Beyond the Jersey: How National Bias Fuels Player Identification and Reshapes Sports Consumption

Behavioral intentions have long been recognized as reliable predictors of actual actions. For example, Yoshida (2017) examined the interplay of social and psychological factors in shaping spectators’ intentions to attend sporting events and their overall demand. More recently, Quansah (2022) demonstrated how aspects of sports facilities influence individuals’ intentions to attend football matches in sub-Saharan Africa. Farrag and Althawadi (2022) created a motive-driven classification system to build a comprehensive framework for predicting AI among tennis spectators. Nevertheless, examination of AIs specifically within the context of identification is limited.

Technological advances have significantly expanded sports media consumption, enabling fans to access content through both traditional and digital platforms (e.g., social media, mobile apps). This transformation has led to significant financial gains; for instance, the NFL’s media rights alone brought in $7.75 billion in 2018 (Solberg & Gaustad, 2022). Consequently, research increasingly examines media consumption habits, revealing that identification correlates with fans’ use of diverse media to follow teams (Kim & Manoli, 2022) and corresponds with platform-specific engagement campaigns such as Twitter fan initiative (Clark & Maher, 2019). Although studies on ethnic nationalism have linked it to fan identification and consumption behaviors (Devlin & Billings, 2016), SFE has received relatively little attention as a potential antecedent of these outcomes (Chiu & Won, 2020).

Sports clubs and leagues rely heavily on LMCI as a key revenue stream and a vehicle for reinforcing brand messages to their fan base (Kim et al., 2011). Kwon et al. (2022) highlight that the strength of the consumer-team relationship is the primary predictor of LMCI across diverse merchandise categories. Accordingly, contemporary sports marketing research has increasingly emphasized licensed sports merchandise, particularly investigating how TI is linked to fans’ intentions to buy officially sanctioned products (Chiu & Won, 2020).

Tajfel and Turner’s SIT (1986) describes self-categorization as the mental mechanism through which people sort themselves into separate groups, guided by attributes like ethnicity, cultural background, gender alignment, or common benefits. These social groups comprise individuals sharing similar social identities or perceiving common group membership (Abrams & Hogg, 1988), a process fostering ethnocentrism (Shimp & Sharma, 1987). Central to social identity is the capacity to classify oneself and others into “in-groups” and “out-groups.” Consequently, SIT offers a suitable conceptual framework for investigating the association of ethnocentrism with diverse behavioral outcomes (Sharma et al., 1995).

At its core, ethnocentrism is defined as the propensity for individuals to prioritize their group and see themselves as a higher level than other groups. Since the 1980s, “ethnocentrism” has been widely explored within marketing research. The term “consumer ethnocentrism” was coined by Shimp and Sharma. (1987), Research has shown that ethnocentric consumers favor locally produced goods over foreign alternatives, motivated by favorable attitudes and stronger purchase intentions toward domestic products (Sharma et al., 1995).

Hu and Bedford (2012) introduced the concept of SFE, suggesting that fans may show ethnocentric behavior similar to consumers who prefer domestic products. Just as ethnocentric consumers are more likely to choose local goods, supporters who believe their nation’s players are superior may be more motivated to follow those athletes’ performances (Chiu et al., 2015; Chiu & Won, 2020). This belief can foster a strong psychological connection with national players, potentially influencing fan behavior. For instance, Chiu et al. (2015) reported a positive association between ethnocentrism and the intention to watch games among Korean and Taiwanese-based baseball supporters. Similarly, Shin et al. (2019) found that 1.5 and second-generation settlers were more engaged with sports media when athletes matched their racial identity. However, not all findings are consistent, as Chiu and Won (2020) noted that SFE did not significantly predict MCI among Taiwanese fans. These mixed results provide a rationale for proposing the hypothesis.

H1: SFE has a significant positive association with fans’ intention to consume media.

Identification often entails forming an emotional connection with a specific player or team, influencing how fans engage with and support them (Wu et al., 2012). SIT (Tajfel & Turner, 1986) explained identification, ethnocentrism, and in-group preference. The preference for the “we-group” over the “other-group” is shared by identification and ethnocentrism. Empirical data showed a significant correlation between ethnocentrism and the varied levels of identification with a specific entity (Devlin & Billings, 2016). In other words, ethnocentrism can predict the extent to which individuals feel connected to a player or team. Strong ethnocentric attitudes are often related to higher levels of identification, particularly with athletes who share their national or cultural background (Chiu & Won, 2020). Therefore, fans who identify more strongly with their ethnic group are more inclined to do so. Still, it has been suggested that sports fans form distinct types of identification due to their varied devotion to their favorite teams and athletes (Trail et al., 2003). In order to better understand the function of SFE in establishing various forms of identification, this study took into account both TI and PI. As a result, the following hypotheses were established:

H2: SFE has a significant positive association with the fans’ PI.

