Generative AI Adoption Among Business School Students: Drivers Of Use And Policy Implications

Abstract

The rapid integration of generative Artificial Intelligence (GenAI) tools into higher education presents both transformative potential and pedagogical challenges. This study researches the key factors influencing the adoption of GenAI among business school students, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as its theoretical framework. Drawing on survey data from 441 students across the Baltic Sea region, the research employed partial least squares structural equation modeling (PLS-SEM) to estimate the relationships between performance expectancy, effort expectancy, social influence, study value, habit, facilitating conditions, and hedonic motivation in predicting the use of GenAI. The findings reveal that habit, social influence, and study value significantly impact students' adoption of GenAI tools. At the same time, performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation do not show statistically significant effects. These results suggest a behavioral-intention gap: positive perceptions of GenAI do not consistently translate into actual use. The study highlights the importance of fostering habitual engagement and peer-driven norms to encourage meaningful integration of GenAI in academic settings. By offering empirical insights into student behavior, this study contributes to the discourse on digital transformation in higher education. It provides actionable recommendations for educators and policymakers to support the ethical, practical, and student-centered adoption of GenAI, aligning with the conference's mission to advance innovation and quality in higher education studies.

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