and level of sport. The PAI consisted of 18 items including three items per point of attachment. Kwon et al. (2005) showed that the subscales of the PAI showed good construct reliability (AVEs = .635-.725) and internal consistency ([alpha] = .83-.87). In addition, Robinson and Trail (2005) also showed adequate to good construct reliability (AVEs = .48-.68) and internal consistency ([alpha] = .69-.85).
Therefore, there were a total 40 items (22 motive items and 18 points of attachment items) in addition to demographic items (gender, age, marital status, and ethnicity). All preface statements for the MSSC and PAI items focused on the university's football team and the response format for MSSC and PAI items in the questionnaire was a 7-point scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree).
The RAMONA Structural Equation Modeling (SEM) technique, available in the SYSTAT 7.0 (1997) statistical package, was used to examine the factor loadings of the items on the specified factors (a confirmatory factor analysis, CFA). In addition, alpha coefficients were calculated for each scale or subscale as internal consistency measures to determine how well the items in a specific scale were correlated to each other. According to Nunnally and Bernstein (1994), values greater than .70 are assumed to be adequate. Average Variance Extracted (AVE) values were used as construct reliability measures to indicate the amount each item contributes to explaining variance in the specified construct. AVE values greater than .50 are assumed to have good construct reliability (Fornell & Larcker, 1981).
In order to compare the four models suggested in the study and select the best fitting model, the RAMONA SEM technique was used. When comparing the models, root mean square error of approximation (RMSEA), the expected cross-validation index (ECVI), and chi-square difference test were used. According to Browne and Cudeck (1992), RMSEA is the best way to check for the fit of the model because chi-square value based fit indices are influenced largely by the sample size and the number of parameters in the model. RMSEA values less than .06 indicate a close fit of the model to the data (Hu & Bentler, 1999). However, according to Browne and Cudeck (1992), values less than .08 are also acceptable indicating a reasonable fit. On the other hand, values greater than .10 should not be considered (Browne & Cudeck, 1992). A chi-square difference test was only used when comparing between model A and B, and between model C and D. Models A, B and Models C, D could not be compared using chi-square difference test because they were not nested models.
In order to examine discriminant validity of the constructs, correlations among the constructs were examined. According to Kline (1985), constructs lack discriminant validity if they are too highly correlated (.85 or above).
In addition, the paths were determined to have practical significance if the variance explained was greater than 6%. According to Cohen (1992), a minimum of 6% of variance is required to have a practical significance.
Results of the confirmatory factor analysis on the MSSC showed a reasonable fit (RMSEA = .079; CI = .074-.085; pclose = 0.0, [chi square]/df = 776/188 = 4.13). The alpha coefficients ranged from .81 to .99, and the AVE values were all greater than .50, ranging from .60 to .83 for the seven motive subscales indicating good construct reliability (Table 1). The PAI showed an adequate fit (RMSEA = .083; CI = .076-.090; [p.sub.close] = 0.0, [chi square]/df = 528/127 = 4.40). The alpha coefficients ranged from .79 to .88, and the AVE values were all greater than .50, ranging from .57 to .69 for the six points of attachment subscales indicating good construct reliability (Table 2).
The goodness-of-fit statistics indicated that Model A (RMSEA = .072; CI = .069-.075; [p.sub.close] = 0.0, [chi square]/df = 2575/723 = 3.56, ECVI = 5.62), Model B (RMSEA = .072; CI = .069-.075; [p.sub.close] = 0.0, [chi square]/df = 2542/721 = 3.52, ECVI = 5.56), Model C (RMSEA = .072; CI = .069-.075; [p.sub.close] = 0.0, [chi sqaure]/df = 2584/723 = 3.57, ECVI = 5.63), and Model D (RMSEA = .072; CI = .069-.075; [p.sub.close] = 0.0, [chi square]/df = 2566/721 = 3.56, ECVI = 5.61) all performed equally well, showing reasonable fit. The chi-square difference test showed that the difference between Model A and B and the difference between Model C and D were significant. However, chi square values are easily inflated by sample size. Due to the inflation of these values, a more accurate way to compare the models is using RMSEA values and their confidence intervals. Yet, there were no differences in the RMSEA values across the four models. While the ECVI values were slightly different among the four models (Model A = 5.62, Model B = 5.58, Model C = 5.63, Model D = 5.61), the confidence intervals all overlapped indicating no practical significant differences among the models.