H3: SFE has a significant positive association with the fans’ TI.

SIT posits that group membership critically corresponds with numerous behaviors (Tajfel & Turner, 1986), with sports TI representing a form of generalized social identification (Tajfel & Turner, 1979). Highly identified (passionate) fans are more likely to engage in sports-related consumption, as James and Trail (2008) noted. Empirical research consistently shows that higher levels of identification are closely linked to increased consumption activities (Carlson et al., 2009; Chiu & Won, 2020; Clark & Maher, 2019; James & Trail, 2008).

For example, Stathopoulou et al. (2022) identified a positive link between TI and attendance among Sub-Saharan African fans. Lee (2021) found a strong positive relationship between team identity and purchase intention among South Korean sports fans. Similarly, James and Trail (2008) demonstrated that club identification serves as a significant predictor of AI, MCI, and LMCI among MLB fans in the southwestern US. These collective findings support the assumptions

H4: TI is significantly and positively associated with fans’ AIs.

H5: TI has a significant and positive link with fans’ MCIs.

H6: TI has a significant and positive association with fans’ intention to purchase licensed products.

This study’s review of identification literature reveals prestige and distinctiveness as fundamental characteristics driving identification toward individuals or brands. Consistent with SIT, individuals are inclined to associate with figures demonstrating prestige and distinctiveness, including athletes with robust brand personalities (Gwinner & Swanson, 2003). Reflecting this framework, Carlson and Donovan (2013) operationalized PI using these two traits. Prior scholarship also indicates that brand personality can predict TI and purchase intention (Carlson et al., 2009).

While TI has traditionally received considerable attention, a growing body of research points to the unique contribution of PI in forecasting various aspects of fan action. For example, Wu et al. (2012) found that while TI generally exerted a more direct association with loyalty, PI was significantly but indirectly linked to behavioral outcomes. Carlson and Donovan (2013) reported a positive correlation between PI, game attendance, and merchandise consumption among American football fans. Similarly, Chiu and Won (2020) demonstrated that PI strongly predicted media consumption among Taiwanese baseball fans. Moreover, consumers often purchase athlete-branded items, such as signature sneakers or apparel, to express support for individual players. Therefore, the following hypotheses are proposed:

H7: Sports fans’ PI has a significantly positive association with their AI.

H8: Sports fans’ PI has a significantly positive association with their intention for media consumption.

H9: Sports fans’ PI has a significantly positive association with their intention to purchase licensed merchandise.

The interconnection between PI and TI is well-documented. Wann, Tucker, and Schrader (1996) postulated that fan allegiance to a team often stems from identification with specific players. Moreover, identification with a broader group, such as a sports team, may be predicted by identification with a related individual or subgroup, such as a specific player or unit within the team (Carlson & Donovan, 2013). Empirical evidence consistently confirms a positive association with PI on TI (Carlson & Donovan, 2013; Lock & Heere, 2017; Wu et al., 2012). Based on this body of research, the following hypothesis is proposed:

H10: The extent of TI among sports fans is significantly and positively associated with their PI.

The theoretical model is shown in Figure 1.

Figure 1

Theoretical model.

Delhi Capitals, a franchise in the IPL, was selected for the context of TI in this study. The IPL, a premier Twenty20 cricket competition featuring eight city-based teams, attracts top global talent. Its brand was valued at US$6.7 billion, and its broadcasting revenue now competes with leagues like the NFL, EPL, and NBA.

Mustafizur Rahman, an internationally recognized Bangladeshi bowler, was chosen to represent PI and SFE. Since entering the IPL in 2016, he has claimed 46 wickets while maintaining an economy rate of 7.80. In 2022, playing for the Delhi Capitals, he secured eight wickets at 7.63. As the only Bangladeshi in the 2022 IPL and a national icon, Mustafizur was a fitting representative for exploring SFE and PI.

