80 Volume 21 ⢠Number 2 ⢠2012 ⢠Sport Marketing Quarterly ... Stephen Weaver, MA, is a graduate of the clinical psychology program at Murray State University.
Team Identification
Sport Marketing Quarterly, 2012, 21, 80-90, © 2012 West Virginia University
An Antecedent Model of Team Identification in the Context of Professional Soccer Nicholas D. Theodorakis, Daniel L. Wann, and Stephen Weaver Nicholas D. Theodorakis, PhD, is an assistant professor of sport management at Aristotle University of Thessaloniki. His research interests include sport consumer behavior and service quality. Daniel L. Wann, PhD, is a professor of psychology at Murray State University. His research interests include team identification and the social well-being of sport fans. Stephen Weaver, MA, is a graduate of the clinical psychology program at Murray State University. His research interests include sport fandom behavior and athletic superstitions.
Abstract The current investigation examined the interrelationships among overall sport team identification, specific dimensions of team identification, and behavioral intentions. Using an antecedents approach to guide predictions (Dabholkar, Shepherd, & Thorpe, 2000), a model was tested in which overall identification would mediate the relationship between specific dimensions of team identification and behavioral intentions. To test the hypothesized pattern of effects, participants completed a questionnaire packet assessing overall identification via the Sport Spectator Identification Scale (Wann & Branscombe, 1993), specific dimensions of identification assessed via the Team Identification Scale (Dimmock & Grove, 2006; Theodorakis, Dimmock, Wann, & Barlas, 2010), and four items assessing behavioral intentions. A series of regression analyses confirmed the predicted pattern of effects. Specifically, both overall identification and the specific dimensions of identification predicted behavioral loyalty and the dimensions predicted overall identification. However, when the dimensions and overall identification were simultaneously entered as predictors of behavioral loyalty, the results indicated that overall identification fully mediated the relationship between specific dimensions of identification and behavioral loyalty, as the beta scores of the dimensions were reduced to non-significant levels.
An Antecedent Model of Team Identification in the Context of Professional Soccer Over the course of the last two decades, sport scientists from psychology, sociology, and marketing/management have greatly expanded our understanding of the emotions, thoughts, and behaviors of sport fans and spectators. One area that has been the target of a particularly large portion of researchers’ attention involves sport team identification. Team identification concerns the extent to which a fan feels a psychological connection to a team and/or player and is a central component of one’s overall social identity (Wann, Melnick, Russell, & Pease, 2001). That is, the degree to which the fan believes that the team is an extension of his or herself. Researchers have examined the consequences of team identification (i.e., team identification as a predictor variable) and factors impacting the development and maintenance of team identification (i.e., team 80 Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly
identification as an outcome variable) (Wann, 2006). Research targeting the impact of identification as a causal (subject) variable indicates that it can have an impact on numerous fan responses (Dietz-Uhler & Lanter, 2008). For instance, sport fans with higher levels team identification tend to report higher levels of sport fan aggression (Rocca & Vogl-Bauer, 1999; Wann, Carlson, & Schrader, 1999). Further, highly identified fans are more likely to use cognitive distortions when evaluating their team’s performance (Abrams, Rutland, & Cameron, 2003; Wann & Dolan, 1994; Wann, Grieve, Waddill, & Martin, 2008). Of particular importance to sport marketers, level of team identification also predicts fans’ attempts to manipulate their association with a team (Trail, Anderson, & Fink, 2005) and consumption decisions (Matsuoka, Chelladurai, & Harada, 2003; Wakefield & Sloan, 1995). With respect to causes of team identification, researchers have identified a number of important
variables (Wann, 2006). These include rival team salience (Luellen & Wann, 2010; Pratt, 1998), geographic location (Jones, 1997; Uemukai, Takenouchi, Okuda, Matsumoto, & Yamanaka, 1995), socialization agents (James, 2001; Kolbe & James, 2003), perceived similarity with the team (Fisher, 1998), need/search for certainty (Dimmock & Grove, 2006), and team success (Fisher & Wakefield, 1998; Gwinner & Swanson, 2003; Sutton, McDonald, Milne, & Cimperman, 1997). One of the most important topics concerning sport team identification involves the accurate measurement of the construct (see Wann, 2006). One of the first instruments designed to measure team identification is the Sport Spectator Identification Scale (SSIS) developed by Wann and Branscombe (1993). The SSIS has strong psychometric properties and has been used in dozens of studies to predict various aspects of the fan experience (Wann et al., 2001). Further, this widely used scale has been translated into several languages including German (Straub, 1995), Dutch (Melnick & Wann, 2004), Japanese (Uemukai, et al., 1995), and French (BernacheAssollant, Bouchet, & Lacassagne, 2007). Although the SSIS has been successfully utilized in many psychological, sociological, and marketing settings, similar to other measures of identification (such as the Psychological Commitment to Team Scale developed by Mahony, Madrigal, & Howard, 2000, and the Connection to Team Scale developed by