IEFInternational Education Forum3083-49022981-8605Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ief.v3i8.12029ArticleResearch on Factors Influencing University Students’ Continuance Intention to Use Generative Artificial Intelligencehttps://artdesignp.com/journal/IEF/3/8/10.26689/ief.v3i8.12029ZhangYue2025382025-09-18To investigate university students’ continuance intention regarding the use of generative artificial intelligence (Gen AI) in academic paper writing and to promote the sustained and healthy development of Gen AI, this study constructs a model of factors driving university students’ continuance intention towards Gen AI. The study integrates the Stimulus-Organism-Response (SOR) framework and the Technology Acceptance Model (TAM). Valid data from 397 questionnaires were collected and analyzed using Smart-PLS software to test the theoretical model. The findings reveal that perceived usefulness, satisfaction, and subjective norms are the primary factors influencing university students’ continuance intention to use Gen AI. Furthermore, perceived usefulness, perceived ease of use, and perceived risk are identified as the main factors affecting university students’ satisfaction with leveraging Gen AI.

Wang YM, Wang XY, Liu CC, 2024, Research on Ethical Risk Management Framework for Generative Artificial Intelligence Application in Education. E-education Research, 45(10): 28–34 + 42.

Chu JW, Du XX, 2024, Research Review of Generative Artificial Intelligence Empowering Knowledge Production in Scientific Research. Journal of Academic Libraries, 42(3): 108–117.

Woodworth RS, 1926, Dynamic psychology. The Pedagogical Seminary and Journal of Genetic Psychology, 33(1): 103–118.

Wang WT, Qian PB, Ding YC, et al., 2023, The Impact of Personalized Content Recommendation Close on Continuous Use Intention of Mobile Social Media. Library and Information Service, 67(11): 88–100.

Zhu HC, Hu X, Wang XB, 2018, The User’s Continuance Use Intention of Government Data Open Platform based on the S-O-R Framework. Journal of Modern Information, 38(5): 100–105 + 116.

Davis FD, 1989, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Quarterly, 13(3): 319–340.

Hong SJ, Thong JYL, Tam KY, 2006, Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet. Decision Support Systems, 42(3): 1819–1834.

Premkumar G, Bhattacherjee A, 2005, Explaining Information Technology Usage: A Test of Competing Models. Omega, 36(1): 64–75.

Thong JYL, Hong SJ, Tam KY, 2006, The Effects of Post-adoption Beliefs on the Expectation-confirmation Model for Information Technology Continuance [J]. International Journal of Human-Computer Studies, 64(9): 799–810.

Bhattacherjee A, 2001, Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3): 351–370.

Qin HX, Li Z, Zhou JH, 2020, A Probe into the Satisfaction with Online Teaching of Different Subjects and the Willingness to Continue Using It—An Empirical Analysis Based on the Technology Acceptance Model. Educational Research, 41(11): 91–103.

Zhang YH, Yuan QJ, Shen HZ, 2022, Perceived Risk Theory and Its Application and Prospect in the Field of Information System Research. Journal of Modern Information, 42(5): 149–159.

Chi H, Yeh H, Hung W, 2012, The Moderating Effect of Subjective Norm on Cloud Computing Users’ Perceived Risk and Usage Intention. International Journal of Marketing Studies, 4(6): 95.

Liu YL, Fan FC, 2024, ChatGPT-AIGC Users Risk Perception Dimension Identification and Management Research: An Exploratory Analysis Based on Grounded Theory. Information Studies: Theory & Application, 47(3): 121–129.

Sun D, Xue L, Zhang LP, 2021, Social Contagion of Emerging Technologies Risk Perception Based on “Coupling-Evolution” Process. Studies in Science of Science, 39(1): 2–11

Ajzen I, Fishbein M, 1972, Attitudes and Normative Beliefs as Factors Influencing Behavioral Intentions. Journal of Personality & Social Psychology, 21(1): 1–9.

Ajzen IU, 1991, The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2): 179–211.

Venkatesh V, Morris GM, Davis BG, et al., 2003, User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3): 425–478.

Tan CH, Li Y, 2020, Research on Influencing Factors of Users’ Continuous Usage Intention of Virtual Academic Community. Research on Library Science, 2020(20): 28–38.

Tan CH, Yi Y, Li L, 2021, Research on the Influential Factors of Users’ Continuance Intention Towards Academic WeChat Public Account. Journal of Modern Information, 41(1): 50–59 + 77.

Li JX, Yu DD, 2023, Analysis on Perceived Usefulness of Users of Scientific Data Sharing Platform. Journal of Intelligence, 42(9): 196–201.

Xu JF, Deng QL, 2024, Chinese EFL Learners’ Acceptance of Live Video-streamed Teaching Platforms: A Study Based on the Technology Acceptance Model. Foreign Language Teaching and Research, 56(2): 262–273 + 320–321.

Fan Z, Liu YL, 2020, Impact of Perceived Usefulness and Ease of Use on User Mobile Visual Search Behavior Intention. Information and Documentation Services, 41(1): 79–86.

Wang X, McGill JT, Klobas EJ, 2020, I Want It Anyway: Consumer Perceptions of Smart Home Devices. The Journal of Computer Information Systems, 60(5): 437–447.

Huang YM, Lou SJ, Hong TC, et al., 2019, Middle-aged Adults’ Attitudes Toward Health App Usage: A Comparison with the Cognitive-affective-conative Model. Universal Access in the Information Society, 18(4): 927–938.

Shahrabani S, Rosenboim M, Shavit T, et al., 2019, Should I Stay or Should I Go?” Risk Perceptions, Emotions, and the Decision to Stay in an Attacked Area. International Journal of Stress Management, 26(1): 57–64.

Zhou M, Zhao L, Kong N, et al., 2019, Factors Influencing Behavior Intentions to Telehealth by Chinese Elderly: An Extended TAM Model. International Journal of Medical Informatics, 2019(126): 118–127.

Kelman CH, 2006, Interests, Relationships, Identities: Three Central Issues for Individuals and Groups in Negotiating Their Social Environment. Annual Review of Psychology, 57(1): 21–26.

Jayasingh S, Eze CU, 2010, The Role of Moderating Factors in Mobile Coupon Adoption: An Extended TAM Perspective. Communications of the IBIMA, 2010(596470): 1.

Lu D, Zheng XQ, 2022, Tourists’ Psychological Mechanism in Sharing Accommodation: A Cognition-emotion-action Theory Perspective. Resource Development & Market, 38(11): 1382–1389 + 1400.

Jiang ZW, 2023, Construction of Influencing Factors Model for Privacy Risk Perception in Human-Machine Interaction—Empirical Research Based on User Use of Smart Speaker. Journalism and Mass Communication, 2023(8): 83–96.

Song Y, Chen L, Li Q, et al., 2022, AI Ethical Risk Perception, Trust and Public Participation. Studies in Science of Science, 40(7): 1153–1162 + 1171.