The respondents comprised Bangladeshi citizens aged 15 years or older who actively followed IPL, were aware of Mustafizur Rahman’s participation in the Delhi Capitals, and had viewed at least one match of the 2022 IPL season. This age threshold was selected so that the maximum active fans are included, and respondents could answer questions meaningfully. The criteria are logical as they support the study’s focus on the fan group who have exposure to the league and the focal player, and are supported by established prior studies such as Chiu and Won (2020) and Hu and Bedford (2012).

Convenience sampling was conducted in public places, including university campuses, shopping centers, and parks, in Noakhali and Chittagong City. Respondents were selected from these two major cities of Bangladesh because of the availability of eligible participants and accessibility. The data were collected between September 4 and 18, 2022, and the time period was immediately after the IPL 2022 season, so that respondents could easily recall the event and minimize recall bias.

Potential participants were selected based on two criteria. First, Participants must be Bangladeshi citizens, and second, they must have viewed at least one match of the Delhi Capitals featuring Mustafizur Rahman during the 2022 season. Eligible respondents were informed about the purpose of the research and ensured confidentiality. Questionnaires were provided in English and Bengali, with expert-reviewed translations. Informed consent was obtained from all participants. Parental consent was obtained for respondents under 18 years of age. Of the 600 distributed questionnaires, 488 valid responses were retained after removing 112 incomplete responses. Most respondents were male (88.5%), aged 20–29 (60.2%), and earning under BDT 20,000 annually (46.7%). These characteristics align with prior research showing sports fans are predominantly young males (Chiu & Won, 2020).

Demographic details are presented in Table 1.

The research items were adapted from prior literature. SFE measures were modified from Hu and Bedford (2012). Three items from Chiu and Won (2020) and Trail et al. (2003) were adapted for player and TI, respectively. MCI used three items from Kim et al. (2011), while AI included two from Trail et al. (2005) and one from Kwon et al. (2007). Licensed merchandise intention was measured using items adapted from Kwon et al. (2007) and Kim et al. (2011). Items were randomized to reduce order bias and rated on a 5-point Likert scale (1 strongly disagree to 5 strongly agree).

The proposed model is significant because it addresses three crucial aspects that were missing in the prior fandom research. First, previous studies predominantly used only TI as an antecedent of consumption (Trail et al., 2005; Wann & Branscombe, 1993), neglecting PI as a distinct predictor. At the same time, PI could be crucial in the franchise-based tournaments. Second, although in some studies, SFE was positioned as a predictor of identification, it was overlooked in its differential association with PI and TI (Chiu & Won, 2020; Hu & Bedford, 2012). Third, previous models typically positioned consumption behavior as a single dimension construct, ignoring consumption patterns dictated by the level of commitment. For example, attendance requires consumers’ high commitment, whereas media consumption, such as watching a live game on a digital device, requires the lowest commitment.

The theoretical model extends the scope of prior relevant fandom models. For example, Carlson and Donovan (2013) examined the Brand personality connection with athlete identification and subsequent consumption behavior, which assessed PI, but no connection was established with SFE. Similarly, Chiu and Won (2020) investigated SFE connection for identification and media consumption only, overlooking the behavioral diversity predicted by commitment and ignoring PI’s mediation role between SFE and TI. The current model positions PI as a crucial factor in the identification formation in the global context and its subsequent direct and indirect roles, overcoming prior models’ scope constraints.

The inclusion of IPMA further strengthens the model and its implications. While PLS-SEM indicates relationship significance, IPMA complements the structure by indicating which construct provides the greatest marginal return. This combination is crucial as it connects theoretical significance with actionable implications. This dimension is missing in the similar prior models (Kim et al., 2011; Trail et al., 2005).

Within the social identity framework, although TI and PI are related constructs, conceptually they are distinct entities or targets of fan loyalty. PI indicates a fan’s psychological link with an individual player who acts as a “human brand” and is derived from perceived prestige, distinctiveness, or parasocial attachment (Carlson & Donovan, 2013). On the contrary, TI is derived from fans’ psychological connection with a collective organization that stems from its rich history, belongingness, contribution, or community involvement (Mael & Ashforth, 1992). The distinction is even more significant in the current study context. With regard to the global franchise league (IPL) organized by India, Bangladeshi fans identify themselves with Mustafizur Rahman on the basis of common nationality and national pride, which is different from the basis of TI with the Delhi Capitals, which is a distant team with no cultural or community connection. For example, a fan might celebrate player success (taking a wicket or being awarded the best player) even if the team loses (weak attachment with the team), whereas another fan might be disappointed with the overall performance of the team even though he had no player attachment. This conceptual difference is further supported by the current study result of discriminant validity (Fornell–Larcker criterion and Heterotrait–Monotrait [HTMT] ratio, Tables 3 and 4), which confirms that the measures are capturing distinct empirical phenomena.

