Determinants of continued use willingness for library smart services: Evidence from university libraries in China

University libraries are undergoing critical smart transformations to provide accurate, convenient, and efficient services through smart technology. However, prior studies on determinants of university library smart services continued use willingness (CUW) predominantly examined isolated dimensions without proposing comprehensive models that integrate system level, individual user, and external environmental influence factors. This study aims to identify the determinants of users’ CUW for smart services in university libraries. It seeks to construct an integrated theoretical model that addresses gaps in prior frameworks by incorporating factors such as social influence, convenience conditions, personal characteristic, and perceived risk. An integrated model was developed by synthesizing constructs from the Technology Acceptance Model (TAM), Information Systems Success Model (D&M), and Unified Theory of Acceptance and Use of Technology (UTAUT). Employing stratified random sampling, we collected 309 valid questionnaires from university library users of China. Statistical analyses included reliability/validity tests, correlation analysis, and linear regression to examine relationships between variables. Information quality, system quality, service quality, personal characteristic, performance expectation, effort expectation, and convenience condition had a significant positive impact on user satisfaction. Conversely, perceived risk and social influence had no significant impact. For CUW, information quality, system quality, service quality, performance expectation, and convenience condition showed significant positive effects, while personal characteristic, effort expectation, perceived risk, and social influence were non-significant. Key challenges included deficiencies in information quality, system limitations, and service shortfalls. The validated framework emphasizes convenience condition, information quality, and system quality as primary drivers of university library users’ CUW. Recommendations include modernizing infrastructure, fostering collaborative resource ecosystems, and implementing AI-driven personalized services to enhance user retention. This study provides theoretical and practical insights for optimizing smart library services globally, though future research should expand cross-cultural comparisons, mixed research etc.