Generative AI adoption in universities: how TAM–TTF and neuroticism influence sustained usage

Purpose Faculty members with high neuroticism are more likely to adopt generative artificial intelligence (AI)-based learning tools to enhance their careers. Their heightened anxiety and uncertainty drive them toward user-friendly and efficient technologies that support teaching and research. Task-technology fit (TTF) theory suggests that aligning technology with task requirements can mitigate anxiety and foster sustained adoption. This study aims to investigate the impact of TTF and neuroticism on the long-term use of generative AI tools among faculty members in higher education. Design/methodology/approach In total, 560 faculty members from higher education institutions participated in this study. A hybrid structural equation modeling (SEM) approach, integrating artificial neural networks (ANN) and necessary condition analysis (NCA), was used to analyze both linear and nonlinear relationships, providing a comprehensive view of the factors driving AI adoption must-have and should-have factors. Findings The results indicate that perceived usefulness and ease of use significantly impact faculty members’ intention to adopt AI-based learning tools, with an R-squared value of 69.7%. TTF indirectly influences adoption through these mediators, whereas neuroticism moderates the relationship between continued AI tool usage and perceived effectiveness. Practical implications These findings suggest that institutions should improve usability, align technology with academic tasks and address individual differences in neuroticism to promote long-term engagement. Providing faculty training, support systems and incentive programs can facilitate AI adoption in higher education. Originality/value This study uniquely explores the intersection of TTF and neuroticism in AI adoption by using a novel SEM–ANN–NCA approach to uncover complex behavioral patterns in technology use.