Determinants of Generative AI Adoption Intention in Higher Education: A Multidimensional Integration Framework and Empirical Exploration in Eastern and Western Chinese Universities

This study constructs a “technology-individual-society” triadic integration framework, embedding cultural-psychological variables into the classic Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology, to systematically investigate the multidimensional determinants of generative AI (GAI) adoption intention in the context of Chinese higher education. Through a large-sample survey of 840 teachers and students from five universities in eastern and western China, partial least squares structural equation modeling and multi-group analysis were employed to empirically reveal significant spatial variations in adoption intention pathways. The findings indicate that emotional dependency and individual innovation drive adoption intention through attitude mediation, while perceived usefulness exhibits a counter-intuitive negative effect, reflecting the underlying tension between technological empowerment and occupational displacement anxiety. Substantial differences in adoption intention patterns exist between eastern and western universities: the eastern group demonstrates a “technology-driven” pathway, where adoption intention is strongly influenced by perceived usefulness and attitude; the western group follows an “institutional adaptation” logic, where the shaping effect of perceived trust on attitude is exceptionally prominent, and the impact of effort expectancy on perceived usefulness is stronger. Regional differences are moderated through a triple mediating chain of “technological performance → attitude → adoption intention,” highlighting the systemic interaction effects of infrastructure, institutional environment, and cultural psychology.