Adoption of AI Devices on E-sports Management Platforms: An Expanded TAM Approach
Abstract
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.This research paper is for the 8th International Conference on Biospheric Harmony and Advanced Research (ICOBAR) 2026 publication.
Fields of Study & Subjects
Technology Adoption and User Behaviour
Digital Games and Media
Big Data and Business Intelligence
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 30 January 2026