399

Masayuki Yoshida is with Biwako Seikei Sport College in Otsu, Shiga, Japan. Brian Gordon is with the University of Wisconsin-La Crosse in La Crosse, WI. Makoto Nakazawa is with the University of Tsukuba in Tsukuba, Ibaraki, Japan. Rui Biscaia is with CIPER, Universidade de Lisboa, Portugal. Address author correspondence to Masayuki Yoshida at yoshida-m@bss.ac.jp.

Journal of Sport Management, 2014, 28, 399-417 http://dx.doi.org/10.1123/jsm.2013-0199 © 2014 Human Kinetics, Inc.

Conceptualization and Measurement of Fan Engagement: Empirical Evidence From a Professional Sport Context

Masayuki YoshidaBiwako Seikei Sport College

Brian GordonUniversity of Wisconsin–La Crosse

Makoto NakazawaUniversity of Tsukuba

Rui BiscaiaUniversidade de Lisboa

In the sport management literature, limited attention has been devoted to the conceptualization and measure-ment of fan engagement. Two quantitative studies were completed to validate the proposed fan-engagement scale composed of three defining elements (management cooperation, prosocial behavior, and performance tolerance). The results from Study 1 provide evidence of convergent and discriminant validity for the three-factor model of fan engagement. In Study 2, we assess nomological validity by examining the antecedents and consequences of fan engagement and found that team identification and basking in reflected glory played a particularly important role in increasing the three dimensions of fan engagement. Furthermore, the results indicate that performance tolerance has a positive effect on purchase intention. These findings highlight the importance of the sequential relationships between team identification, performance tolerance, and purchase intention.

Keywords: fan engagement, customer engagement, nontransactional behavior, extra-role behavior, coopera-tion, prosocial behavior

Sport fans are defined as “individuals who are interested in and follow a sport, team and/or athlete” (Wann, Melnick, Russell, & Pease, 2001, p. 2). A grow-ing number of researchers suggest that loyal sport fans will engage in various forms of behavior related to sport teams (Bristow & Sebastian, 2001; Funk & James, 2001; Hunt, Bristol, & Bashaw, 1999; Holt, 1995). Sport fans’ engagement in following their favorite teams includes attending sporting events, watching games on televi-sion, purchasing a number of team products, reading sport magazines and newspapers, and talking with others about sport (Bristow & Sebastian, 2001; Funk & James, 2001; Hunt et al., 1999). Highly engaged sport fans are likely to focus not only on self-interested tasks (e.g., attending, watching, reading, and purchasing) but

also on tasks that benefit their favorite sport teams (e.g., supportive displays of sport fandom, positive word-of-mouth, and collaborative event attendance; de Ruyter & Wetzels, 2000; Swanson, Gwinner, Larson, & Janda, 2003) and other fans (e.g., sharing knowledge about a team with other fans, cooperative communications in the stands, and consumer-to-consumer helping behaviors in fan communities; Dietz-Uhler & Murrell, 1999; Fisher & Wakefield, 1998). Such team- and others-oriented behaviors are referred to as extrarole behaviors (Ahearne, Bhattacharya, & Gruen, 2005; Podsakoff, MacKen-zie, Paine, & Bachrach, 2000). In consumer behavior research, a number of extrarole behaviors have been investigated, such as positive word-of-mouth, recruit-ing new consumers, providing suggestions for product improvement, participating in new product development, and collaborating with other consumers (Ahearne et al., 2005; Bettencourt, 1997; Füller, Matzler, Hoppe, 2008; Gruen, Summers, & Acito, 2000).

Theorizing about the development of sport fan loy-alty, Funk and James (2001) stipulate that such engag-ing behaviors are specifically observable at the stage of

Official Journal of NASSMwww.JSM-Journal.com

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400 Yoshida et al.

allegiance that reflects the extent to which an individual’s commitment to a sport team is persistent and resistant. To explain fans’ commitment to sport teams, many useful constructs have been developed in the sport manage-ment literature, including team identification (Wann & Branscombe, 1990), team identity (Heere, James, Yoshida, & Scremin, 2011), fan loyalty (Funk & James, 2001), psychological commitment to team (Mahony, Madrigal, & Howard, 2000), psychological connection to team (James, Kolbe, & Trail, 2002), team attachment (Mahony, Nakazawa, Funk, James, & Gladden, 2002), spectator-based brand equity (Ross, Russell, & Bang, 2008), and consumer-team relationship quality (Kim, Trail, & Ko, 2011). However, previous research has focused on the attitudinal aspect of sport fans and has largely ignored fans’ unique behavioral responses. In view of the limitations of the existing research, a con-ceptual model and scale items for measuring sport fans’ engagement behaviors have yet to be developed in the sport context. Such an endeavor would satisfy a glaring void in the sport management literature and is the aim of the current study.

In recent years, the idea of customer engagement has attracted significant attention from researchers (Brodie, Hollebeek, Juric, & Ilic, 2011; Schau, Muñiz, & Arnold, 2009; van Doorn, Lemon, Mittal, Nass, Pick, Pirner, & Verhoef, 2010; Verhoef, Reinartz, & Krafft, 2010). Notably, past research on relationship marketing has been conducted primarily on the transactional side of a consumer-company relationship (Grewal, Monroe, & Krishnan, 1998; Rust, Lemon, & Zeithaml, 2004). Transactional consumer behavior is based on a trade-off between costs and benefits, is an in-role behavior, and is typically executed according to company guidelines. Researchers have investigated the predictor variables of customer retention (e.g., repeat purchase, cross-buying, and word-of-mouth activity) in the exchange of money, time, and effort for a product (a physical good or a ser-vice; Grewal et al., 1998; Rust et al., 2004). On the other hand, nontransactional consumer behavior (e.g., volun-tary participation in marketing programs, collaborative product customization, and social bonding with fellow brand users) has been largely ignored but has become increasingly important in a networked society in which consumers can easily interact with other consumers and companies through the Internet and other new media (Schau et al., 2009; Verhoef et al., 2010). The idea of cus-tomer engagement captures a variety of nontransactional consumer behaviors (Verhoef et al., 2010) and can be useful for explaining how consumers and firms cocreate new value propositions in nontransactional buyer-seller exchanges (Hoyer, Chandy, Dorotic, Krafft, & Singh, 2010; van Doorn et al., 2010).

There are three particular items of note to consider regarding this study. First, unlike previous sport manage-ment studies, this work focuses on sport fans’ unique behavioral responses and presents a conceptual model of fan engagement in nontransactional, extrarole behaviors. Second, by analyzing the nontransactional side of sport

consumer behavior, the current study extends previous sport marketing research that is based mainly on the transactional side of exchange. Third, the current study adds to the body of knowledge in the sport management literature, where fan engagement has lacked empirical attention. Given the limitations of previous research, the purposes of this study are to (a) define the concept of fan engagement and develop a scale for measuring the construct, (b) investigate the reliability and validity of the proposed fan-engagement scale, and (c) assess nomological validity by examining the antecedents and consequences of fan engagement. Through a thorough review of the relevant literature, the following section addresses the conceptual background of fan engagement.

