Extending UTAUT2 to Explain Artificial Intelligence Adoption Among University Students in Indonesian Islamic Higher Education: An Empirical SEM-PLS Study

Abstract

Empirical evidence on AI adoption in Islamic higher education institutions within emerging economies remains limited, as prior studies have predominantly focused on universities in developed countries and have rarely examined the applicability of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) in this unique educational context. Addressing this gap, this study extends the UTAUT2 framework to examine the determinants of AI adoption among students at Islamic universities in Pekanbaru, Indonesia. A quantitative approach was employed using survey data collected from 200 students through probability sampling and analyzed with Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results reveal that Performance Expectancy, Facilitating Conditions, Hedonic Motivation, and Habit significantly influence Behavioral Intention, whereas Effort Expectancy, Social Influence, and Price Value do not. Furthermore, Behavioral Intention has a strong positive effect on Use Behavior. The structural model demonstrates substantial explanatory power, with an R² of 0.721 for Behavioral Intention and 0.428 for Use Behavior, indicating that the proposed model effectively explains students' AI adoption behavior. This study contributes theoretically by validating and extending UTAUT2 in the underexplored context of Islamic higher education in an emerging economy, highlighting the dominant roles of perceived usefulness, intrinsic motivation, and habitual technology use. Practically, the findings provide evidence-based guidance for university leaders and policymakers to develop AI-supported learning environments through improved digital infrastructure, student engagement strategies, and sustainable AI integration in higher education.