The difference between Models A, B and Models C, D is solely due to the location of the Social Interaction motive. In Models A and B, the Social Interaction motive was included in Overarching Motives while it was included in Fan Motives in Models C and D. The results indicated that whereas the Social motive loaded slightly higher on Overarching Motives (Model A, [beta] = .61; Model B, [beta] = .61) than on Fan Motives (Model C, [beta] = .57; Model D, [beta] = .57), the confidence interval values overlapped, indicating the Social motive loaded equally well on either Overarching Motives or Fan Motives.
The correlations between the second-order construct of Overarching Motives and Spectator Motives, and the first order construct of the Vicarious Achievement in Model A and Model B showed that these three constructs were highly correlated (Model A ranging from .81 to.91, Model B ranging from .77 to .93). The correlations between the second-order constructs of Fan Motives and Spectator Motives and the first order construct of Escape in Model C and Model D also showed that these three constructs were highly correlated (Model C ranging from .70-.84, Model D ranging from .68 to .82). Correlations greater than .85 raise an issue of discriminant validity. In Models A and B, the correlations between Vicarious Achievement and Overarching Motives (Model A, [beta] = .91, Model B, [beta] = .89) and the correlations between Overarching Motives and Spectator Motives (Model A, [beta] = .93, Model B, [beta] = .93) exceeded .85, indicating these constructs were not distinct from each other, whereas no correlation coefficients in Models C and D exceeded .85. Therefore, Models A and B were eliminated.
In addition, the difference between Models A/C and Models B/D is the inclusion of the paths between Organizational Identification and Sport Identification in Models B and D. Model D indicated that the path from Sport Identification to Organizational Identification was not significant ([beta] = .05) while the path from the Organizational Identification to the Sport Identification was both statistically ([beta] = .26) and practically significant explaining approximately 6.8% of the variance. This indicates that the addition of the path was correct (contrary to Model C); therefore, we chose Model D for further evaluation.
In Model D, most of the first order factors well represented their second order factors (Table 3), ranging from [beta] = .57 to [beta] = .95 for the motive subscales and [beta] = .52 to [beta] = .83 for the Points of Attachment subscales. There were three exceptions: Identification with Sport, Identification with Team, and Identification with Players. Identification with Sport loaded perfectly ([beta] = 1.00) on Sport Identification. Although Identification with Team did not load perfectly on Organizational Identification, the factor loading was .99, and the confidence interval ranged from .96 to 1.02, indicating a possible perfect loading. The former indicates a boundary parameter violation for this data set and the latter indicates a fallacy. On the other hand, Identification with Player did not load highly on the second order construct of Organizational Identification ([beta] = .26). This indicates that Identification with Player does not share much commonality with the other first order constructs in Organizational Identification for this data set.
All of the relationships among the latent variables were significant and explained a fair amount of variance except one, the path from Sport Identification to Organizational Identification. The Escape motive was significantly correlated with both Fan Motives ([beta] = .75) and Spectator Motives ([beta] = .68), and Fan Motives and Spectator Motives were also highly correlated ([beta] = .82). Fan Motives explained 77% of the variance in Organizational Identification. Spectator Motives explained 31% of the variance in Sport Identification. In addition, Organizational Identification explained 7% of variance in Sport Identification. However, Sport Identification was not significantly related to Organizational Identification (Table 3).