Sample size adequacy was determined beforehand using G*Power 3.1.9.2. Considering the number of predictors in the model, with an alpha level of 0.05, statistical power of 0.80, and an effect size (f ²) of 0.15, the minimum recommended sample size was 74 participants. Our final sample size (N = 488) therefore exceeds this requirement.

The Mann–Whitney U test was conducted in SPSS 24 to check for non-response bias, comparing responses from the first 100 and last 100 participants. The absence of significant differences (p = 0.0001) indicates non-response bias is unlikely. Common method bias (CMB) was evaluated using Harman’s single-factor test, which showed that a single factor accounted for 45.33% of the variance – below the critical 50% threshold – indicating CMB is not a significant issue.

Structural equation modeling (SEM) was implemented employing SmartPLS 3.3.3. PLS-SEM was chosen because it effectively handles complex models even with smaller sample sizes and does not require strict assumptions about data distribution. Analysis followed the two-stage method recommended by Hair et al. (2017):

• Measurement model assessment: Assessment of reliability and validity.

• Structural model assessment: Testing of hypothesized relationships using 5,000 bootstrap samples for significance testing.

The measurement model’s key components are construct reliability, discriminant validity, and convergent validity, which were evaluated following Hair et al. (2017). For reflective constructs, outer loadings should exceed 0.708, and convergent validity was assessed via average variance extracted (AVE). Three items (SFE2 = 0.508; SFE3 = 0.558; and SFE7 = 0.498) were removed due to substandard loadings. SFE1 was retained (0.655) based on content validity considerations. Post-refinement, loadings ranged from 0.740 to 0.962. Both Cronbach’s α (CA: 0.765–0.955) and composite reliability (CR: 0.850–0.971) exceeded the 0.70 reliability threshold. Convergent validity was established as AVE values (0.589–0.917) surpassed the 0.50 benchmark. Complete results are detailed in Figure 2 and Table 2.

Figure 2

Hypothesized model with loadings and R-squared values.

Discriminant validity was evaluated using the HTMT ratio and Fornell–Larcker criterion. As shown in Table 3, the Fornell–Larcker analysis confirmed discriminant validity for all constructs: the square root of each construct’s AVE exceeded its correlations with other constructs.

Discriminant validity was further assessed using the HTMT ratio, which compares within-construct correlations to between-construct correlations. In this study, the maximum HTMT value observed was 0.814, below both the conservative threshold (0.85) and liberal threshold (0.90), confirming discriminant validity (Table 4).

Multicollinearity was assessed by calculating variance inflation factors (VIFs), with the largest VIF observed being 1.86, well below the critical value of 3.3 (Hair et al., 2017). The model’s explanatory capacity ranged from moderate to substantial, as indicated by R ² coefficients of 0.585 for AI, 0.578 for MCI, 0.470 for LMCI, and 0.437 for PI (Hair et al., 2017). Goodness‑of‑fit was further corroborated by an standardized root mean squared residual (SRMR) of 0.059, comfortably under the conventional cutoff of 0.08.

Predictive relevance was demonstrated through Stone–Geisser Q ² statistics, all exceeding zero in the blindfolding procedure: AI = 0.524, MCI = 0.524, LMCI = 0.430, PI = 0.389, and TI = 0.165. To quantify the connection intensity of each predictor, effect sizes (F ²) were computed (Table 4). Every path yielding statistical significance exhibited an F ² of at least 0.02 (Hair et al., 2017).

Hypothesis testing results appear in Table 5 and Figure 3.

Figure 3

Theoretical model with path coefficients.

H1 proposed that SFE is positively linked with MCI, and the results supported the assumption (β = 0.151, t = 2.621, p < 0.05), which means ethnocentric fans tend to engage with media consumption behavior.

H2 and H3 assessed SFE’s association with PI and TI, respectively. SFE showed strong positive effects on both PI (β = 0.662, t = 19.741, p < 0.001) and TI (β = 0.256, t = 4.381, p < 0.001), thus confirming H2 and H3. The significant finding is that the effect size of PI was larger than TI (0.662 vs 0.256), meaning SFE predicts PI strongly.

