Adoption of 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. By extending the Technology Acceptance Model (TAM), this study investigates how AI tools are adopted within e-sports management platforms. Alongside traditional TAM constructs Perceived Ease of Use, Perceived Usefulness, Attitude Towards Use, and Behavioral Intention model integrates Social Influence, Information Quality, and Perceived Competitive Advantage. A quantitative cross-sectional survey will serve as the method for data collection, and Partial Least Squares Structural Equation Modeling (PLS-SEM) will be applied for analysis. 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. By fostering Research and Development in AI for e-sports management, this study provides a framework for Continuous Innovation and Technological Innovation in digital competitive platforms, supporting the global transition toward sustainable industrialization as outlined in SDG 9. Keywords: E-sports, E-sports Management, Artificial Intelligence, Technology Acceptance Model, User Adoption, PLS-SEM, Information Quality, Competitive Advantage, Advanced Technology, Innovation, ICT, Technological Innovation, Research and Development.