The purpose of the current study was to test different models that explain the relationships among motives and points of attachment and find a model that explains the greatest amount of variance in the referent variables. In particular, the motives were divided into different sets of categories based on whether an individual is a fan or a spectator, and it was assumed that fan motives and spectator motives would be related to different points of attachment. The results indicated that in fact, the motives for fans and spectators were different, and these different sets of motives led to different sets of points of attachment for this group of college football fans and spectators.
The choice of Model D showed that Social Interaction and Vicarious Achievement were more likely to be motives for fans of teams, whereas Skills, Aesthetics, Drama, and Knowledge were more likely to be the motives for fans of sport, meaning spectators. In addition, Escape was a motive that seemed to be connected to both groups. For example, both fans and spectators may attend games to escape from daily responsibilities. These results are in contrast to Trail, Robinson, et al.'s (2003) findings, which provided support for the Social Interaction motive as well as the Escape motive for being overarching motives that apply to both fans and spectators. This might be due to the fact that more social interaction opportunities may make themselves available when winning is valued. Winning is an objective outcome that may enhance sentiments of belonging. For example, fans might cheer together when their team scores and BIRG together after the team wins. When the team loses, the fans might engage in BLASTing together. On the other hand, these opportunities may be somewhat limited to spectators because the evaluations of physical skills of the athletes and appreciating the aesthetic nature of the performance is more subjective and oriented to the specific individual, meaning that one skill that is appreciated by an individual is not necessarily a skill that is appealing to others.
Regarding the relationships among motives and points of attachment, the Fan Motives variable was correlated with the Organization Identification variable, whereas the Spectator Motives variable was associated with Sport Identification. This finding supports the notion that different motives are related to different points of attachment and is consistent with previous findings (Trail, Robinson et al., 2003; Robinson et al., 2004; Robinson & Trail, 2005).
However, different from our expectations that Sport Identification would lead to Organizational Identification, and Organizational Identification would lead to Sport Identification, the path from Sport Identification to Organizational Identification was not significant. This result is understandable because liking a type of sport or a level of sport does not always mean that an individual would like the particular team salient to the survey.
In addition, different from our expectations, Identification with Player did not load on the second order construct of Organizational Identification as highly as the other factors. This may be due to the fact that fans and spectators acknowledge the fact that the college players are with the team only for a short period of time until they graduate. As a result, the fans and spectators do not necessarily view the players as a part of the organization. However, although player identification did not have a high factor loading on Organizational Identification, the magnitude (mean score) of player identification was as high as the other points of attachment. This indicates that player identification may be a separate point of attachment that does not belong to either Organizational Identification or Sport Identification, but still is an important point of attachment for fans. Therefore, utilizing players in marketing plan should not be ignored.
In the current study, the motive of physical attractiveness was not included because of the requirement by the athletic department. However, it is possible that the motive of physical attractiveness would load on both fan motives and spectator motives if it were to be included in the model. It may load on fan motives because the physical attractiveness of the players may influence why people become fans. Or, it may load on spectator motives because it is possible that people just come to the games to see attractive players without being fans of the players. More studies are needed in this matter to expand our model.
The findings of the current study have important practical implications for marketers. First of all, it suggests that motives that explain why people want to be, or feel that they are, attached to the organization (including the team, coach, players, etc.) may be somewhat different than motives that explain why people are fans of the sport or level of sport. This seems to indicate that there are, in fact, two different market segments, fans of the team and fans of the sport. The latter, who may have limited or no attachment to the marketer's team, would be considered spectators. This is consistent with Sloan's (1989) and Trail, Robinson, et al.'s (2003) claims. Therefore, marketers need to identify the target markets and should develop separate marketing strategies based on their target markets. If their focus is to attract more fans, the plan should include features that emphasize vicarious achievement and social interaction. On the other hand, if they would like to increase spectator attendance, then marketers should come up with strategies that focus more on the aspects of aesthetics, player skills, drama, and/or knowledge acquisition. For example, marketers can plan a social event where fans can get together after the games and interact with each other. In order to promote drama for spectators, marketers can schedule games with teams that have similar winning records or are more likely to provide other conditions conducive to a heightened level of drama.