H4, H5, and H6 hypothesized that TI is positively associated with AI, MCI, and LMCI, respectively. Subsequently, TI exhibited significant direct link with AI (β = 0.696, t = 22.573, p < 0.001), MCI (β = 0.239, t = 7.525, p < 0.001), and LMCI (β = 0.635, t = 16.129, p < 0.001), validating hypotheses H4, H5, and H6.

H7, H8, and H9 predicted PI positively predict AI, MCI, and LMCI, respectively. PI was found positively associated with AI (β = 0.145, t = 4.483, p < 0.001), MCI (β = 0.519, t = 10.051, p < 0.001), and LMCI (β = 0.111, t = 3.225, p < 0.05), providing support for H7, H8, and H9.

Finally, H10 assumed PI has a positive association with TI. The result validated the H10 (β = 0.230, t = 4.211, p < 0.001), indicating that if a fan identifies himself with a player, he will subsequently identify with the team.

Further, the current study examines several mediating indirect paths between SFE and behavioral intentions: MCI, AI, and LMCI, in which we observe that SFE not only directly but also indirectly, through player and TI, predicts sports behavioral intentions (Table 6).

An IPMA was conducted for AI, MCI, and LMCI to complement the PLS-SEM analysis. IPMA identifies managerial improvement priorities by comparing total effects (importance) and average latent variable scores (performance) of predecessor constructs (Ringle & Sarstedt, 2016). Following del-Castillo-Feito et al. (2022), four quadrants were defined: Q1 (Keep Up), Q2 (Focus Here), Q3 (Possible Overkill), and Q4 (Low Priority). Quadrant boundaries were determined using weighted averages from IPMA results (Table 7).

Key findings: TI showed the highest importance for both AI (total effect = 0.747) and LMCI (total effect = 0.635). However, its performance scores were low (Figure 4a–c), indicating a substantial performance gap. This reinforces the Delhi Capitals case finding that TI is critically undervalued despite its strategic impact.

Figure 4

(a) Importance–performance map (Latent variable (LV) = AI). (b) Importance–performance map (LV = MCI). (c) Importance–performance map (LV = LMCI).

Theoretically, this study advances the sports‑fandom literature in three primary ways. First, by applying SIT (Tajfel & Turner, 1986) to a global franchise context, we demonstrate that SFE not only fosters collective “us versus them” distinctions but differentially activates identification with native players vs the foreign franchise itself. While prior work has linked ethnocentrism to group‐level attachments (Perreault & Bourhis, 1999; Devlin & Billings, 2016), our findings reveal that in highly globalized leagues such as the IPL, SFE more strongly predict PI than TI through identity permeability, hierarchical identity, and context dependance, thereby refining SIT’s boundary conditions and reconciling conflicting results (e.g., Chiu & Won, 2020).

Second, we contribute a novel integrative model that positions SFE, PI, and TI as antecedents of three distinct consumption intentions: AI, MCI, and long‑term LMCI. Unlike previous frameworks that treat fan ethnocentrism as a unidimensional predictor of aggregate consumption (Chiu et al., 2015; Kim et al., 2011), our model disaggregates behavioral outcomes by commitment level and illustrates how SFE’s association unfolds with separate PI‑ and TI‑driven pathways. In doing so, we fill a critical theoretical void by showing that PI, often overlooked in favor of team‑level constructs, exerts a substantial link with low‑commitment (MCI) and moderate‑commitment (LMCI) behaviors, and even mediates the SFE → TI link.

Third, through rigorous mediation and IPMA, we chart a fine‐grained process model in which SFE sequentially engages PI and TI to predict each consumption intention. Our mediation results (e.g., SFE → PI → AI; SFE → PI → TI → LMCI) uncover nuanced indirect effects scarcely examined in prior sport‐consumer research (Carlson & Donovan, 2013; Fink et al., 2009). Meanwhile, the application of IPMA to SFE is, to our knowledge, unprecedented; by identifying TI as the highest‑importance/lowest‑performance lever for attendance and merchandise, and PI/SFE as adequate for media consumption, we introduce a practical yet theoretically rich tool for prioritizing identity‑based interventions. Collectively, these contributions deepen our understanding of identity dynamics in a global sports ecosystem and pave the way for future longitudinal and cross‐cultural validations.