Trail & James, 2001), one potential concern of the SSIS lies in its unidimensional structure. Based on other literatures such as organizational behavior (Meyer & Allen, 1991) and social identity (Henry, Arrow, & Carini, 1999), it is likely that team identification is a multidimensional construct. Consequently, Dimmock, Grove, and Eklund (2005; Dimmock & Grove, 2006) recently validated a multidimensional measure of team identification. Labeled the Team Identification Scale (TIS), this instrument assesses three dimensions of team identification: Cognitive-Affective, Personal Evaluative, and Perceived Other Evaluative. Cognitive-Affective identification involves one’s knowledge of one’s group membership and the emotional significance of the membership. Personal Evaluative concerns the extent to which one values his or her team while Perceived Other Evaluative reflects one’s perceptions of how others value a team. This scale has strong psychometric properties and has been successfully translated into Greek (Theodorakis, Dimmock, Wann, & Barlas, 2010). In the current investigation, we examined the relationship between the unidimensional Sport Spectator Identification Scale and the multidimensional Team Identification Scale. It is clear that both the SSIS and the TIS can be successfully used to predict a variety of fan responses. For instance, both measures have been
used to predict intergroup bias among fans (Dimmock et al., 2005; Wann & Grieve, 2005). In the current study, we were interested in a possible antecedent model involving these scales and behavioral fan loyalty (e.g., attending a team’s sporting events). Specifically, we hypothesized that the specific components (e.g., forms) of identification assessed by the TIS may serve as antecedents to an overall sense of identification as assessed by the SSIS. If such an effect were valid, one should find a mediational pattern among these scales and outcomes such as behavioral loyalty. Several literatures served as the basis for this prediction. The first line of research supporting an antecedent hypothesis involves research on self-concept. One’s selfconcept is commonly defined as “an organized collection of beliefs and self-perceptions about oneself” (Baron & Berne, 2000). Although individuals have a single overall self-concept, this overall self-concept is comprised of many individual components or self-schema that can be viewed as a hierarchy (Byrne & Shavelson, 1996; Rentsch & Heffner, 1994). Similarly, self-esteem is often viewed as being both specific (an individual’s evaluation of a specific domain) and global (one’s evaluation of the total sum of the individual domains) (Harter, 1978, 1993; Pelham, 1993). Given this structure to selfconcept, it stands to reason that team identification might function in much the same way. That is, specific forms of identification (as assessed by the TIS) may form a total identification (as assessed by the SSIS). A second line of support for the antecedent hypothesis comes from work on services marketing. Recently, a number of researchers have highlighted the importance of perceptions of service quality within sport settings (Dale, van Iwaarden, van der Wiele, & Williams, 2005; Theodorakis, Alexandris, & Ko, 2011) and have successfully developed service quality models for sport spectating (Hightower, Brady, & Baker, 2002; Ko, Zhang, Cattani, & Pastore, 2011). With respect to model development, authors have sometimes viewed perceptions of service quality in a hierarchical fashion. For instance, Dabholkar, Thorpe, and Rentz (1996) suggest that overall service quality is a function of five specific (i.e., basic) dimensions: physical aspects (e.g., appearance), reliability, personal interaction, problem solving, and store policy. These authors found strong empirical support for their proposed hierarchical structure and argue that their model is useful for practitioners “interested in determining overall service quality as well as specific dimensions of service quality” (p. 13). Furthermore, in a longitudinal examination of perceptions of service quality of a national photographic company, Dabholkar, Shepherd, and Thorpe (2000) proposed that an antecedents model better represents perceptions of service quality than a simple compoVolume 21 • Number 2 • 2012 • Sport Marketing Quarterly 81
nents approach. That is, they argued dimensions (i.e., factors such as personal attention) of service quality are “not a straightforward sum of the components” (p. 166). Rather, the factors serve as antecedents to overall service quality. Their results support their line of reasoning. Specifically, they found that the individual factors acted as antecedents to overall service quality which, in turn, influenced behavioral intentions. The aforementioned research by Dabholkar and colleagues highlights the distinction between conceptualizing a construct as reflective versus formative (Dagger, Sweeney, & Johnson, 2007; Jarvis, MacKenzie, & Podsakoff, 2003). Viewing a construct (such as service quality) as reflective suggests that certain dimensions serve as indicators of an overall construct. Conversely, a formative approach implies that an individual dimension will “give rise to or cause the overall construct” (Dagger et al., p. 125). In keeping with the hierarchical approach described above, a formative approach would predict a mediational model in which an overall construct (e.g., perceptions of overall service quality) will mediate the relationship between individual dimensions (e.g., individual dimensions of service quality) and various outcomes (e.g., behavioral loyalty). As, Dabholkar et al. (2000) noted, “a component to antecedent transition would be a natural progression in the development of constructs” (p. 143).
