📄 Abstract
Artificial intelligence is now woven into professional sports marketing, showing up in personalized content, recommendation engines, automated fan communication, predictive analytics, and data-driven fan services. Even so, solid empirical evidence on how AI-enabled personalization actually shapes fan engagement and downstream commercial outcomes is still thin, especially in South Asian professional sport. This paper sets out a theory-driven model connecting perceived AI-driven personalization to fan engagement, brand loyalty, and purchase intention. The model draws on UTAUT2, on Yoshida et al.'s (2014) sport-fan engagement framework, and on Puntoni et al.'s (2021) consumer–AI experiential perspective, treating fan engagement as the mediating mechanism and team identification and perceived algorithmic depersonalization as boundary conditions. The proposed design is a cross-sectional survey of adult professional-sport fans who have used at least one AI-enabled fan touchpoint, with Partial Least Squares Structural Equation Modeling (PLS-SEM) as the intended analytical approach — covering measurement-model assessment, structural path estimation, bootstrapped mediation and moderation tests, conditional indirect effects, predictive assessment, and robustness checks. What follows is the theoretical model, hypotheses, measurement framework, survey instrument, and a fully pre-specified statistical protocol. No respondent-level dataset has yet been collected for this manuscript, so no numerical results are reported here; the protocol is written so that genuine survey data can be dropped in and analyzed later without needing to invent coefficients, significance levels, reliability estimates, or fit statistics.
🏷️ Keywords
📚 How to Cite:
Mukul Sharma, Dr Jainish Bhagat , AI-DRIVEN SPORTS MARKETING AND FAN OUTCOMES IN PROFESSIONAL SPORTS LEAGUES , Volume 14 , Issue 9, September 2026, EPRA International Journal of Economic and Business Review(JEBR) , Pages: 31 - 37 , DOI: https://doi.org/10.36713/epra31505