Determinants of Generative AI Adoption in Higher Education: A Social Science Perspective on Thai Faculty Behavioral Intentions

Abstract

The elements influencing Thai university faculty members' behavioral intents to employ generative artificial intelligence in their academic work are investigated in this study. The unique features of GAI adoption were captured using an expanded UTAUT framework that included felt satisfaction and perceived risk. A bilingual questionnaire was utilized to gather information from faculty members at several Thai universities, and the suggested associations were assessed using structural equation modeling. The findings indicate that while perceived risk has a negative impact, performance expectancy, effort expectancy, perceived enjoyment, and social influence all strongly predict faculty members' inclinations to adopt GAI. Through effort expectancy, facilitating situations have an indirect impact on intention. These results show that in order to encourage responsible and successful GAI adoption, colleges must improve training opportunities, bolster institutional support, and address ethical and practical issues. The study offers empirical insights into the factors that influence GAI adoption in the setting of higher education.