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Factors influencing Chinese college students’ intention to use AIGC: a study based on the UTAUT model

Author

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  • Kai Guo

    (Henan University of Science and Technology)

  • Chengyuan Zhan

    (Henan University of Science and Technology)

  • Xiang Li

    (Qingdao City College)

Abstract

This study focuses on the intention of Chinese college students to use Artificial Intelligence Generated Content (AIGC), aiming to explore the influencing factors and their mechanisms of action and to provide theoretical and practical guidance for the application of AIGC in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, diffusion of innovations theory, and perceived risk theory, an analytical framework is constructed. A questionnaire survey was conducted, collecting 415 valid samples, and structural equation modeling (SEM) was used for empirical analysis. The study found that performance expectancy, effort expectancy, social influence, facilitating conditions, satisfaction, and relative advantage positively affect the intention to use; relative advantage and satisfaction act as mediators between multiple factors and the intention to use, with the mediating effect of relative advantage being more pronounced; chain mediation effect analysis shows that facilitating conditions play a stronger mediating role in enhancing the intention to use AIGC; perceived risk negatively moderates the relationship between relative advantage and the intention to use. The results of the study provide recommendations for higher education institutions and technology developers.

Suggested Citation

  • Kai Guo & Chengyuan Zhan & Xiang Li, 2025. "Factors influencing Chinese college students’ intention to use AIGC: a study based on the UTAUT model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(4), pages 1663-1677, April.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:4:d:10.1007_s13198-025-02772-x
    DOI: 10.1007/s13198-025-02772-x
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    References listed on IDEAS

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    1. Yuhui Jing & Haoming Wang & Xiaojiao Chen & Chengliang Wang, 2024. "What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    2. Jörg Garrel & Jana Mayer, 2023. "Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    3. Mohamed L. Seghier, 2023. "ChatGPT: not all languages are equal," Nature, Nature, vol. 615(7951), pages 216-216, March.
    4. Attila Dabis & Csaba Csáki, 2024. "AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
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