Determinants of AI Adoption Behaviour Among Lecturers: The Moderating Role of Institutional Support in Jordanian Higher Education

Abstract

This study examines the determinants influencing lecturers’ intention to use and adopt artificial intelligence (AI) in higher education. As AI technologies become increasingly integrated into teaching and learning, understanding lecturers’ perceptions is critical for successful adoption. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study investigates the roles of AI self-efficacy, perceived usefulness, relative advantage, and institutional support.A quantitative research design was employed using a systematic random sampling approach. Data were collected from 371 lecturers in public universities in Jordan through a structured questionnaire. All constructs were measured using validated items on a five-point Likert scale, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI. Furthermore, institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour. The findings also confirm that intention is a significant predictor of AI adoption behaviour.Theoretically, this study extends the UTAUT model by incorporating AI-specific determinants and emphasising the moderating role of institutional support, providing deeper insights into technology adoption in educational contexts. Practically, the findings highlight the importance of institutional support in developing lecturers’ AI-related skills and competencies. Targeted training programs and supportive environments can facilitate the effective and sustained use of AI tools in higher education.