We are suggesting that sport team identification may function in much the same way. That is, consistent with the theoretical arguments outlined above (Dabholkar et al., 2000; Dagger et al., 2007), it was hypothesized that specific dimensions of team identification would serves as antecedents of overall team identification which, in turn, would influence behavioral intentions (this pattern is depicted graphically in Figure 1). Thus, we predicted a mediational model that was consistent with the formative approach. Recent work by Lings and Owen (2007) tested the antecedents model among fans. These authors assessed affective (emotional) commitment to a team (see Allen & Meyer, 1990), team identification, and intentions to purchase sponsors’ products.1 As expected, and consistent with the theoretical framework outlined above, team identification mediated the relationship between affective commitment and purchase intentions. However, Lings and Owen (2007) only assessed one form of commitment, affective commitment. Research from a social identity perspective suggests that team or group commitment is multidimensional (e.g., Ellemers, Kortekas, & Ouwerkerk, 1999). Consequently, the purpose of the current investigation was to extend the work of Lings and Owen (2007) by testing an antecedents model with multiple forms of commitment. We examined the
Figure 1. Hypothesized antecedents (i.e., hierarchical) relationships among specific dimensions of team identification, overall team identification, and behavioral loyalty.
82 Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly
hypothesized pattern of effects by assessing specific dimensions of team identification, overall team identification, and behavior intentions to consume the product (e.g., attend games).
Method Participants Owing to the absence of a comprehensive list of the population of Greek sport fans, a non-probability sampling method was used with convenience sampling. The sample consisted of 202 students from a physical education and sport department at a metropolitan university in Greece. Demographic information indicated that 158 (80.6%) of the participants were male and 38 (19.4%) female. Ages ranged from 18 to 34 years with a mean of 21.03 years (SD = 2.14). Procedure Lecturers from the university were asked for permission to distribute questionnaires to students who completed the protocols in a university classroom (testing sessions lasted approximately 10 minutes). Prior to the distribution of questionnaires, researchers explained the purpose of the study, provided instructions for questionnaire completion, and obtained informed consent. The participants were informed that they could cease participation at any time. After completing the questionnaires, participants were debriefed and excused from the testing session. Measures Participants completed a questionnaire packet containing four sections. The first section contained demographic items assessing gender and age. Next, participants completed the Greek version of the Sport Spectator Identification Scale (SSIS–G; Theodorakis, Vlachopoulos, Wann, Afthinos, & Nassis, 2006) to measure their overall psychological connection to the team (Overall Team Identification). Similar to the original SSIS (Wann & Branscombe, 1993), the Greek version is unidimensional and contains seven Likertscale items. Respondents provide their responses on a scale ranging from 1 (low identification) to 8 (high identification). Recently, Theodorakis et al. (2006) provided evidence regarding the scale’s internal consistency, test-retest reliability, factor structure, concurrent validity, construct validity, and cross-cultural validity. The participants targeted their favorite sport team when completing the SSIS-G. The third section assessed the multidimensional nature of team identification via the Greek version of the Team Identification Scale (TIS-G; Theodorakis et al., 2010). Consistent with the original Team
Identification Scale (Dimmock & Grove, 2006; Dimmock et al., 2005), the TIS-G has three dimensions (i.e., three, three-item subscales): Cognitive-Affective (e.g., “My favorite team’s successes are my successes.”), Personal Evaluative (e.g., “My favorite team has a lot to be proud of.”), and Perceived Other Evaluative (e.g., “Others have a positive view of my favorite team.”). Responses were anchored via a 7-point Likert-scale ranging from 1 (strongly disagree) to 7 (strongly agree), with greater numbers representing higher levels of team identification. The psychometric properties of the TISG were recently tested by Theodorakis et al. (2010) and the scale was found to be reliable, valid, have predictive validity, and generalizable across nationality. Similar to the completion of the SSIS-G, participants targeted their favorite team when completing the TIS-G. The final section contained four items measuring behavioral loyalty. Participants indicated how often they attended their favorite team’s sporting events in person, watched their favorite team on television, listened to their favorite team on the radio, and discussed their favorite team with friends and relatives. Responses options to these items were: 1 = never, 2 = once a year, 3 = twice a year, 4 = once a month, 5 = twice a month, 6 = once a week, 7 = twice a week, and 8 = once a day. These behavioral indices are based on the work of McPherson (1976) and Smith, Patterson, Williams, and Hogg (1981) and are frequently used in sport literature (Melnick & Wann, 2004; James, 2001; Theodorakis & Wann, 2008; Wann et al., 2001).
