Determinants of AI-Powered Online Reading Adoption: An Integrated TAM-UTAUT Approach among University Students in Hubei, China
Keywords:
Artificial Intelligence, Online Reading, Behavioral Intention, Use Behavior, Undergraduate StudentAbstract
Purpose: This study investigates the factors influencing students’ behavioral intention and use behavior toward AI-powered online reading in Hubei, China, addressing the limited understanding of how AI-driven reading contexts extend beyond conventional e-learning adoption models, based on an integrated TAM and UTAUT framework. Research design, data and methodology: A quantitative approach was employed, with data collected from 500 undergraduate students across three universities in Hubei Province using non-probability sampling, including judgment sampling to select institutions and quota sampling to ensure proportional representation. A validated questionnaire was administered, and data were analyzed using confirmatory factor analysis and structural equation modeling to examine measurement validity and structural relationships. Results: The findings indicate that social influence, attitude, perceived usefulness, performance expectancy, and perceived ease of use significantly affect behavioral intention. Social influence shows the strongest effect, followed by attitude and perceived usefulness. Behavioral intention significantly predicts use behavior, confirming its mediating role. Conclusions: The results suggest that effective adoption of AI-powered online reading depends on strengthening social support, enhancing perceived academic benefits, and improving system usability. This study contributes by extending TAM-UTAUT to an AI-powered reading context and highlighting the role of social and affective factors beyond traditional models. Universities and platform developers should focus on integrating social interaction features and user-centered design to promote sustained student engagement.
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