This study offers guidance on three actionable areas for sports leagues, franchises, and marketers seeking to harness ethnocentric fan motivations and identity dynamics to predict engagement and revenue.

First, Clubs should focus on recruiting players who serve as a national bridge. When a club or team signs a contract with a player having strong national identity and cultural ties (such as Mustafizur Rahman), it immediately gets access to that community and market. To leverage this, teams could highlight these players in pre-game broadcasts, social media posts, and marketing campaigns aimed at their home countries’ markets. In addition, player-centric official merchandise, such as jerseys with the player’s name, branded accessories, could inspire the ethnocentric fans to display pride while engaging with the league. Teams should arrange community events such as youth clinics, autograph sessions, or cultural celebrations that would bring fans. For example, Manchester United’s promotion of Asian players to capture viewership in India, or the Los Angeles Dodgers’ spotlighting of Latino stars, demonstrated how SFE‑driven content yields vicarious fulfillment, elevates media consumption, and opens new sponsorship and broadcast‑rights opportunities (Chung et al., 2015).

Second, Teams should build team loyalty through fan experiences. Our study results indicated that Bangladeshi fans were less connected to the Delhi Capitals than to their national player. The result is crucial because the study further indicates that team loyalty motivates fans to engage in high commitment, such as purchasing tickets and merchandise. To overcome the issue, the team should emphasize fan experiences such as starting digital membership programs that provide fan access to exclusive behind-the-scenes content, a virtual tour of the team, and interaction with players online. Teams could arrange fan festivals during the off-season, targeting the overseas market by displaying team history, arranging interactive games, or interacting with former star players. Besides this, the team should create heritage storytelling campaigns, such as displaying documentaries on team journeys or celebrating major winning moments, so that fans can emotionally get attached to the team.

Third, as the study confirmed, PI predicts TI, teams should use star players to promote deeper team loyalty. Rather than treating player and team marketing as separate identities, they should converge both so that fans could connect liking a player to supporting the whole team. For example, franchises could combine player-focused content, such as performance highlights, success celebrations, or training videos, with team stories such that the player fits in with team goals and is a part of the team identity. Similarly, teams could offer products connecting both identity, such as a jersey that has the player’s name with the team’s symbol. Moreover, they could launch loyalty programs in which fans will earn loyalty points for engaging with player content, such as watching videos or sharing content, and those points could be redeemed for team experiences, such as discounted match tickets or access to exclusive team merchandise.

This study advances understanding of SFE, but several limitations warrant acknowledgment. The cross-sectional design does not allow causal inferences, especially the mediation effect reported in the study (e.g., SFE → PI → TI). Apparently, the arrow might imply temporal sequences that cannot be confirmed with single-point data, requiring longitudinal data to infer. Thus, the research findings are not a proven causal connection, but a theoretical structural association. In addition, focusing on a single player-team context (Mustafizur Rahman/Delhi Capitals) risks contextual bias. While helpful in isolating SFE effects, findings may not generalize to leagues dominated by national athletes (e.g., K-pop stars in Korean baseball) or distinct fan cultures (e.g., NBA, EPL). Future research should incorporate multiple players/teams across diverse leagues to assess SFE’s operation in varied settings.

The sample lacks demographic diversity (88.5% male, concentrated Bangladeshi cities, income homogeneity), limiting generalizability. Cricket’s gender-skewed fandom in South Asia contrasts with markets like European football, where female viewership grows. Future studies should employ quota sampling across gender, age, and income strata. Future research must validate the model in diverse sports (e.g., football, basketball) and markets (e.g., Europe, Latin America) to broaden applicability, testing SFE’s cultural boundaries and consistency. Examining women’s leagues and non-cricket sports can uncover underexplored gender-based variations.

Future studies should also explore moderators influencing the SFE-fan identification relationship, such as team performance, player mobility, or external crises (e.g., scandals). Additionally, place identification (e.g., city pride) could interact with SFE in driving consumption, enhancing the model’s explanatory power. Longitudinal tracking across seasons is essential to capture how behavioral intentions shift with athlete/team changes. Mixed-method approaches (e.g., surveys with interviews) could offer deeper insights into SFE’s psychological mechanisms, particularly whether PI stems from parasocial bonds or national hero-worship. While Chiu et al. (2015) argue for SFE’s cross-cultural relevance, this Bangladesh-specific setting necessitates caution in generalization. Addressing these avenues will enhance understanding of ethnocentrism, identity, and fandom in global sports.