Adoption of Artificial Intelligence (AI) Devices on E-sports Management Platforms: An Expanded TAM Approach

Abstract—The rapid growth of e-sports has introduced increasingly complex managerial challenges, including player performance analysis, training coordination, scheduling, event organization, and talent scouting. To address these demands, e-sports management platforms have begun integrating Artificial Intelligence (AI) tools such as predictive performance analytics, automated tournament scheduling, data-driven talent scouting, and real-time strategic simulations. However, the availability of AI features does not guarantee user adoption. This study examines the determinants influencing the adoption of AI tools within e-sports management platforms using an extended Technology Acceptance Model (TAM). The proposed model incorporates Social Influence, Information Quality, and Perceived Competitive Advantage alongside core TAM constructs: Perceived Ease of Use, Perceived Usefulness, Attitude Towards Use, and Behavioral Intention. Data will be collected through a quantitative cross-sectional survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The expected outcomes include identifying the mediating roles of PEOU, PU, and ATU, validating the extended TAM in the e-sports-AI context, and providing practical recommendations for e-sports teams, coaches, administrators, and platform developers seeking to enhance AI adoption. Keywords—E-sports, E-sports Management, Artificial Intelligence, Technology Acceptance Model, User Adoption, PLS-SEM, Information Quality, Competitive Advantage.