Results Confirmatory Factor Analysis To test the dimensionality of the Team Identification Scale, a three-factor Confirmatory Factor Analysis model (CFA: Ullman, 1996) was computed using EQS (Bentler, 1995). Initially, an exploratory data analysis based on the inspection of skewness values, kurtosis values, along with the use of the Kolmogorov-Smirnov test of normality, showed that that all variables were not normally distributed (see Table 1). Mardia’s coefficient of multivariate kurtosis was 19.90 (Mardia, 1970) and the normalized estimate was 9.87. As Bentler (1995) suggested normalized estimate values greater than 5 indicate departures from normality. Thus, it seemed that the assumption of multivariate normality was not tenable. Based on the above results, and in conjunction with the fact that the present data were in ordinal scale, it was decided to use the Sattora-Bentler scaled χ2 statistic. Results indicated an adequate fit for the three factor model: X2 = 62.533, df = 24, p < .001, NNFI = .934, CFI = .956, SRMR = .059, RMSEA = .091. Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly 83
Table 1. Descriptive and CFA Item Statistics for the TIS-G and the SSIS-G.
Variable
M
SD
Cognitive/Affective (TIS-G) Item 1 4.28 2.03 Item 2 3.95 2.08 Item 3 3.18 1.89 Personal Evaluative (TIS-G) Item 4 5.40 1.76 Item 5 5.49 1.53 Item 6 5.58 1.54 Perceived Others Evaluative (TIS-G) Item 7 5.15 1.39 Item 8 5.08 1.45 Item 9 5.09 1.56 Team Identification (SSIS-G) Item 1 5.77 2.01 Item 2 5.21 2.14 Item 3 5.16 2.20 Item 4 5.96 2.12 Item 5 5.36 2.19 Item 6 4.74 2.31 Item 7 4.27 2.45
Skewness Kurtosis
Factor Loading
Error Term
SMCs
-0.16 -1.22 0.05 -1.28 0.52 -0.75
.92 .88 .62
.23 .46 .78
.94 .78 .39
-0.94 -0.14 -1.03 0.58 -1.07 0.39
.91 .86 .83
.41 .50 .55
.82 .74 .69
-0.69 0.27 -0.77 0.32 -0.74 0.12
.72 .89 .68
.68 .44 .72
.53 .80 .47
-0.68 -0.26 -0.26 -0.81 -0.42 -0.14 0.10
.79 .94 .91 .78 .86 .62 .70
.60 .33 .39 .62 .49 .77 .71
.63 .89 .84 .61 .75 .39 .49
-0.43 -1.01 -1.09 -0.42 -1.01 -1.14 -1.37
Note: SMC = Square Multiple Correlations. N = 202. To examine the factor structure of the SSIS-G, a similar procedure was followed (Table 1). For the SSIS-G items, skeweness values ranged from -.81 to .10 and item kurtosis ranged from -1.37 to -.42. Mardia’s (1970) coefficient of multivariate kurtosis was 12.60 and the normalized estimate was 7.92. The SattoraBentler scaled χ2 statistic was also used. Results indicated a good fit of the model to the data: X2 = 29.34, df = 13, p < .001, NNFI = .977, CFI = .986, SRMR = .033, RMSEA = .801. For both scales all item loadings had significant t-values ranging from 10.11 to 17.81 (Anderson & Gerbing, 1988), and average variance extracted (AVE) for both constructs (Table 1) exceeded the suggested 0.50 cut off (Fornell & Larcker, 1981). Descriptive Statistics and Reliability The seven items comprising the SSIS-G were summed and divided by seven, resulting in a score consistent with the original parameters of each item (i.e., 1-8). Similarly, the items on each TIS-G subscale were summed and divided by three while the four behavioral loyalty items were summed and divided by four. Means, standard deviations, and reliability estimates for all measured variables are presented in Table 2. Correlations among the variables can be found in 84 Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly
Table 3. The SSIS-G represented fans’ overall identification with the sport team (M = 5.21, SD = 1.84). Responses on the three antecedents of Overall Team Identification were moderately high for the Personal Evaluative (M = 5.46, SD = 1.51), and Perceived Other Evaluative (M = 5.06, SD = 1.31) dimensions, with the Cognitive-Affective dimension receiving lower scores (M = 3.80, SD = 1.78). All correlation coefficients were statistically significant, yielding moderate to relatively high values. The alpha values for all measured variables were satisfactory and ranged from .78 to .92. Average variance extracted (AVE) for all constructs exceeded the suggested 0.50 cut off (Fornell & Larcker, 1981). The composite reliabilities exceeded the 0.60 threshold suggested by Bagozzi and Yi (1988), also indicating good levels of reliability (Table 2). Testing for the Mediation Effect of Team Identification The causal steps approach, as presented in a series of articles by Kenny and his colleagues (Baron & Kenny, 1986; Judd & Kenny, 1981; Kenny, Kashy, & Bolger, 1998), was used to test the mediation effect of team identification on the relationship between the three potential antecedents and fans’ Behavioral Loyalty. This method is quite common in psychological