In addition, marketers should recognize what aspect the fans and/or spectators are attached to and incorporate the information into the marketing plans. For example, the results showed that fans of teams are attached to the team, player, coach, and university. Therefore, in order to attract more fans to the games, the marketers can plan an event where fans can take pictures with the players and the coaches after the game. Conversely, for the spectators who are attached to the sport, marketers can set up a booth in the stadium where they provide interesting information about the specific sport, if there are a sufficient number of sport fans in the audience. In order to attract both fans and spectators, marketers can use a strategy that satisfies both groups. For instance, a free lesson where skills are taught by the players should be able to attract both fans who are attached to the players and spectators who are attached to the sport. Moreover, the idea of points of attachment can be expanded to marketing merchandise as well. For example, rather than selling products that only have team logos, they can expand the idea and produce products that are associated with players, coaches, and the sport itself.
There are some limitations in this study. Although the sample size was relatively large, whether the sample well-represents the population is debatable because it was a convenience sample. It is possible that if another sample is drawn using a different sampling method, the results might be significantly different from the current study. In addition, the sample consisted of only college students; therefore, it cannot be generalized to other populations other than college students. Also, the results cannot be generalized to other sports other than college football because the items only focused on a specific college football team. Further, the football team in the study was a Division I-A (Football Bowl Division) team; therefore, the findings may not be applied to other divisions. For these reasons, generalizability of the study is limited.
The current study contributes to the body of sport management literature, in particular spectator sport consumer motives. The findings support the notion that fans and spectators should be studied and marketed differently, therefore, providing evidence to researchers that these two segments should be studied separately as well as evidence to marketers that their marketing plans should be developed differently for fans and spectators. For example, Kim, Greenwell, Andrew, Lee, and Mahony (2008) found that a fan motive of vicarious achievement was significantly related to media consumption for an individual combat sport. In addition, Trail and James (2001) demonstrated that vicarious achievement, aesthetics, physical attraction, physical skill, and social interaction motives were significantly related to merchandise purchasing, whereas vicarious achievement, aesthetics, escape, physical skill, and social interaction motives were significantly associated with media consumption. Further, the authors found that acquisition of knowledge, aesthetics, and physical skill were significantly correlated with game attendance.
In order to provide more guidelines to researchers and practitioners, many more studies on the relationships among motives and points of attachment are needed. Future studies should examine the applicability of the model in different settings, such as different national cultures, types of sport, and levels of sport. In addition, studies should be conducted regarding gender differences to discover if the model is applicable to both males and females. Alternatively, a new model that considers the identification with player as a separate point of attachment not belonging to either organizational identification or sport identification should be tested to see if it better explains the relationships between motives and points of attachment.
Furthermore, researchers have recently started viewing team identification as a multi-dimensional construct (e.g., Dimmock, Grove, & Eklund, 2005; Heere & James, 2007). Therefore, including each dimension of team identification as separate points of attachment and studying the model would be beneficial. As discussed earlier, testing the model with inclusion of physical attractiveness motive is also necessary to improve the model.
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Boyun Woo, Galen T. Trail, Hyungil Harry Kwon, and Dean Anderson
Boyun Woo is a PhD candidate in sport management at Ohio State University. Research interests include organizational behavior and consumer behavior.
Galen, T. Trail, PhD, is an associate professor and the coordinator of the Master's of Sport Administration and Leadership program at Seattle University. His research focuses on sport consumer behavior.
Hyungil Harry Kwon, PhD, is an assistant professor in the Department of Physical Education at Chung-Ang University, Korea. His research interests include sport team licensed merchandise sales and sport consumers' psychological constructs such as team identification and sport fan commitment.
Dean Anderson, PhD, is a professor in the Department of Kinesiology at Iowa State University. His research focuses on sociology of sport.
