https://orcid.org/0000-0002-9650-2798
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ABSTRACT
Research Question
The COVID-19 pandemic has highlighted the need for a transformative perspective on the role of sport brands in promoting fans’ psychological well-being. Drawing upon attachment theory, the current research explores how individuals’ involvement with sport brands may contribute to their psychological well-being in the wake of COVID-19.
Research Methods
Data were collected from sport fans (n = 770) in mainland China through an online survey. Machine learning-based model selection algorithms were used to optimize the balance between the predictive power and parsimoniousness of the empirical model. Bayesian structural equation modeling was performed to examine the effects of sport brand involvement (SBI), crisis management performance, and perceived togetherness (PT) on fans’ sense of hope and emptiness.
Results and Findings
The results indicate that fans’ involvement with sport brands was positively associated with fans’ psychological well-being. SBI mitigated fans’ perceived emptiness. This relationship was partially mediated by PT but not by crisis management performance. Furthermore, sport brands’ crisis management performance and PT fully mediated the positive relationship between SBI and hope.
Implications
This research contributes to theorizing the transformative role of sport brands in enhancing fans’ psychological well-being. We offer an alternative view of sport branding literature by moving beyond fans’ contributions to business outcomes to explore how sport brands may benefit fans’ well-being. Findings highlight the importance of the transformative power of ‘we’ in unifying sport brands and fans amid the uncertainty of the COVID-19 pandemic.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 We used unstandardized point estimates and related confidence intervals of main and indirect effects (see Inoue et al., Citation2015, p. 40) as informative priors. To check the robustness of our findings, we conducted Bayesian structural mediation analysis using non-informative/uniform priors. The goal was to justify how our results were sensitive to this statistical modification, given the underlying assumption that little existing literature was available to configure the distributions of tested relationships in the proposed model. We employed uniform priors on all included parameters, linkages, and residual specifications and assumed diagonal variance-covariance matrices using 20,000 MCMC iterations.