Determinants of Generative AI Adoption Through the UTAUT Model: Insights From Postgraduate Business Students

In a highly competitive context where generative artificial intelligence (GAI) tools are gaining increasing relevance in educational learning environments, it is essential to understand the motivations and factors driving graduate students to adopt these technologies. This study systematically identifies the factors influencing graduate students’ intentions to use GAI tools. Students and alumni from a graduate business school in Peru were surveyed to assess their intentions regarding GAI technology usage. The study builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating GAI literacy as a variable. In late 2024, 251 participants from diverse backgrounds completed a questionnaire, which was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.1.0.2. This analysis aimed to uncover key factors influencing GAI adoption in higher education. The findings reveal that performance expectancy (PE), effort expectancy (EE), and perceived risk (PR) significantly influence the intention to use GAI, whereas facilitating conditions (FC) and social influence (SI) do not. Furthermore, prior experience with GAI moderates the relationships between FC, SI, and the intention to use GAI. These insights into the factors shaping GAI adoption intentions are vital for informing strategies to ethically leverage artificial intelligence (AI) in business and academia. By understanding user motivations, organizations can develop targeted policies and training programs to ensure responsible AI integration and maximize its potential benefits.