Conceptual BackgroundFan engagement is a specific form of customer engage-ment in the sport context. In marketing, researchers define customer engagement as a consumer’s sponta-neous, interactive, and cocreative behaviors primarily in nontransactional consumer-company exchanges to achieve his or her individual and social purposes (Brodie et al., 2011; van Doorn et al., 2010; Verhoef et al., 2010). Because engaged consumers provide referrals and recom-mendations for specific products, customer engagement is a key element in firms’ strategies on solution develop-ment, new product development, and customer retention (Hoyer et al., 2010; Marketing Science Institute [MSI], 2010; Verhoef et al., 2010).

Customer Engagement in the Marketing Literature

Despite its widely recognized importance, there is a considerable amount of confusion regarding the con-ceptualization of customer engagement. Customer engagement has been viewed as a cognitive appraisal, an affective attachment, a behavioral response, or a combination of these (Brodie et al., 2011). Furthermore, customer engagement has been viewed as a global (over-all) or multidimensional construct (Brodie et al., 2011). In this study, we attempt to alleviate the aforementioned conceptual confusion and fill a considerable gap in the literature. A review of the relevant literature reveals that three divergent conceptualizations exist in previous research: (a) cognitive, (b) behavioral, and (c) cognitive/affective/behavioral. In the following section, we first explain the difference between the cognitive and behav-ioral approaches to the conceptualization of customer engagement. Second, we further differentiate between the unidimensional and multidimensional approaches.

The cognitive and behavioral approaches. In the literature, the most widely supported conceptualization of customer engagement is a behavior-based model (MSI, 2010; Schau et al., 2009; van Doorn et al., 2010; Verhoef et al., 2010). Van Doorn et al. (2010) define customer engagement as “a customer’s behavioral manifesta-

Fan Engagement 401

tions that have a brand or firm focus, beyond purchase, resulting from motivational drivers” (p. 254). Engaged consumers’ behavioral manifestations include numer-ous nontransactional behaviors such as word-of-mouth activity, recommendations, consumer-to-consumer interactions, blogging, and writing reviews (MSI, 2010). Furthermore, the valuation model of Kumar et al. (2010) indicates that the nontransactional aspects of customer engagement value include customer referral value, cus-tomer influence value, and customer knowledge value. Following these contentions, researchers have reached similar conclusions that customer engagement reflects a consumer’s nontransactional behavior and is a significant route for creating, building, and enhancing consumer-firm relationships (Hoyer et al., 2010; Libai, Bolton, Bügel, de Ruyter, Gotz, Risselada, & Stephen, 2010; MSI, 2010; Verhoef et al., 2010).

On the contrary, another contemporary view of cus-tomer engagement is based on cognition and is derived from Sprott, Czellar, and Spangenberg’s (2009) “brand engagement in self-concept” (BESC). BESC refers to consumers’ tendency to incorporate their own favorite brands into the self-concept. To measure BESC, their 8-item scale captures the elements of self-brand connec-tion, brand identity, person-brand fit, and self-definition. Although BESC explains the notion that multiple brands are integrated into a consumer’s self-concept, Brodie et al. (2011) criticize that BESC focuses primarily on a consumer’s cognitive attitudes toward brands and fails to capture the defining components of customer engage-ment (e.g., consumer-to-consumer interactions, solution development, and cocreation in service relationships). To properly define the customer-engagement construct, consumers’ interactive, cocreative experiences should be included in its conceptualization as suggested by MSI (2010), van Doorn et al. (2010), Verhoef et al. (2010) and Brodie et al. (2011).

The unidimensional and multidimensional ap-proaches. Another important issue to consider is how customer engagement is measured and whether the construct is unidimensional or multidimensional. The conceptual model of van Doorn et al. (2010) indicates that customer engagement is behavioral and consists of multiple defining indicators such as valence (i.e., positive or negative reactions), modality (i.e., extrarole behavior), scope (i.e., temporal and geographic scopes), impact (i.e., intensity, breadth, and longevity of impact), and consum-ers’ purpose (i.e., engagement objects). In addition, Keller (2003) operationalized the construct of active engagement in regards to a brand using a behavioral approach. Both of these approaches advocate for a unidimensional approach to the measurement of customer engagement. On the other hand, other researchers include cognitive and affective components as well as behavioral reactions in their conceptual models as an illustration of the multidi-mensional approach. For example, the scale development study of Vivek (2009) verifies that customer engagement is a three-dimensional construct, composed of enthusi-

asm (emotion), conscious participation (behavior), and social interaction (behavior). In a more comprehensive manner, several researchers conceptualize that customer engagement is a hybrid construct of cognitive, affective, and behavioral aspects (Brodie et al. 2011; Hollebeek, 2011; Patterson, Yu, & de Ruyter, 2006). According to Patterson et al. (2006), the cognitive aspect reflects absorption (i.e., concentration on an engagement object). The affective dimension is composed of dedication (i.e., sense of belonging to a brand). The behavioral component is represented by vigor (i.e., energy in interacting with a brand) and interaction (i.e., two-way communications).

As noted above, the notable confusion concern-ing the cognitive, affective, and behavioral aspects of customer engagement highlights the importance of a better understanding of the construct and represents a gap in the literature. Most importantly, as suggested by Brodie et al. (2011), the expression of specific cognitive, emotional, and behavioral dimensions varies consider-ably across engagement objects and contexts. In the next section, drawing from the literature on sport marketing, we attempt to derive conceptual support for sport fan engagement.

Customer Engagement in the Sport Marketing Literature

To explain the level of sport fandom and unique behav-ioral patterns in spectator sport, the term “engage” is often used in the sport marketing literature. We conducted a keyword search using the term “engage” within the sport marketing literature and identified publications with customer engagement-related contents. Table 1 presents a summary review of the relevant literature identifying engagement points in the sport context. Although no established scale is available for measuring customer engagement in sport, three main streams of research are relevant to this study: (a) customer engagement in nontransactional behaviors, (b) customer engagement in transactional behaviors, and (c) customer engagement in long-term relationships with a sport team. The behavioral approach to conceptualizing sport consumer engagement is the most widely used, but varies across researchers (see Table 1). The first theme that emerges is the focus on nontransactional behaviors. In sport, engaged consum-ers’ nontransactional behaviors include self-enhancement by basking in reflected glory (BIRGing) and cutting off reflected failure (CORFing; Cialdini, Borden, Thorne, Walker, Freeman, & Sloan, 1976; Cialdini & Richard-son, 1980), displays of sport fandom (Holt, 1995), social interaction (Holt, 1995), play and rituals (Holt, 1995), fan community-related behavior (Dietz-Uhler & Murrell, 1999; Fisher & Wakefield, 1998), performance tolerance (de Ruyter & Wetzels, 2000), pregame tailgating parties (James, Breezeel, & Ross, 2001), sharing knowledge of a game/team (Westerbeek & Shilbury, 2003), supportive word-of-mouth behavior (Swanson et al., 2003), basking in spite of reflected failure (BIRFing; Campbell, Aiken, & Kent, 2004), cutting off reflected success (CORSing;

402 Yoshida et al.

Campbell et al., 2004), and participation in memorable marketing programs (Jowdy & McDonald, 2002). Of these, several behaviors are self-oriented to increase one’s self-esteem and public image (e.g., BIRGing and CORFing), whereas other behaviors are team- and others-oriented and include social, interactive, and collaborative behaviors. The latter examples are extrarole behaviors and are consistent with the general definition of customer engagement (Brodie et al., 2011; MSI, 2010; van Doorn et al., 2010; Verhoef et al., 2010).