Table 2. Means, Standard Deviations, and Reliability Estimates for the Variables.
Composite Reliability
Variable
M
SD
alpha
Behavioral Loyalty Cognitive/Affective (TIS-G) Personal Evaluative (TIS-G) Perceived Others Evaluative (TIS-G) Team Identification (SSIS-G)
5.21 5.21 3.80 5.46 5.06
1.54 1.84 1.78 1.51 1.31
.78 .92 .86 .90 .82
Variable
1
2
3
4
5
Behavioral Loyalty (1) Cognitive/Affective (TIS-G) (2) Personal Evaluative (TIS-G) (3) Perceived Others Evaluative (TIS-G) (4) Team Identification (SSIS-G) (5)
— .70* .58* .53* .26*
— .75* .68* .45*
— .66* .42*
— .58*
—
— .91 .74 .79 .70
AVE — .81 .66 .75 .59
Table 3. Correlations among the Variables.
Note: * = p < .01. research (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002) and has been successfully used by scholars in the sport and leisure domains (e.g., Alexandris, Kouthouris, & Girgolas, 2007; Stevenson & Lochbaum, 2008; Thogersen, Fox, & Ntoumanis, 2002). In this method, a series of regression analyses are performed in four steps. The first step is designed to demonstrate a significant relationship between the predictors (i.e., the three team identification dimensions of the TIS-G) and the outcome variable (i.e., Behavioral Loyalty). In the second step, a significant relationship between the predictors (the three team identification dimensions of the TIS-G) and the mediator variable (Overall Team Identification as assessed by the SSIS-G) should be established. Next, in Step 3, one should establish that the mediator (Overall Team Identification as assessed by the SSIS-G) is related to the outcome variable (Behavioral Loyalty). In the fourth and final step, one needs to document that the mediator (Team Identification) significantly reduces the strength of the relationship between the predictors (three team identification dimensions) and the outcome (Behavioral Loyalty). Step 1: Establishing the link between the three dimensions of team identification (TIS-G) and Behavioral Loyalty. The first regression analysis was conducted to establish the link between the three identification dimensions as assessed by the TIS-G and Behavioral Loyalty (see Table 4). Behavioral Loyalty
was set as the dependent variable and the three dimensions as the predictor variables. Results indicated that a significant amount of variance in Behavioral Loyalty was predicted by the three variables (F = 40.99, p < .001). With respect to specific contributions, the Cognitive-Affective (B = .33) and Personal Evaluative (B =.35) dimensions offered significant contributions to the prediction. However, Perceived Others Evaluative did not offer a significant contribution to the prediction. Step 2: Establishment of the link between the three dimensions of team identification (TIS-G) and Overall Team Identification (SSIS-G). The next step was designed to establish the link between the three dimensions of team identification and overall team identification (see Table 4). Thus, we regressed team identification on the Cognitive-Affective, Personal Evaluative, and Perceived Other Evaluative dimensions. The regression analysis produced a significant effect (F = 115.34, p < .001). Cognitive-Affective (B =.50) and Personal Evaluative (B =.43) offered significant unique contributions to the prediction. Perceived Others Evaluative did not offer a significant contribution to the prediction. Step 3: Establishing the link between Overall Team Identification and Behavioral Loyalty. A third regression analysis was employed to test whether Overall Team Identification was related to Behavioral Loyalty (Table 4). The unstandardized regression coefficient (B Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly 85
Table 4. Multiple Regression Analyses of the Mediation Effect of Team Identification (SSIS-G).
