Usage Intention of AI Among Academic Librarians in China: Extension of UTAUT Model

Abstract

This study explores how academic librarians adopt artificial intelligence (AI) technologies, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as its main framework, expanded with elements from Personal Innovativeness in IT (PIIT) and the Technology Readiness Index (TRI). A quantitative approach was applied, gathering data from 340 academic librarians and analyzing them using PLS-SEM. The results indicate that facilitating conditions (β = 0.345, p < 0.001) and effort expectancy (β = 0.123, p = 0.034) significantly influence behavioral intention, while performance expectancy (β = 0.091, p = 0.085) and top management support (β = 0.000, p = 0.997) show limited direct effects. These findings challenge some traditional assumptions of the UTAUT model. Additionally, attitudes were found to mediate the relationship between effort expectancy and social influence on behavioral intentions, while individual readiness and personal innovativeness moderate these relationships (β = −0.069, p = 0.003), highlighting the importance of individual traits. The model demonstrated strong predictive power, with R² values of 0.677 for behavioral intention and 0.574 for actual behavior, along with Q² predict values exceeding 0.56. By incorporating PIIT and TRI, this study broadens existing models of technology adoption, offering deeper insights into how organizational factors, personal traits, and readiness interact to influence AI adoption. Practical recommendations include introducing adaptive training programs, personalized support systems, and AI-driven infrastructure enhancements to encourage effective AI integration. Future research should consider longitudinal studies to examine how readiness and innovativeness evolve over time, explore cross-cultural differences, and refine strategies to ensure sustainable AI adoption in diverse academic settings.