The second theme in the literature is customer engagement in transactional behaviors. Research has focused on sport consumers’ transactional behaviors such as attending games (Funk & James, 2001; Hunt et al., 1999; Trail, Fink, & Anderson, 2003), watching games on television (Funk & James, 2001; Hunt et al., 1999), buying team products (Funk & James, 2001; Hunt et al., 1999), purchasing peripheral game-related products (Pritchard & Funk, 2006), and participating in fantasy sports (Hunt et al., 1999). As the third research stream, several researchers also suggest that sport consumers

may maintain a long-term relationship with a sport team (James et al., 2002; Jowdy & McDonald, 2002). James et al. (2002) suggest that sport consumers engage in long-term relationships with their favorite sport teams by forming an emotional and cognitive attachment to the objects. Similarly, Jowdy and McDonald (2002) reported that highly engaged sport fans consume sport with a strong desire for long-term associations with their favorite teams and actively participate in relationship-building programs (e.g., fan loyalty program participa-tion, season-ticket purchase, booster membership). These findings indicate that the relational aspect of customer engagement may be another indicator when investigating the behavior of sport fans.

In summary, a growing body of research has identi-fied the defining characteristics of customer engage-ment in sport. A review of the literature indicates that sport consumers engage in various behaviors, including sport-related behaviors (e.g., attend, read, watch, listen, and purchase), impression-management behaviors (e.g., BIRGing and CORFing), relationship-building behaviors

Table 1 Relevant Engagement Points in the Sport Context

Customer Engagement In Relevant Engagement Points Source

Nontransactional behaviors

Basking in reflected glory (BIRGing) and cutting off reflected failure (CORFing)

Cialdini et al. (1976); Cialdini and Richardson (1980)

Displays of sport fandom, social interactions, play, and rituals Holt (1995)

Behaviors that support a fan community Fisher and Wakefield (1998)

Social mobility, social creativity, social competition in a successful fan community

Dietz-Uhler and Murrell (1999)

Positive word-of-mouth and performance tolerance de Ruyter and Wetzels (2000)

Behavior to support positive attitudes toward a team Bristow and Sebastian (2001)

Pregame tailgating parties James et al. (2001)

Sharing knowledge about a game/team, engaging in social communica-tion in the stands

Westerbeek and Shilbury (2003)

Supportive word-of-mouth behaviors Swanson et al. (2003)

Basking in spite of reflected failure (BIRFing) and cutting off reflected success (CORSing)

Campbell et al. (2004)

Participation in memorable marketing programs Jowdy and McDonald (2002)

Transactional behaviors

Attending games, watching games on television, buying products endorsed by a favorite athlete, or participation in a fantasy sports league via the Internet

Hunt et al. (1999)

Attending games, watching games on television, purchasing team prod-ucts, reading sport magazines and newspapers, and listening to games on the radio

Funk and James (2001)

Attending future sporting events Trail et al. (2003)

Peripheral game-related behavior (e.g., buying sport-related merchan-dise and wearing team clothing)

Pritchard and Funk (2006)

Relationship Maintaining a psychological connection to a sport team James et al. (2002)

Maintaining a long-term relationship with a sport team Jowdy and McDonald (2002)

Fan Engagement 403

(e.g., loyalty program participation, season-ticket pur-chase, and booster membership), and nontransactional extrarole behaviors (e.g., social interaction, word-of-mouth, and participation in marketing programs). In the following study, we address how fan engagement is conceptualized and measured in spectator sport.

Study 1: Conceptualization and Measurement of Fan Engagement

The objectives of Study 1 were to (a) define fan engage-ment in the sport context, (b) generate survey items for the proposed construct, and (c) provide evidence of reliability and validity for the proposed fan-engagement scale.

Conceptualization

On the basis of the literature review (see Table 1), we developed a typology of sport consumers’ engagement behaviors (see Figure 1). Focusing on the two axes of customer activity (transactional or nontransactional) and customer role (in-role or extrarole), we identified four types of engagement behavior in spectator sport: sport-related behaviors, relationship-building behaviors, impression-management behaviors, and fan-engagement behaviors. Although several studies in the sport market-ing literature include transactional consumer behaviors in customer engagement, our focus was primarily on nontransactional exchanges. As shown in Figure 1, transactional behaviors are sport-related (e.g., attend, watch, purchase, and read) or relational (e.g., fan loyalty program participation and season-ticket purchase), and these behaviors can be captured by traditional measures of repurchase behavior, media consumption, merchandising,

and relationship equity. On the other hand, fan engage-ment is a sport consumer’s prosocial behavior (de Ruyter & Wetzels, 2000) that benefits not only a sport team but also team management and other fans in nontransactional exchanges (Ahearne et al., 2005; Bettencourt, 1997; Dholakia, Blazevic, Wiertz, & Algesheimer, 2009).

We further distinguished between in-role and extrarole behaviors. In the sport consumer context, in-role behaviors are behaviors shaped by self-interest (e.g., attending, watching, and reading) when following a sport team. On the other hand, extrarole behaviors are behaviors that are directed toward a sport team and other fans on the basis of a consumer’s moral obligation as a fan (de Ruyter & Wetzels, 2000). To share a collective feeling of success with a sport team and other event attendees, sport fans often engage in prosocial, extrarole behaviors (e.g., positive word-of-mouth, collaborative event attendance, and helping other fans; de Ruyter & Wetzels, 2000). In contrast, spectators who do not have a strong feeling of obligation to support sport teams rarely engage in extrarole behaviors that go beyond self-interested tasks (e.g., attending, watching, and reading). Sport consumers’ self-enhancement tactics (i.e., BIRGing and CORFing) are self-oriented and thereby considered in-role behaviors (see Figure 1).

In this study, we define fan engagement as a sport consumer’s extrarole behaviors in nontransactional exchanges to benefit his or her favorite sport team, the team’s management, and other fans (Ahearne et al., 2005; Bettencourt, 1997; de Ruyter & Wetzels, 2000; Dholakia et al., 2009; van Doorn et al., 2010). Consistent with research in marketing (Ahearne et al., 2005; Bet-tencourt, 1997; Brodie et al., 2011; Dholakia et al., 2009), the elements of management cooperation (i.e., helping team management) and prosocial behavior (i.e., helping

Figure 1 — Four types of engagement behavior in spectator sport.

404 Yoshida et al.

other fans) were included in our conceptual framework. In addition to these two elements, a sport fan’s persistent behavior (i.e., helping a sport team) was included in our model because loyal sport fans’ extrarole behaviors are consistent and stable over an extended period of time regardless of game valence, player performance, team standings, and player transfer to other teams (Funk & James, 2001; Mahony et al., 2000). Similarly, fan engage-ment is an effort-intensive behavior (Brodie et al., 2011) that requires performance tolerance to overcome negative team performance, service failure, and negative infor-mation (de Ruyter & Wetzels, 2000; Yi & Gong, 2013). Stable and persistent fan behavior (as described by perfor-mance tolerance) can improve the atmosphere at games, aid in team performance, positively influence other fans of the team, and increase ticket and merchandising sales. The concept of performance tolerance is grounded in the sport context and will help sport teams prevent fan switching behavior (de Ruyter & Wetzels, 2000; Yi & Gong, 2013). In line with these considerations, we posit that fan engagement is a multidimensional, behavioral construct composed of management cooperation, pro-social behavior, and performance tolerance.

