Ethical Awareness and Perceived Risk in Generative AI Adoption: An Extended UTAUT Study among University Students - Dian Puspita Sari Andri; Muhammad Fajar B (Journal of Vocational, Informatics and Computer Education, 2026)
Purpose – This study aims to extend the UTAUT by integrating ethical awareness and perceived risk to address the limitations of approaches that have predominantly emphasized usefulness and ease of use in explaining generative AI usage behavior. The study shows that ethical considerations not only complement existing factors but may also function as a direct determinant in shaping technology usage behavior. Methods – This study employed a quantitative survey with a cross-sectional design involving 327 students at Universitas Negeri Makassar. Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings – The structural model results indicate that ethical awareness has a significant effect on usage behavior (β = 0.377; p < 0.001), suggesting that students with higher ethical awareness tend to use generative AI more responsibly. Social influence significantly affects behavioral intention (β = 0.618; p < 0.001) and use behavior (β = 0.194; p = 0.012). Behavioral intention also influences use behavior (β = 0.168; p = 0.002), while facilitating conditions have a positive effect on usage behavior (β = 0.193; p = 0.012). In contrast, perceived risk does not show a significant effect (β = 0.057; p = 0.256). Research implications – The cross-sectional design and the use of a single institutional sample limit causal inference and the generalizability of the findings. Originality – This study extends UTAUT by integrating ethical awareness and perceived risk. The key novelty lies in identifying ethical awareness as a direct determinant of use behaviour, highlighting the central role of ethical factors in generative AI adoption, while perceived risk shows limited influence.