Table 1. Factor Loadings (b), Confidence Intervals (CI), Standard Errors (SE), t-values, and Average Variance Explained (AVE) Values for the Motivation Scale for Sport Consumption Factor and Item [beta] CI SE t Vicarious Achievement I feel a personal sense of .82 .79-.85 .019 44.29 achievement when the team does well. I feel like I have won when the .84 .81-.87 .017 48.70 team wins. I feel proud when the team .83 .80-.81 .018 46.31 plays well. Aesthetics I appreciate the beauty .88 .86-.90 .013 69.29 inherent in the sport. I enjoy the natural beauty in .89 .88-.91 .012 74.96 the sport. I enjoy the gracefulness .86 .84-.89 .014 62.24 associated with the sport. Drama I enjoy the drama of close .84 .84-.89 .014 60.00 games. I prefer watching a close .70 .70-.78 .023 32.19 game rather than a one-sided game. I enjoy it when the outcome of .83 .80-.86 .017 49.29 the game is not decided until the very end. I enjoy the uncertainty of .85 .83-.88 .015 55.08 close football games. Escape The game provides an escape .87 .84-.90 .019 46.78 from my day-to-day routine. The game provides a distraction .64 .59-.69 .030 20.86 from my everyday activities. The game provides a diversion .80 .76-.84 .022 36.74 from "life's little problems" for me. Knowledge I can increase my knowledge .85 .83-.87 .016 53.24 about the sport. I can increase my understanding .91 .89-.93 .013 72.28 of the sport's strategy by watching the game. I can learn about the technical .85 .82-.88 .016 54.19 aspects of the sport by watching the game. Physical Skills The athletic skills of the .83 .81-.86 .017 49.41 players are something I appreciate. I enjoy watching a well- .81 .78-.84 .018 44.86 executed athletic performance. I enjoy a skillful performance .81 .78-.84 .018 44.90 by the team. Social I enjoy interacting with other .89 .87-.91 .011 79.91 people when I go to a game. I enjoy talking with other .94 .94-.99 .008 114.80 people when I go to a game. I enjoy socializing with other .9 .88-.92 .011 84.51 people when I go to a game. Factor and Item [alpha] AVE Vicarious Achievement .81 .70 I feel a personal sense of achievement when the team does well. I feel like I have won when the team wins. I feel proud when the team plays well. Aesthetics .99 .77 I appreciate the beauty inherent in the sport. I enjoy the natural beauty in the sport. I enjoy the gracefulness associated with the sport. Drama .89 .68 I enjoy the drama of close games. I prefer watching a close game rather than a one-sided game. I enjoy it when the outcome of the game is not decided until the very end. I enjoy the uncertainty of close football games. Escape .81 .68 The game provides an escape from my day-to-day routine. The game provides a distraction from my everyday activities. The game provides a diversion from "life's little problems" for me. Knowledge .90 .76 I can increase my knowledge about the sport. I can increase my understanding of the sport's strategy by watching the game. I can learn about the technical aspects of the sport by watching the game. Physical Skills .86 .67 The athletic skills of the players are something I appreciate. I enjoy watching a well- executed athletic performance. I enjoy a skillful performance by the team. Social .94 .83 I enjoy interacting with other people when I go to a game. I enjoy talking with other people when I go to a game. I enjoy socializing with other people when I go to a game. Table 2. Factor Loadings (b), Confidence Intervals (CI), Standard Errors (SE), t-values, and Average Variance Explained (AVE) Values for the Points of Attachment Index (PAI) Factor and Item [beta] CI SE Identification with the players I identify more with an individual .68 .64-.73 .029 player(s) on the team than with the team. I am a big fan of specific player(s) .86 .83-.90 .022 more than I am a fan of the team. I consider myself a fan of certain .83 .79-.87 .023 players rather than a fan of the team. Identification with the team I consider myself to be a "real" .85 .82-.88 .017 fan of the team. I would experience a loss if I had .8 .76-.83 .020 to