Method

Measurement. The construct of fan engagement consists of three defining attributes: management cooperation, prosocial behavior, and performance tolerance. The concept of management cooperation refers to a sport consumer’s collaborative, constructive participation in the value creation and service delivery process at sporting events (e.g., providing constructive feedback to event personnel to enhance the event experience, assisting event personnel to ensure the safety of spectators at the event site, and abiding by the organizations’ policies regarding ethical fan conduct; Auh, Bell, McLeod, & Shih, 2007; Bettencourt, 1997). The element of prosocial behavior captures the notion that sport consumers engage in network development such as interpersonal and computer-mediated fan-to-fan helping behaviors on behalf of the team (Brodie et al., 2011; Dholakia et al., 2009; van Doorn et al., 2010). The concept of performance tolerance reflects a sport consumer’s engagement by the display of team-related products even during unsuccessful team performance (de Ruyter & Wetzels, 2000). We adapted items from previous research to measure the element of management cooperation (Auh et al., 2007; Bettencourt, 1997). Prosocial behavior was measured with a 4-item scale adapted from Dholakia et al.’s (2009) helping others scale. To measure performance tolerance, a 4-item scale was adapted from De Ruyter and Wetzels’s (2000) performance tolerance scale. Through these processes, 12 items were constructed (see Table 2). The wording was modified to reflect the sport consumer’s view and fan engagement behavior.

Next, the initial items were content analyzed through a procedure based on Tian, Bearden, and Hunter’s (2001)

scale development. The items were submitted to a panel of four sport marketing researchers from four different universities. They were asked to rate each statement as not representative (0), somewhat representative (1), or clearly representative (2) of the construct defini-tion. Items evaluated as clearly representative by three reviewers and no worse than somewhat representative by a fourth reviewer were retained. In addition, the judges were asked to provide suggestions for changing words and phrases in the items. This process eliminated three items, leaving 9 items (see Table 2).

Back translation. To minimize discrepancies between the original and translated instruments, back translation was conducted. The survey instrument was first translated into Japanese by one of the authors. To test the equivalence between the original and Japanese instruments, back-translation into English was conducted by another native of Japan who is also fluent in English. To verify the accuracy of the translation, a U.S.-born American citizen assessed differences in meaning between the original and back-translated instruments. The comparison of the two forms led to the conclusion that both instruments reflect the construct domain.

Pilot study. Before conducting our main study, we pretested the proposed fan-engagement instrument with undergraduate students at a private university in Japan. Our goal at this pilot study was to establish the reliability of the scale. A total of 64 students from sport marketing and sport sponsorship classes were invited to participate in the study. Eleven students were eliminated from the data set because they had not attended any professional sporting events in the past twelve months, leaving 53 subjects (n = 53). The participants rated, on the basis of favorite sport teams and previous experiences at professional sporting events, their engagement levels in management cooperation, prosocial behavior, and performance tolerance on a seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree).

The psychometric properties of the items were assessed through an examination of internal consistency via IBM SPSS (Version 20.0; see Table 2). The item-to-total correlations (ITTC) for all items were greater than the recommended cutoff point of .50 (Zaichkowsky, 1985). Cronbach’s alpha coefficients for management cooperation, prosocial behavior, and performance toler-ance were .86, .85, and .97, respectively. The results indi-cate that the three dimensions were internally consistent, confirming the reliability of the initial measures.

Main study. For the main study, we collected data from spectators at a professional soccer game in a midsized city in east Japan. The soccer club belonged to the Japan professional football league’s (J. League) Division II. Although this project was part of the official marketing research conducted by the J. League, the selection of this club was a matter of convenience. Questionnaires were distributed in the stands before the game started. To collect data as systematically as possible, we used

405

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onal

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406 Yoshida et al.

a mixture of convenience and proportionate sampling that was stratified by both age and gender. Before distributing the questionnaires, 22 trained surveyors observed an assigned block of the stands to estimate the percentages of each gender (male/female) and age group (ages between 18 and 29/ages between 30 and 49/ages of 50 and above) among the spectators. Each surveyor was responsible for distributing 20 self-administered questionnaires according to the estimated gender and age percentages. Of the 431 questionnaires distributed, 428 were returned, for a response rate of 99.3%. Among the 428 forms returned, 26 were rejected because many items were left blank. Thus there was a final usable response rate of 93.3% (402 usable responses). Of the total sample, 65.8% of the respondents were male. The average age of the respondents was 39.9 years old. Age was also classified into six categories. Approximately one third of the subjects were between 30 and 39 years old (30.3%), 28.6% were between 40 and 49 years old, 20.5% were 50 years or older, and 12.8% were between 20 and 29 years old.

Results

Assessment of the measures. The psychometric properties of the items were assessed through a confir-matory factor analysis (CFA) using LISREL (Version 8.8; Scientific Software International, Skokie, IL). The fit of the CFA for Study 1 is acceptable with, χ2 (24) = 56.24, p < .01, χ2/df = 2.35, comparative fit index (CFI) = .99, non-normed fit index (NNFI) = .99, root-mean-square error of approximation (RMSEA) = 0.058, standardized root-mean-square residual (SRMR) = .027 (Hu & Bentler, 1999). Scale statistics, including factor loadings (λ), composite reliability (CR), and average variance extracted (AVE) values, are presented in Table 2. All items were loaded on their respective factors, which ranged from .76 to .99. The CR values for all fac-tors were greater than the recommended cutoff point of .60 (Bagozzi & Yi, 1988), indicating that the proposed constructs were internally consistent. A further assess-ment of construct reliability and discriminant validity was conducted by an examination of the AVE values. The AVE values for the three dimensions ranged from .73 to .95, providing evidence of construct reliability (Fornell & Larcker, 1981). Discriminant validity is indicated when the AVE estimate for each construct exceeds the squared correlations between the respective constructs. In a total of three cases, the AVE values were consider-ably greater than any squared correlations between the dimensions (see Table 2). Thus, discriminant validity was indicated.

Discussion

Study 1 was conducted to develop a conceptual model of fan engagement and to assess the construct validity of the proposed fan-engagement scale with college student and sport consumer samples. The current study included the

three dimensions of management cooperation, prosocial behavior, and performance tolerance that constitute fan engagement. The elements of management cooperation and prosocial behavior are attributed to the emerging customer engagement theory in marketing (Brodie et al., 2011; van Doorn et al., 2010; Yi & Gong, 2013). In addition, on the basis of attitude-strength studies in the sport marketing field (Funk & James, 2001; Mahony et al., 2000), the dimension of performance tolerance was believed to be a critical factor underlying fan engagement in sport. This consideration led to the inclusion of perfor-mance tolerance in our conceptual model. Conceptually, the three-dimensional model reflects the professional sport context, because professional sporting events can create an environment in which sport consumers interact with other fans (Oliver, 1999), cocreate unique experi-ences (Decrop & Derbaix, 2010), and follow both suc-cessful and unsuccessful teams (Mahony et al., 2000). The results in Study 1 support our multidimensional conceptualization.

