Beyond AI Adoption: Understanding Student Satisfaction and Continuance Intention in ChatGPT Use for Higher Education

Kevin Pangestu; Siti Rahayu; Juliani Dyah  Trisnawati

doi:10.61987/jemr.v5i5.3183

Authors

Kevin Pangestu Universitas Surabaya
Siti Rahayu Universitas Surabaya
Juliani Dyah Trisnawati Universitas Surabaya

DOI:

https://doi.org/10.61987/jemr.v5i5.3183

Keywords:

ChatGPT, Generative Artificial Intelligence, Student Satisfaction, Continuance Intention

Abstract

The growing adoption of generative artificial intelligence (AI) in higher education highlights the need to understand factors influencing students’ satisfaction and continued use of ChatGPT. This study aims to examine the effects of information quality, service quality, perceived enjoyment, perceived usefulness, and perceived ease of use on satisfaction and continuance intention, while testing the direct and moderating role of need for interaction with a service employee (NFI-SE). A quantitative causal design was employed using data from 211 respondents aged 18–25 who had used ChatGPT for academic purposes. Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that information quality, service quality, perceived enjoyment, perceived usefulness, and perceived ease of use positively and significantly affect satisfaction. Satisfaction significantly predicts continuance intention, while enjoyment, usefulness, and ease of use also directly influence continuance intention. NFI-SE positively affects satisfaction and strengthens several relationships. The study concludes that human interaction complements AI adoption, implying that institutions should integrate ChatGPT with sustained human academic guidance.

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