Understanding GenAI Adoption in Higher Education: An Integrated Model of Motivational Needs and Technology Acceptance

The rapid advancement in generative artificial intelligence (GenAI) has transformed learning, problem-solving, and creative practices in higher education, yielding a blend of both opportunities and challenges. This study investigates university students' adoption of GenAI tools, their satisfaction with these tools, and the resulting behavioural changes by integrating the Uses and Gratifications (U&G) theory with the Technology Acceptance Model (TAM). An extended model was proposed in this study, incorporating motivational needs, technology acceptance factors, and Negative Usage Tendency (NU) as a moderating factor. A quantitative survey involving 237 university students in Macao was conducted, and the data were analysed using Structural Equation Modelling (SEM) and path analysis. Results confirmed the classical TAM pathways, indicating that Perceived Ease of Use (PEOU) influenced Perceived Usefulness (PU), which subsequently predicted Behavioural Intention (BI) and Actual Use (AU). Motivational needs were shown to have a significant impact on PEOU, while NU was found to moderate the relationship between AU and satisfaction. Collectively, these findings advanced knowledge of GenAI adoption, offering theoretical insights into university students' thinking regarding GenAI use. Additionally, limitations and future research directions were further discussed.