Study 1 represents the initial effort to empirically test the proposed multidimensional model of fan engagement. Additional effort should be made to ascertain whether the three dimensions and other cognitive (e.g., team identi-fication), affective (e.g., positive affect), and behavioral (e.g., BIRGing) constructs are distinct. In the next study, we attempt to validate the proposed conceptualization using a new sample of sport consumers. Through an examination of the antecedents and consequences of fan engagement, further evidence of construct and nomologi-cal validity will be provided.

Study 2: Assessing Construct and Nomological Validity

The objectives of Study 2 were (a) to provide further evidence of construct validity of the fan-engagement scale and (b) to assess its nomological validity by investigating the antecedents and consequences of fan engagement. Building from the sport management literature, we examine three antecedents (team identification, positive affect, and BIRGing) and two consequences (purchase intention and referral intention) of fan engagement. We attempt to extend previous research by incorporating fan engagement into a traditional framework of sport con-sumer behavior (Madrigal, 1995; Matsuoka, Chelladurai, & Harada, 2003, Trail, Anderson, & Fink, 2005). In the following section, research hypotheses are derived to theorize the role of fan engagement in the formation of sport consumer behavior.

Hypotheses

Previous research on sport consumer behavior has focused on constructs such as team identification (Mad-rigal, 1995; Matsuoka et al., 2003; Trail et al., 2005), positive affect (Harrolle, Trail, Rodriguez, & Jordan, 2010; Trail et al., 2005; Wakefield, Blodgett, & Sloan,

Fan Engagement 407

1996), and BIRGing (Madrigal, 1995; Trail, Kim, Kwon, Harrolle, Braunstein-Minkove, & Dick, 2012). This sec-tion provides our rationale to integrate fan engagement into a traditional framework of sport consumer behavior. Figure 2 shows the preestablished relationships between the traditional constructs. The preestablished paths in the literature are denoted in solid arrows, and the newly hypothesized paths are depicted in dotted arrows (see Figure 2).

Antecedents of fan engagement. In spectator sport, Trail et al. (2005) provide one perspective on the ante-cedents of fan engagement. Viewed broadly, there are two primary routes to drive sport consumption behavior: cognitive and affective. In the sport management litera-ture, team identification and positive affect have been used as significant cognitive and affective predictors of fan loyalty (Madrigal, 1995; Matsuoka et al., 2003; Trail et al., 2005). In this study, team identification is defined as a consumer’s perceived connectedness to a sport team and the tendency to experience the team’s successes and failures as one’s own (Gwinner & Swanson, 2003; Mael & Ashforth, 1992). Positive affect is defined as a pleasurable feeling state that reflects emotions such as

happiness, joy, and contentment when watching sporting events (Mazodier & Merunka, 2012; Wakefield et al., 1996). Sport management researchers have confirmed the predictive power of both team identification and positive affect for behavioral consequences (Matsuoka et al., 2003; Trail et al., 2005; Wakefield et al., 1996). In the current study, we further propose that team identifi-cation and positive affect are each positively related to fan engagement (Figure 2). This proposition stems from the emerging theory of customer engagement in market-ing (van Doorn et al., 2010; Verhoef et al., 2010). Van Doorn et al. (2010) conceptualize that consumer-based antecedents of customer engagement include cogni-tive identity, affective responses, trust, commitment, consumption goals, resources, and perceived costs and benefits. In empirical research, Ahearne et al. (2005) find that consumer-company identification leads to consumer extrarole behaviors such as cooperation, service learn-ing, and advocacy. In addition, Auh et al. (2007) suggest that a consumer’s affective commitment has a positive impact on consumer cooperation behavior. According to these studies, consumers will be more likely to engage in extrarole, cooperative behaviors if they have higher levels of consumer-team identification and greater affective

Figure 2 — Nomogolical validation: assessing the antecedents and consequences of fan engagement.

408 Yoshida et al.

responses to a sport team. Because our research model includes three fan engagement dimensions (management cooperation, prosocial behavior, and performance toler-ance), the following hypotheses are proposed:

Hypothesis 1: Team identification has positive effects on management cooperation (H1a), prosocial behav-ior (H1b), and performance tolerance (H1c).

Hypothesis 2: Positive affect has positive effects on management cooperation (H2a), prosocial behavior (H2b), and performance tolerance (H2c).

We attempt to extend the existing literature by examining the effect of BIRGing on fan engagement. BIRGing refers to a sport spectator’s tendency to publicly display his or her association with a successful sport team (Cialdini et al., 1976). BIRGing may emerge when a person has a sense of accomplishment based on the glory of his or her favorite team and publicizes one’s relationship with the successful team through the display of visible products such as team apparel (Cialdini et al., 1976). Previous research provides a theoretical basis for the relationship between BIRGing and fan engagement. From one perspective, the favorable external image of a company strengthens a consumer’s extrarole behaviors such as cooperation, service learning, and advocacy (Ahearne et al., 2005). BIRGing, like the external image of a company, has been discussed in terms of its role of enhancing one’s external image and self-esteem (Cialdini et al., 1976; Cialdini & Richardson, 1980). According to Cialdini et al. (1976), BIRGing is an image-management tactic that sport consumers often use to display their connections with a highly successful sport team to increase self-esteem. Discussion of customer extrarole behaviors (Ahearne et al., 2005) suggests that customer engagement is not only intrinsically motivated (i.e., driven by team identification and positive affect) but also extrinsically motivated (i.e., BIRGing) in light of the visible social setting in which sport consumers support and cooperate with their favorite sport teams. We test the following hypothesis derived from these arguments:

Hypothesis 3: BIRGing has positive effects on management cooperation (H3a), prosocial behavior (H3b), and performance tolerance (H3c)

Consequences of fan engagement. Recent studies provide support for the impact of fan engagement on behavioral consequences. Conceptually, van Doorn et al. (2010) and Verhoef et al. (2010) contend that higher levels of customer engagement will lead to greater repeat purchase and consumption in transactional exchanges. In addition, it is expected that customer engagement will be positively related to a consumer’s friend-invitation process (Kumar et al., 2010). More engaged consumers are more likely to invite friends to future sporting events. On the basis of this discussion, we attempt to empiri-cally examine the impact of fan-engagement behaviors (i.e., management cooperation, prosocial behavior, and performance tolerance) on a consumer’s purchase and

referral intentions. Therefore, the following hypotheses are derived:

Hypothesis 4: Management cooperation has posi-tive effects on purchase intention (H4a) and referral intention (H4b).

Hypothesis 5: Prosocial behavior has positive effects on purchase intention (H5a) and referral intention (H5b).

Hypothesis 6: Performance tolerance has positive effects on purchase intention (H6a) and referral intention (H6b).

