Designing the Framework for Adopting Artificial Intelligence Technology in Iran’s Sports Services

Abstract

This study aimed to design and present a comprehensive, context-based framework for the adoption of artificial intelligence (AI) technology in Iran’s sports services. A mixed-methods design was applied. Quantitative data were collected from 380 sports managers, experts, and service users via a questionnaire developed based on established technology acceptance models. Data were analyzed using structural equation modeling (PLS-SEM) and confirmatory factor analysis. Reliability and validity were confirmed through Cronbach’s alpha, composite reliability, AVE, and overall goodness-of-fit indices. The qualitative phase involved 20 semi-structured interviews with sports managers and coaches, analyzed through inductive content analysis to identify barriers and facilitating factors for AI adoption. Quantitative analysis revealed that perceived usefulness had the strongest effect on attitudes (β=0.52, P<0.001) and behavioral intention (β=0.28, P<0.01). Perceived ease of use and the importance of sports influenced behavioral intention directly and indirectly through attitudes. Attitude significantly mediated the relationship between individual factors and AI adoption (β=0.61, P<0.001). Qualitative findings highlighted barriers such as limited technical infrastructure, security and privacy concerns, insufficient training, and cultural resistance. Key enablers included strong top management support, continuous user training, promotion of a technology-friendly culture, and active user involvement in system development and deployment. The integrated framework provides a scientifically validated, practical guide for policymakers and managers to foster AI acceptance in sports services by addressing individual, organizational, technological, and environmental factors. Implementing strategies such as continuous training, data security assurance, and cultural adaptation can enhance service quality and user satisfaction.