Testing Steps Step 1 Outcome: Behavioral Loyalty Predictor: Cognitive/Affective Predictor: Personal Evaluative Predictor: Perceived Others Evaluative Step 2 Mediator: Team Identification Predictor: Cognitive/Affective Predictor: Personal Evaluative Predictor: Perceived Others Evaluative Step 3 Outcome: Behavioral Loyalty Mediator: Team Identification Step 4 Outcome: Behavioral Loyalty Mediator: Team Identification Predictor: Cognitive/Affective Predictor: Personal Evaluative Predictor: Perceived Others Evaluative
B
SE B
95% CI
beta
p
.33 .35 -.88
.06 .08 .08
.20 - .46 .18 - .52 -.25 - .07
.38 .33 -.07
.001 .001 n.s.
.50 .43 .10
.06 .07 .07
.38 - .62 .27 - .58 -.04 - .25
.48 .34 .07
.001 .001 n.s.
.58
.04
.50 - .67
.70
.001
.50 .06 .14 -.13
.07 .07 .08 .07
.36 - .64 -.07 - .20 -.02 - .31 -.28 - .01
.60 .07 .14 -.11
.001 n.s. n.s. n.s.
Note: CI = Confidence Interval. = .58) associated with the effect of Team Identification on fans’ Behavioral Loyalty was significant (p < .001). Thus, the requirement for Step 3 was met. Step 4: Examining the meditation role of Overall Team Identification. On the final step, a regression analysis was performed examining simultaneously the influence of Overall Team Identification (SSIS-G) and its antecedents (Cognitive-Affective, Personal Evaluative, and Perceived Other Evaluative) on fans’ Behavioral Loyalty (see Table 4). Overall, the regression model was significant (F = 39.17, p < .001). Results indicated that Overall Team Identification fully mediated the relationship between its antecedents and fans’ Behavioral Loyalty, as the beta scores of Cognitive-Affective (B = .06), Personal Evaluative (B = .14), and Perceived Other Evaluative (B = -.13) were reduced to non-significant levels.