Method

Measurement. To measure the three dimensions of fan engagement, the same measures used in Study 1 were administered in Study 2. To measure team identification (Trail & James, 2001), positive affect (Mazodier & Merunka, 2012), BIRGing (Trail et al., 2012), purchase intention (Yoshida & James, 2010; Yoshida, James, & Cronin, 2013a), and referral inten-tion (Wangenheim & Bayón, 2007), we adapted items used to measure the constructs from previous research (see Appendix A). Fan engagement, team identification, positive affect, BIRGing, and purchase intention were measured with a seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Refer-ral intention was measured with a single-item scale by asking respondents how many individuals they intend to invite to the soccer club’s future games in the cur-rent season.

Data collection. In Study 2, data were collected from spectators attending another J. League Division II game in a large city in western Japan. As in Study 1, we used a mixture of convenience and proportionate sampling that was stratified by both age and gender. Questionnaires were distributed in the stands before the start of the game. Before distributing the questionnaires, 17 trained surveyors observed an assigned block of the stands to estimate the percentages of each gender (male/female) and age group (ages between 18 and 29/ages between 30 and 49/ages of 50 and above) among the spectators. Each surveyor was responsible for distributing 30 self-administered questionnaires according to the estimated percentages of gender and age.

Of the 500 questionnaires distributed, 493 were returned, for a response rate of 98.6%. Among the 493 forms returned, 21 were rejected because many items were left blank, which yielded a final usable response rate of 94.4% (n = 472). Of the total sample, 64.6% of the respondents were male. The average age of the respondents was 40.77 years old. Age was also trans-formed into a categorical variable. Approximately one third of the subjects were between 40 and 49 years old (31.4%), 26.7% were 50 years old and above, 18.9% were between 30 and 39 years old, and 18.0% were between 20 and 29 years old. The correlations between observed

Fan Engagement 409

variables and the raw means and standard deviations for all survey items are shown in Appendix B.

Results

Assessment of the measures. Through a CFA using LISREL (Version 8.8), we assessed the psychometric properties of the scale items. The fit indices indicate the measurement model is an acceptable fit to the data, (χ2 (182) = 467.41, χ2/df = 2.57, CFI = .99, NNFI = .98, RMSEA = 0.058, SRMR = .036). Scale statistics, including factor loadings (λ), CR, and AVE, are presented in Table 3. All items were loaded on their respective constructs, and the factor loadings for the latent constructs ranged from .70 to .99. The CR values for all factors were greater than the recommended cutoff point of .60 (Bagozzi & Yi, 1988). A further assessment of construct reliability and discriminant validity was conducted by an examination of AVE values. The AVE values for the proposed constructs ranged from .65 to .93, providing evidence of construct reliability (Fornell & Larcker, 1981). Discriminant validity was assessed by comparing the AVE estimate for each construct with the squared correlations between the respective constructs (see Table 3). In all cases, the AVE values were considerably greater than any squared correlations between all pairs of the constructs. Therefore, discriminant validity was indicated (Fornell & Larcker, 1981).

Hypothesis testing. Table 4 shows the results of hypothesis testing. The hypothesized model demonstrated an acceptable fit to the data, χ2 (186) = 500.65, χ2/df = 2.71, CFI = .99, NNFI = .98, RMSEA = 0.059, SRMR = .044. The effects of team identification on BIRGing, management cooperation, prosocial behavior, performance tolerance, and purchase intention were positive and significant. The impact of team identification on referral intention was not significant. These results provided support for H1a, H1b, and H1c. As an affective antecedent, the construct of positive affect had significant influences on BIRGing, management cooperation, performance tolerance, and purchase intention, whereas positive affect had no significant impact on prosocial behavior and referral intention. Thus, H2a and H2c were supported, whereas H2b was rejected. Furthermore, we tested the impact of BIRGing on the fan engagement dimensions. The findings indicate that BIRGing positively influences management cooperation, prosocial behavior, and referral intention in support of H3a and H3b. However, when performance tolerance and purchase intention were the endogenous variables, BIRGing did not approach statistical significance.

Moreover, we examined the effects of the three dimensions of fan engagement on behavioral conse-quences. The results indicate that purchase intention is positively influenced by management cooperation and performance tolerance, and referral intention is positively affected by prosocial behavior. On the other hand, the

effects of management cooperation and performance tolerance on referral intention were nonsignificant. In addition, the impact of prosocial behavior on purchase intention was nonsignificant. When taken together, H4a, H5b, and H6a were supported, but H4b, H5a, and H6b were not supported. The ability of the exogenous variables to explain variations in the endogenous variables was assessed by R2 values (see Table 4). The R2 values for BIRGing, management cooperation, prosocial behavior, performance tolerance, purchase intention, and referral intention were .33, .64, .34, .47, .69, and .07, respectively.

Discussion

In assessing the magnitude of the path coefficients (Cohen, 1988), the results in Study 2 are meaningfully supportive of five hypotheses (H1a, H1c, H3a, H3b, and H6a) and help us identify the antecedents and conse-quences of fan engagement and generalize the findings to a context that involves actual sport consumer behaviors. The results indicate that (a) team identification is an important precursor of all the dimensions of fan engage-ment, (b) BIRGing is a predictor of management coopera-tion and prosocial behavior, and (c) positive affect is a statistically significant but not meaningful antecedent of management cooperation and performance tolerance (the amount of explained variance < 6%; Cohen, 1988). Study 2 explained the relationships between team identification, positive affect, BIRGing, and fan engagement.

With respect to the consequences of fan engagement, our study shows how a consumer’s purchase intention is influenced by the fan-engagement dimensions and other predictor variables. The findings indicate that performance tolerance has a positive effect on purchase intention even if the simultaneous effects of positive affect and team identification are examined. In summation, the results of Study 2 support the notion that team identifica-tion, positive affect, BIRGing, and the fan-engagement dimensions are distinct constructs that have significant effects on purchase intention and nonsignificant impact on referral intention.

General DiscussionAlthough research investigating issues on sport con-sumers’ engagement behavior spans nearly 30 years (see Table 1), we are still trying to understand how and why it works. The results of this study further extend our understanding by testing a model that integrates fan engagement into a traditional sport consumer behavior framework. Because examining the nontransactional, behavioral facets of fan-engagement behaviors goes beyond traditional sport management concepts such as team identification, positive affect, and BIRGing, the current study contributes significantly to the literature and practice in four different ways.

First, we synthesized the recent conceptual develop-ment of customer engagement in marketing (Brodie et

410

Tab

le 3

D

escr

ipti

ve S

tati

stic

s, ϕ

Mat

rix,

an

d t

he

Co

nfir

mat

ory

Fac

tor

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alys

is R

esu

lts

ϕ

Mat

rixa

(n =

472

)

Con

stru

ct1

23

45

67

8λ

CR

AVE

1. T

eam

iden

tifica

tion

1.00

.36

.30

.53

.19

.45

.56

.03

.83–

.96

.93

.81

2. P

ositi

ve a

ffec

t.6

0**

1.00

.21

.34

.11

.24

.33

.01

.88–

.95

.94

.85

3. B

IRG

ing

.55*

*.4

6**

1.00

.41

.30

.19

.21

.05

.88–

.96

.95

.85

4. M

anag

emen

t coo

pera

tion

.73*

*.5

8**

.64*

*1.

00.3

3.2

7.3

9.0

2.7

5–.9

0.8

8.7

15.