stop being a fan of the team. Being a fan of the team is very .88 .85-.90 .015 important to me. Identification with the coach I am a big fan of Coach X. .65 .61-.70 .028 I follow the football team because .90 .87-.92 .016 I like Coach X. I am a fan of the football team .92 .89-.94 .015 because they are coached by X. Identification with the university I identify with numerous aspects .76 .72-.81 .026 of the university rather than just its team. I feel a part of the university .83 .79-.86 .024 community not just its teams. I support the university as a .74 .70-.79 .027 whole not just its athletic teams. Identification with sport First and foremost I consider .79 .75-.82 .022 myself a football fan. Football is my favorite sport. .70 .66-.74 .027 I am a football fan of all levels .81 .77-.84 .021 (e.g., high school, college, professional). Identification with level of sport I am a fan of collegiate football .82 .78-.85 .022 regardless of who is playing. I don't identify with one specific .59 .54-.65 .033 college football team, but collegiate football in general. I consider myself a fan of .83 .80-.87 .021 collegiate football, and not just one specific team. Factor and Item t [alpha] AVE Identification with the players .83 .63 I identify more with an individual 23.80 player(s) on the team than with the team. I am a big fan of specific player(s) 39.95 more than I am a fan of the team. I consider myself a fan of certain 35.92 players rather than a fan of the team. Identification with the team .88 .69 I consider myself to be a "real" 51.02 fan of the team. I would experience a loss if I had 40.29 to stop being a fan of the team. Being a fan of the team is very 58.6 important to me. Identification with the coach .85 .69 I am a big fan of Coach X. 23.3 I follow the football team because 57.08 I like Coach X. I am a fan of the football team 60.71 because they are coached by X. Identification with the university .82 .60 I identify with numerous aspects 29.18 of the university rather than just its team. I feel a part of the university 34.59 community not just its teams. I support the university as a 27.53 whole not just its athletic teams. Identification with sport .81 .59 First and foremost I consider 36.18 myself a football fan. Football is my favorite sport. 26.07 I am a football fan of all levels 39.21 (e.g., high school, college, professional). Identification with level of sport .77 .57 I am a fan of collegiate football 37.60 regardless of who is playing. I don't identify with one specific 17.72 college football team, but collegiate football in general. I consider myself a fan of 39.60 collegiate football, and not just one specific team. Table 3. Maximum Likelihood Point Estimates (b), Confidence Intervals (CI), Standard Errors (SE), t-values (t) for Sport Spectator Consumption Behavior Model D. 1st Order Factors on [beta] CI SE t 2nd Order Factors Vicarious Achievement <- .95 .92-.98 .017 57.44 Fan Motives Social <-Fan Motives .57 .51-.63 .035 16.46 Skill <-Spectator Motives .92 .89-.94 .016 56.65 Aesthetics <-Spectator Motives .9 .88-.93 .015 59.57 Drama <-Spectator Motives .75 .71-.79 .025 29.52 Knowledge <-Spectator Motives .66 .61-.71 .031 21.40 ID Team <-Organizational .99 .96-.1.02 .020 49.05 Identification ID Player <-Organizational .26 .18-.33 .048 5.29 Identification ID University <-Organizational .53 .47-.60 .04 13.31 Identification ID Coach <-Organizational .52 .46-.58 .038 13.76 Identification ID Sport <-Sport 1.00 1.00-1.00 -- -- Identification ID Level of Sport <-Sport .83 .08-.87 .024 34.76 Identification Fan Motives <-> Escape .75 .70-.79 .028 26.61 Fan Motives <-> Spectator .82 .77-.85 .023 34.92 Motives Escape <-> Spectator Motives .68 .63-.73 .032 21.62 Organizational Identification .88 .79-.97 .055 15.88 <- Fan Motives Sport Identification <- .56 .43-.69 .079 7.08 Spectator Motives Organizational Identification .05 -.24 .072 .72 <- Sport Identification Sport Identification <- .26 .11-.41 .089 2.90 Organizational Identification
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