Discussion The current investigation examined the relationship between two measures of identification: the unidimensional Sport Spectator Identification Scale (Wann & Branscombe, 1993) and the multidimensional Team Identification Scale (Dimmock & Grove, 2006; Dimmock et al., 2005). Based on research and theory on the organization of the self (Byrne & Shavelson, 1996; Rentsch & Heffner, 1994; Harter, 1978) and work 86 Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly
on perceptions of service quality (Dabholkar et al., 2000; Dabholkar et al., 1996), we predicted that the relationship between these two measures would best be understood from an antecedents framework. More specifically, we expected that specific dimensions of identification as assessed by the TIS would serve as antecedents of overall identification as measured by the SSIS. Further, we hypothesized that overall identification would mediate the relationship between dimensions of identification and behavioral loyalty (e.g., game attendance). The mediational analyses confirmed the expected patterns of effects. Traditionally, sport marketing researchers have treated the psychological connection to a team as a unidimensional measure (Mahony et al., 2000; Trail & James, 2001; Wann & Branscombe, 1993). More recently, researchers have suggested that team identification is better conceived as a multidimensional construct (Dimmock et al., 2005; Heere & James, 2007). Supporting the multidimensional conceptualization of team identification, our results indicated that dimensions of team identification should be seen as antecedents to the construct. As Dabholkar et al. (2000) argued, a transition of treating a construct’s dimensions as antecedents to the overall construct, instead of its components, is a natural step in theory building. In line with recent theoretical insights from
the marketing literature, we propose a formative approach for modeling team identification in which dimensions of identification give rise to or cause overall identification (Dabholkar et al., 2000; Dagger et al., 2007; Jarvis et al., 2003). The data presented above are consistent with the formative approach, rather than a reflective approach where team identification dimensions act as reflective indicators of the overall construct (Dagger et al., 2007). Our findings provide an extension of Lings and Owen’s (2007) investigation of the interrelationships among affective commitment, team identification, and behavioral intentions. In their work, Lings and Owen (2007) found evidence for an antecedents approach as overall team identification mediated the relationship between affective commitment and intentions to purchase sponsors’ products. Our data both replicated and extended this finding. Given that the current investigation supported an antecedents approach, it would be interesting for sport scientists to examine the validity of this framework for other sport fan behaviors. For instance, perhaps fan motivation is also best understood within an antecedents model. Many different fan motives have been proposed including group affiliation, eustress/drama, escape/diversion, self-esteem, and aesthetics (Funk, Mahony, & Ridinger, 2002; Funk, Ridinger, & Moorman, 2003; James & Ross, 2004; McDonald, Milne, & Hong, 2002). Further, these dimensions are sometimes summed to develop as assessment of overall (total) motivation (e.g., Wann, 1995). The various motives are then expected to impact fan behavior, in particular, direct and indirect consumption (Cohen & Avrahami, 2005; Lee, Ko, & Chun, 2005; Wigley, Sagas, & Ashley, 2002). Perhaps the relationships among the specific types/dimensions of motivation, overall motivation, and sport consumption are also best viewed within an antecedents framework. Based on the findings reported here and elsewhere (e.g., Dabholkar et al., 2000), one could predict that the specific dimensions of motivation would serve as antecedents of overall motivation which, in turn, would predict sport consumption. Such a prediction could be easily tested using assessments of overall motivation, subscales of specific domains, and items assessing behavioral loyalty. Implications for Sport Marketing Research and Practice The results presented above have implications for sport marketing research and practitioners. One such implication involves the supported antecedent framework. Dabholkar et al. (1996) note that marketing professionals often have a need to assess both individual components of a construct and the overall construct. While assessing an overall construct can provide insight at a general level, additional information can be gained through assessments of specific dimensions. Indeed,
certain dimensions may be more related to behavioral outcomes than are others (Dimmock et al., 2005). In fact, this is precisely the pattern we found here as the Perceived Other Evaluative dimension did not significantly predict behavioral loyalty, even though this team identification dimension consistently predicted sport fans’ behaviors in other studies (Dimmock & Grove, 2006; Dimmock et al., 2005). In terms of the team identification inventories examined in the present investigation, this suggests that when sport marketing scholars desire an overall assessment of identification they would be wise to utilize the Sport Spectator Identification Scale (Wann & Branscombe, 1993). Because a great number of sport marketing studies take place inside the sport arena, practitioners might use the overall identification instrument to handle time and money constraints, especially when their research goal is to gather benchmark data for their fans’ identification levels (i.e., among other league teams), or conduct a periodic “check” on their team’s fan base (Dabholkar et al., 1996). However, when their research/application more precisely involves a specific dimension (or dimensions) of identification, they should opt for the Team Identification Scale (Dimmock et al., 2005). Furthermore, by using both instruments, sport marketers could capture the extent to which and to what degree antecedents of identification ultimately influence fans’ allegiance with their team. In other words, our results suggest that sport marketers should improve levels of identification among their fans via the three dimensions. For example, sport marketers could develop campaigns to influence fans’ cognitive and affective responses, as well as their perceptions of a