Pro

soci

al b

ehav

ior

.43*

*.3

3**

.55*

*.5

8**

1.00

.18

.15

.04

.70–

.88

.86

.68

6. P

erfo

rman

ce to

lera

nce

.67*

*.4

9**

.44*

*.5

2**

.43*

*1.

00.5

6.0

3.9

5–.9

9.9

8.9

37.

Pur

chas

e in

tent

ion

.75*

*.5

8**

.46*

*.6

2**

.39*

*.7

5**

1.00

.02

.77–

.86

.85

.65

8. R

efer

ral i

nten

tionb

.18*

*.1

1*.2

2**

.14*

*.2

1**

.17*

*.1

5**

1.00

1.00

--

Mea

nc5.

105.

494.

934.

603.

104.

855.

502.

79

SDc

1.55

1.13

1.46

1.30

1.60

1.95

1.43

2.47

Not

es. *

p <

.05.

**p

< .0

1. χ

2 (18

2) =

467

.41,

χ2 /

df =

2.5

7, c

ompa

rativ

e fit

inde

x =

.99,

non

-nor

med

fit i

ndex

= .9

9, ro

ot-m

ean-

squa

re e

rror

of a

ppro

xim

atio

n =

0.0

58, s

tand

ardi

zed

root

-mea

n-sq

uare

resi

dual

=

.027

. BIR

Gin

g =

bas

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in r

eflec

ted

glor

y.a

Cor

rela

tions

are

take

n fr

om ϕ

mat

rix

usin

g L

ISR

EL

(V

ersi

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cien

tific

Soft

war

e In

tern

atio

nal,

Skok

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L)

and

are

repo

rted

in th

e lo

wer

tria

ngle

of

the

ϕ m

atri

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quar

ed c

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latio

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re d

epic

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uppe

r tr

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f th

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mat

rix.

b T

he c

onst

ruct

of

refe

rral

inte

ntio

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as m

easu

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on a

sin

gle-

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sca

le (

“The

num

ber

of r

efer

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I w

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ake

for

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fut

ure

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this

sea

son”

).

c The

mea

n sc

ores

and

sta

ndar

d de

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PSS

Stat

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s (V

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0.0)

.

Fan Engagement 411

al., 2011; van Doorn et al., 2010; Verhoef et al., 2010) and the defining attributes of sport fans’ engagement behavior (e.g., de Ruyter & Wetzels, 2000; Fisher & Wakefield, 1998; Holt, 1995). Traditionally, the con-cept of customer engagement has been viewed as both a transactional and a nontransactional behavior in the sport marketing literature (see Table 1). However, given the theoretical perspective of Verhoef et al. (2010), we excluded transactional consumer behavior from the idea of fan engagement and conversely included the ele-ments of management cooperation, prosocial behavior, and performance tolerance behaviors in our model (see Figure 2). We added the dimension of performance tol-erance to the conceptualization (de Ruyter & Wetzels, 2000) because enthusiastic sport consumers engage

in conspicuous displays of sport fandom to maintain their commitment to a favorite team even if the team is unsuccessful and performs poorly (Funk & James, 2001; Mahony et al., 2000). Incorporating the theoreti-cally relevant element grounded in sport phenomena will advance our understanding of fan engagement in spectator sport.

As a second contribution, the current study repre-sents one of the first attempts to validate the idea of fan engagement in the sport management field. Past research predominantly viewed customer engagement as a global construct (Keller, 2003; Schau et al., 2009; Sprott et al., 2009). Although a more recent study (Brodie et al., 2011) demonstrated a multidimensional approach to the conceptualization of customer engagement, there has

Table 4 Nomological Validation: Hypothesis Testing

PathStandardized Path

Coefficient Hypothesis Testing

Team identification → BIRGing .43** (7.78)

Team identification → Management cooperation .47** (9.41) H1a Supported

Team identification → Prosocial behavior .19** (3.22) H1b Supported

Team identification → Performance tolerance .55** (10.33) H1c Supported

Team identification → Purchase intention .33** (5.00)

Team identification → Referral intention .09 (1.02)

Positive affect → BIRGing .20** (3.83)

Positive affect → Management cooperation .15** (3.45) H2a Supported

Positive affect → Prosocial behavior .02 (.29) H2b Not supported

Positive affect → Performance tolerance .13** (2.66) H2c Supported

Positive affect → Purchase intention .12** (2.63)

Positive affect → Referral intention –.02 (–.33)

BIRGing → Management cooperation .32** (7.52) H3a Supported

BIRGing → Prosocial behavior .44** (8.11) H3b Supported

BIRGing → Performance tolerance .08 (1.78) H3c Not supported

BIRGing → Purchase intention –.03 (–.56)

BIRGing → Referral intention .16* (2.25)

Management cooperation → Purchase intention .13* (1.99) H4a Supported

Management cooperation → Referral intention –.12 (–1.35) H4b Not supported

Prosocial behavior → Purchase intention –.03 (–.71) H5a Not supported

Prosocial behavior → Referral intention .13* (2.11) H5b Supported

Performance tolerance → Purchase intention .43** (8.81) H6a Supported

Performance tolerance → Referral intention .07 (1.03) H6b Not supported

R2 Fit indicesBIRGing .33 χ2(df) 500.65(186)

Management cooperation .64 χ2/df 2.71

Prosocial behavior .34 CFI .99Performance tolerance .47 NNFI .98

Purchase intention .69 RMSEA .059

Referral intention .07 SRMR .044Notes. *p < .05. **p < .01. BIRGing = basking in reflected glory. CFI = comparative fit index. NNFI = nonnormed fit index. RMSEA = root-mean-square error of approximation. SRMR = standardized root-mean-square residual.

412 Yoshida et al.

been a lack of empirical research on the dimensionality of customer engagement. In this research, measures for the three dimensions of fan engagement (management cooperation, prosocial behavior, and performance tolerance) were generated, refined, and validated with spectators attending at two professional soccer events. The factor analyses in Study 1 and Study 2 were sup-portive of the convergent and discriminant validity of the proposed fan-engagement scale.

Our third major finding is that team identification and BIRGing are the dominant factors in enhancing fan engagement (see Table 4). Team identification was found to be a significant predictor of management cooperation, prosocial behavior, and performance tolerance. It is also worth noting that BIRGing had a more powerful effect on the dimension of prosocial behavior than team iden-tification. However, the construct of positive affect had only small effects on the fan-engagement dimensions. This means that team identification and BIRGing play a particularly important role in increasing consumers’ level of fan engagement.

Fourth, this research explains the important mediat-ing role of performance tolerance in the development of loyalty intentions. The results of hypothesis testing (see Table 4) indicate that the effects of performance toler-ance and team identification on purchase intention are positive and significant. This research also reveals team identification to have a significant effect on performance tolerance. Thus, this study extends previous team identi-fication theory by suggesting that performance tolerance may partially mediate the relationship between team identification and purchase intention.

For practitioners, some conclusions can be drawn that may better inform their marketing decisions. The results from this article clearly demonstrate that pur-chase intention is affected by fan-engagement behaviors. Engaging consumers in extrarole behaviors will enhance the likelihood of repeat purchasing. By providing extrarole engagement opportunities for consumers (e.g., consumer-to-consumer interactions, voluntary participa-tion in marketing programs, collaborative product cus-tomization, and reciprocal service learning and delivery), sport marketers may be able to contribute to increasing the profitability of engaged consumers. This is especially important with the proliferation of social media, where sport organizations can engage their fan base as well as foster fan-to-fan social interaction.

