关键词: 公共图书馆, 人工智能, 用户使用意愿, TAM模型, PLS-SEM, 感知风险, 用户信任

Abstract:

[Purpose/Significance] This study aims to reveal the influencing factors that affect users' behavioral intention to adopt Artificial Intelligence (AI) smart services in public libraries. As public cultural institutions transition toward intelligent service paradigms, the integration of generative AI offers unprecedented opportunities to enhance knowledge accessibility and operational efficiency. By exploring users' actual needs for AI-driven tools - such as intelligent reference desks, personalized reading recommendations, and automated retrieval systems - this research seeks to provide robust theoretical and practical guidance. Ultimately, it aims to promote the deep integration of AI technologies within the broader framework of smart library construction, ensuring that these innovations align with user expectations and the public interest. [Method/Process] Drawing upon the Technology Acceptance Model (TAM) as the foundational theoretical framework, this study introduces Trust and Perceived Risk as critical external variables to accurately reflect the current technological climate, which is increasingly characterized by data privacy concerns and algorithmic opacity. Data were collected through a structured online questionnaire survey targeting a diverse demographic of public library users, resulting in 257 valid responses. To empirically test the proposed research model and hypotheses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. The rigorous analytical process included a comprehensive assessment of the measurement model to confirm internal consistency, convergent validity, and discriminant validity, followed by the evaluation of the structural model to determine the statistical significance of the path coefficients and the overall explanatory power of the integrated framework. [Results/Conclusions] The empirical evaluation of the structural model yielded several key findings. First, both Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) exert a significant positive impact on user satisfaction, highlighting that functional utility and intuitive interfaces are baseline requirements for AI adoption. Second, Trust, Satisfaction, PU, and PEOU are all identified as strong, direct positive predictors of users' Behavioral Intention (BI) to use AI smart services. Third, Perceived Risk (PR) significantly and negatively influences BI, acting as a major barrier to adoption. Interestingly, the influence of PR on PU was found to be statistically insignificant, suggesting that users evaluate the functional benefits of AI independently of its potential risks. Finally, Trust was shown to effectively mitigate user concerns, exerting a significant negative impact on PR. Based on these insights, it is recommended that public libraries prioritize enhancing the algorithmic transparency of their AI applications to systematically build user trust. Furthermore, libraries should integrate regional cultural elements to develop localized and distinctive AI services, diversify AI application scenarios to meet multifaceted user demands, and actively implement educational workshops and lectures focused on improving public AI literacy.

Key words: public libraries, artificial intelligence, user behavioral intention, TAM model, PLS-SEM, perceived risk, trust