IJIET 2026 Vol.16(3): 625-639
doi: 10.18178/ijiet.2026.16.3.2535
doi: 10.18178/ijiet.2026.16.3.2535
Trust, Usefulness, and Satisfaction in ChatGPT Adoption: An ELM–TAM Study among University Students
Ching-Fang Lee1, Thi-Hue Lai1, Ixora-Javanisa Eunike1, and Kim-Anh Tran1,2,*
1. Department of Business Administration, Chaoyang University of Technology, Taichung, Taiwan
2. Department of Economics, Thuongmai University, Hanoi, Vietnam
Email: cf@cyut.edu.tw (C.-F.L.); laihue96@gmail.com (T.-H.L.); ixorajavanisaeunike@gmail.com (I.-J.E.); trankimanh@tmu.edu.vn (K.-A.T.)
*Corresponding author
2. Department of Economics, Thuongmai University, Hanoi, Vietnam
Email: cf@cyut.edu.tw (C.-F.L.); laihue96@gmail.com (T.-H.L.); ixorajavanisaeunike@gmail.com (I.-J.E.); trankimanh@tmu.edu.vn (K.-A.T.)
*Corresponding author
Manuscript received August 12, 2025; revised September 11, 2025; accepted October 12, 2025; published March 10, 2026
Abstract—This study integrates the Elaboration Likelihood Model (ELM) and the Technology Acceptance Model (TAM) to examine how trust mediates the cognitive and emotional mechanisms underlying students’ continued use of generative Artificial Intelligence (AI) chatbots—specifically, ChatGPT. A bibliometric analysis of 21 studies from the Web of Science database (2010–2025) reveals limited integration of ELM and TAM in educational AI contexts, highlighting a theoretical gap. To address this, we developed a dual-path model combining cognitive constructs (e.g., perceived usefulness, information quality) and affective cues (e.g., fairness, accountability, transparency), with trust serving as a mediating variable. A survey of 359 Vietnamese university students was analyzed using Structural Equation Modeling (SEM) and Multi-Group Analysis (MGA) to examine how ChatGPT usage duration moderates the persuasive process. Findings indicate that peripheral cues (e.g., fairness, accountability) significantly shape trust across user groups. However, the central route (information quality) on trust is heavily moderated by user experience: while it exerts a strong effect on low-frequency users, its impact is significantly attenuated—to less than half the strength—among high-frequency users. Furthermore, trust strongly predicts both satisfaction and perceived usefulness, with satisfaction emerging as the most powerful determinant of continuance usage intention. The study contributes to theory by validating the integration of ELM and TAM in the context of AI adoption and highlighting the moderating role of usage frequency. Practical implications include promoting AI literacy and designing chatbot interfaces with greater transparency to meet the cognitive expectations of experienced users.
Keywords—ChatGPT adoption, Elaboration Likelihood Model (ELM), Technology Acceptance Model (TAM), trust, higher education
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—ChatGPT adoption, Elaboration Likelihood Model (ELM), Technology Acceptance Model (TAM), trust, higher education
Cite: Ching-Fang Lee, Thi-Hue Lai, Ixora-Javanisa Eunike, and Kim-Anh Tran, "Trust, Usefulness, and Satisfaction in ChatGPT Adoption: An ELM–TAM Study among University Students," International Journal of Information and Education Technology, vol. 16, no. 3, pp. 625-639, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).