Given the significant impact of fan engagement on sport consumer behavior, sport marketers should begin to measure fan engagement to monitor and benchmark the level of engagement among their target fan base. This information can be used and integrated into marketing strategies. For example, sport marketers can monitor fan engagement to assess the impact of operational changes (e.g., merchandise mix, facilities, fan loyalty programs, fan communities, and social media) on fans’ engagement level. In addition, sport leagues should consider measur-ing fan engagement of all teams to evaluate which teams

are successful or unsuccessful in engaging consumers and eventually for increasing profitability.

Limitations and Directions for Future Research

Several limitations may influence the results of this study. First, the proposed fan-engagement scale was tested in a spectator sport setting. The lack of applicability of the findings to other settings should be acknowledged. The factor structure depends on the context, and the defining attributes may change over time. Additional effort should be made to identify which engagement points will have greater importance in other settings. The relevance of the engagement dimensions varies across sports (e.g., ama-teur and professional sports), consumption types (e.g., spectator and participant sports), athletic levels (Division I and Division II), and event types (e.g., recurring events, annual events, and participant- versus spectator-driven events).

Second, results of the current study explained only 7% of the variance in referral intention. Although the effects of BIRGing and prosocial behavior approached statistical significance, these influences are not mean-ingful because of the small effect size (Cohen, 1988). On the other hand, as marketing researchers have been interested in both self-brand connection and communal-brand connection (e.g., Rindfleisch, Burroughs, & Wong, 2009), there might be two different types of sequential relationships: (a) the relationships among team identifica-tion, performance tolerance, and purchase intention on the basis of self-team connection and (b) the relationships among BIRGing, prosocial behavior, and referral inten-tion on the basis of communal-team connection. Future research should use more accurate measures of referral intention to improve the predictive power of BIRGing and prosocial behavior for referral intention.

Third, we did not examine how consumer behav-ior is contingent on consumer characteristics such as demographic, psychological, and relational moderators (Yoshida & Gordon, 2012). A suggestion for future research is to examine the moderating effects of these demographic, psychological, and relational variables on the relationship between nontransactional fan engage-ment behavior and transactional consumer behavior.

The fourth limitation might be the omission of important variables. For example, we examined only purchase and referral intentions in Study 2 and did not include additional behavioral consequences. As shown in Table 1, transactional sport consumer behavior contains a variety of game-related (e.g., attend, read, watch, listen, purchase, BIRFing, and CORSing) and relationship-building (e.g., fan loyalty programs, season-ticket pur-chase, fan appreciation day participation, and premium seating) activities. Future research should address the relationship between fan engagement and various trans-actional behaviors.

Fan Engagement 413

Finally, the effects of positive affect on fan engage-ment and loyalty intentions were weak or nonsignificant in the context of professional soccer. On the contrary, Yoshida, James, and Cronin (2013b) found that the major predictor of consumer behavioral intentions is positive affect in the college football setting. If the impact of posi-tive affect varies across American and Japanese sports, the weak or nonsignificant effects of positive affect on behav-ioral consequences might be implausible in American sport. This limitation reinforces an earlier point regarding the need to test these ideas in a variety of sport contexts.

This study was driven by important research ques-tions, including how fan engagement is conceptualized and measured in the sport setting and the roles of fan engagement in a framework of sport consumer behavior. Conceptualizing the nontransactional side of extrarole consumer behavior, the proposed fan-engagement construct extends previous research that has primarily focused on the transactional aspect of sport consumer behavior. The fan-engagement scale developed here provides numerous opportunities to continue advancing our knowledge of sport consumer behavior.

Acknowledgment

This research was funded by the Japan Professional Football League (J. League).

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416

Appendix A Survey Items in Study 2

Construct Item λ CR AVE

Team identification .93 .81

I consider myself to be a “real” fan of (team name). .83I would experience a loss if I had to stop being a fan of (team name). .90Being a fan of (team name) is very important to me. .96

Positive affect .94 .85

Watching games of (team name) at this stadium makes me happy. .94Watching games of (team name) at this stadium gives me pleasure. .95I feel good when I watch games of (team name) at this stadium. .88

BIRGing .95 .85

I would like to let others know about my association with (team name) when the team wins. .88I would like to publicize my connection with (team name) when the team plays really well. .96I would like to tell others about my association with (team name) when the team performs well. .93

Management cooperation .88 .71

I try to work cooperatively with my team. .90I do things to make my team’s event management easier. .87The employees of (team name) get my full cooperation. .75

Prosocial behavior .86 .68

I often interact with other fans to talk about issues related to (team name). .87I often advise other fans on how to support (team name). .88I spend time on social media (e.g., Facebook, Twitter) sharing information with other fans of (team name).

.70

Performance tolerance .98 .93

I wear apparel which represents the fans of (team name) even if the team has an unsuccessful season. .95I display the logo of (team name) on my clothing even if (team name) do not perform well. .99I wear clothing that displays the name of (team name) even if (team name) have an unsuccessful season. .95

Purchase intention .85 .65

The probability that you will attend another sporting event of (team name) is (“very low/very high”). .78The likelihood that you would actively buy additional products (apparel and goods) from (team name) is (“very low/very high”).

.86

The probability that you will spend more than 50% of your spectator sports budget on (team name) is (“very low/very high”).

.77

Referral intention - -

The number of referrals I will make for attending (team name)’s future games in this season is ____.a 1.00

Notes. a Referral intention was measured by a single item that asked the number of referrals.

417

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.39

.38

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9. B

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3.5

1.4

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0.3

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2.4

0.8

1.9

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1.6

1.6

0.6

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5.4

8.4

9.5

4.5

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31.

0011

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2.5

1.5

1.5

7.3

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3.4

2.4

4.4

7.4

5.8

01.

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3.4

6.4

5.5

0.3

0.3

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0.6

6.6

91.

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. PB

1.4

8.3

8.4

0.2

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0.4

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41.

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. PB

2.3

8.3

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4.2

3.2

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4.4

2.4

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. PB

3.3

6.2

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8.2

1.2

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. PT

1.6

1.5

3.6

2.3

8.4

3.4

2.3

7.3

9.3

8.4

9.4

6.3

3.3

8.3

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81.

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. PT

2.5

8.5

0.5

9.4

0.4

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3.3

5.3

8.3

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7.4

4.3

1.4

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51.

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. PT

3.5

7.5

0.5

8.4

1.4

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8.4

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3.9

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. PI1

.48

.48

.52

.38

.42

.40

.30

.31

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.46

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.28

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.21

.21

.55

.54

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1.00

20. P

I2.5

2.5

2.5

5.3

7.4

3.4

1.3

3.3

7.3

7.4

9.4

7.3

4.3

7.2

7.2

3.6

4.6

6.6

5.6

81.

0021

. PI3

.56

.58

.64

.40

.43

.37

.31

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.32

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.30

.25

.20

.62

.62

.61

.60

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1.00

22. R

I.2

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.30

.27

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1.45

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1.71

1.92

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1.99

1.45

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