Explaining Generative Artificial Intelligence Acceptance Among Indonesian University Students: an Extended Technology Acceptance Model

Abstract

The rapid integration of Generative Artificial Intelligence (GenAI) into higher education has transformed learning practices, creating a need to better understand the factors that influence students’ acceptance of this emerging technology. While the Technology Acceptance Model (TAM) has been widely used to explain technology adoption, limited studies have incorporated contextual factors relevant to educational environments. This study extends TAM by integrating Value Compatibility (VC) and Lecturer Support (LS) to examine students’ acceptance of GenAI in Indonesian higher education. A quantitative cross-sectional survey was conducted with 100 university students selected through purposive sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 and a bootstrapping procedure of 5,000 subsamples. The findings reveal that Perceived Ease of Use significantly influences Perceived Usefulness, while Perceived Usefulness positively affects both Attitude Toward Using and Behavioral Intention. Attitude Toward Using significantly predicts Behavioral Intention, which subsequently influences Actual Use Behavior. Furthermore, the extended variables demonstrate significant effects, with Value Compatibility positively affecting Behavioral Intention and Lecturer Support enhancing Attitude Toward Using. The model exhibits moderate to high explanatory power and satisfactory predictive relevance. This study contributes to the technology acceptance literature by demonstrating that students’ adoption of GenAI is shaped not only by technological perceptions but also by contextual and institutional factors. The findings provide practical insights for higher education institutions seeking to promote effective and responsible integration of GenAI into teaching and learning practices.