Artificial intelligence in Chinese academic libraries: A study on the influence factors of user adoption intention based on the extended UTAUT model

Academic libraries play critical roles in artificial intelligence adoption and implementation within higher education environments. This study conducts an empirical investigation using an extended Unified Theory of Acceptance and Use of Technology framework that incorporates trust and perceived risk to examine the determinants of users’ adoption intention toward artificial intelligence in Chinese academic libraries. Data were collected from 323 participants in China and analyzed using structural equation modeling with SPSS and SmartPLS 4.0. The findings indicate that performance expectancy, effort expectancy, social influence, trust, and perceived risk are significantly associated with users’ adoption intention, whereas facilitating conditions are not. In addition, the findings show that trust is significantly associated with perceived risk, whereas performance expectancy is not significantly associated with trust, and social influence is not significantly associated with perceived risk. These results highlight the importance of trust and perceived risk in shaping artificial intelligence adoption intention in academic libraries and offer context-specific insights for supporting responsible artificial intelligence use, improving artificial intelligence service performance, and promoting artificial intelligence service adoption.