A fsQCA Study on University Faculty's Adoption Intentions of GenAI Across Disciplinary Differences

The application of generative artificial intelligence (GenAI) in higher education is increasingly widespread, yet the formation mechanisms of faculty adoption intentions may vary across different disciplinary backgrounds. Most existing research treats the faculty population as a homogeneous group, overlooking the shaping influence of disciplinary cultures, the internal drivers of knowledge paradigms, and the impact of technological logic. This study establishes an analytical framework for GenAI adoption intent among university faculty based on the UTAUT model and AI literacy theory. Using fsQCA analysis on 385 questionnaires from teachers across STEM, humanities, and social sciences, it identifies the following patterns: STEM faculty exhibit a dual-core “performance-skill” driving mechanism, characterized by high performance expectations and high skill proficiency, following an efficient tool-oriented path. In contrast, humanities and social sciences faculty exhibit a dual-track adoption pathway: one is a community-adaptive approach relying on social influence and positive attitudes, while the other is a cautious compliance approach centered on critical evaluation and neutral stances. This study reveals fundamental differences in faculty technology adoption logic across disciplines, challenging previous homogeneous explanatory paradigms. It holds significant implications for universities to develop targeted support strategies for faculty AI education development across diverse academic fields.