team’s values. Such a strategy should lead to strong levels of team identification. Recently, increased competition among sport organizations and professional leagues, a saturated sport marketplace, and a plethora of other recreational activities have forced sport marketers to use a wide variety of market segmentation strategies to better understand and satisfy the complex needs of fans (Funk & Pastore, 2000; Howard & Crompton, 2004; Ross, 2007). Our antecedents model of team identification could be used by sport marketing professionals to segment their fan base. Mahony et al. (2000) argued that marketers should first develop a clear understanding of their customers by segmenting them on the basis of their degree of overall team identification. Then, they should develop appropriate marketing strategies to develop behavioral loyalty among these segments (Ross, 2007). For example, research indicates that to influence the future behavior of the highly identified fan segment, sport marketers should provide economic incentives (Dale et al., 2005). However, the above pricing tactic is not Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly 87
appealing to the low identified fan segment, which is most attracted by entertainment opportunities inside the sport arena (Mahony et al., 2000). The level of fan identification could also be used to drive sponsorship decisions. As research indicated, it might be more beneficial to sponsors to target the highly identified fans segment, because this group is more likely to develop a positive image of their team sponsor, express a willingness to recommend the sponsors, and eventually purchase the sponsor’s products and services (Gwinner & Swanson, 2003; Tsiotsou & Alexandirs, 2009). Drawing from other leisure sectors where practitioners segment their customers using psychological profiles, sport marketers could also use the three antecedents of overall identification to construct fan profiles. These can then be used to identify distinct market segments (Dimanche, Havitz, & Howard, 1993; Kyle, Kerstetter, & Guadagnolo, 2002). This type of segmentation might be more beneficial to sport professionals because it provides richer information than the overall measurement of the construct (Dimanche et al., 1993). Segmenting their market using identification profiles along with other variables such as service preference and behavioral measures, sport marketing practitioners could optimally meet the needs of their fans by tailoring elements of the marketing mix to each group’s needs (Kyle et al., 2002). Limitations and Suggestions for Future Research Although the current data and analyses advance our understanding of the relationships among overall team identification, specific dimension of identification, and behavioral loyalty, there is still much we do not know that could be the focus of additional research. First, it is clear that future researchers need to generalize the current findings to other groups of fans. One limitation of the current study was that the test sample was comprised primarily of male Caucasian college students in Greece. Future investigations should attempt to replicate the findings detailed here with a more heterogeneous sample (e.g., females, older persons, etc.). Similarly, the current sample was rather homogeneous in terms of level of identification, with most fans reporting moderate to high levels. Future endeavors should attempt to replicate the findings reported here with samples containing greater numbers of fans low in team identification. Further, future investigations should focus on other measures of overall identification and specific components of identification. Although the measures employed in the current research have strong psychometric properties, within the last few decades a number of other measures of identification have been developed which could be used to replicate the current 88 Volume 21 • Number 2 • 2012 • Sport Marketing Quarterly
research. Some of these additional measures assess overall sport team identification (e.g., the aforementioned Psychological Commitment to Team Scale, Mahony et al., 2000, and the Connection to Team Scale, Trail & James, 2001). Other measures of team identification are multidimensional and, similar to the Team Identification Scale utilized here, assess multiple forms of identification. One such measure is the Scale of Commitment to Sport Teams (SCST; Matsuoka, 2001). The SCST assesses five dimensions of team identification (commitment): Personal Identity (a fan’s sense of belonging to a team), Affective Commitment (emotional attachment), Calculative Commitment (costs associated with no longer supporting a team), Social Obligation (normative pressure to follow a team), and Regional Tribalism (pride and connection to a specific location). Future investigations should utilize these additional scales in an attempt to replicate the mediational pattern of effects reported here. And finally, it is interesting to note that Lings and Owen (2007) found that team success was an important variable in the interrelationships among commitment, identification, and purchase intentions. In their research on Australian Football League fans, the authors conducted separate examinations of fans supporting successful teams (i.e., the two teams in the league finals) and fans of unsuccessful teams (i.e., the bottom three teams in the league standings). Although team identification mediated the relationship between affective commitment and the purchase intentions of supporters of successful teams, the effect was absent among fans of poorly performing teams. This suggests that there are situational factors that influence the antecedents approach. Future researchers should attempt to identify these factors, such as league type (e.g., professional versus amateur), sport type, and media exposure of the team.
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Endnote 1
As has been noted elsewhere (Wann, 2006), the terms team identification and team commitment tend to be used interchangeably in the literature on sport fan psychology and marketing. Although small differences between these labels may exist (Mael & Ashforth, 2001), we will use them interchangeably here because research suggests that questionnaires assessing team identification and commitment are highly correlated (Wann & Pierce, 2003) and operational definitions of these constructs frequently overlap (see Wann, 2006).
