Assessing students’ intention to adopt generative artificial intelligence

This study examines the factors that influence the use, adoption, and recommendation of generative artificial intelligence (GenAI) tools among French management science students. Our study extends the Unified Theory of Acceptance and Use of Technology (UTAUT 3) model by including trust, learning value, and empowerment in learning. This extension fills gaps in our understanding of the psychosocial factors influencing the adoption of GenAI tools in higher education. We used a questionnaire and partial least squares structural equation modeling (PLS-SEM) to analyze data collected from 257 French management science students across different institutions. The results show that the most significant factors are performance expectancy, habit, hedonic motivation, and trust. These factors explain 49.3% of the intention to use GenAI tools, 58.7% of the intention to adopt them, and 39.6% of the intention to recommend them. However, other factors had no significant effect on behavioral intentions. This study contributes to the literature on technology acceptance by extending the UTAUT 3 model to an educational context. Additionally, it provides practical recommendations for educators, policymakers, and technology providers to promote the integration of GenAI tools in management science education and prepare students for future professional environments centred on GenAI tools.