A structural model of student continuance intentions in ChatGPT adoption

Anoop Saxena; Tenzin Doleck

doi:10.29333/ejmste/13839

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Anoop Saxena ¹ , Tenzin Doleck ² ^*

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¹ McGill University, Montreal, QC, CANADA² Simon Fraser University, Burnaby, BC, CANADA^* Corresponding Author

Abstract

ChatGPT has experienced unprecedented acceptance and use, capturing popular and academic attention. With this growth in use comes the need to focus on the determinants of ChatGPT use as the success of a technology or service depends largely on users’ continuance intentions. Modeling what influences students’ intention to continue using ChatGPT is important to better understand how students search for information and their decision-making process. Using a sample of 106 students, we test a structural model developed using the unified extended-confirmation model. The research model included the following elements: subjective norm, perceived usefulness of continued use, disconfirmation of their expectations from prior use, satisfaction with prior use, and continuance intention. The findings demonstrate support for the proposed research model as the research model explains 60.5% of the variance in continuance intention. In terms of the direct influence on continuance intention, the role of perceived usefulness and satisfaction were documented. The present study has the potential to serve as a starting point for improving our understanding of antecedents of continuance intentions in the context of ChatGPT.

Keywords

ChatGPT
technology acceptance
continuance intention
large language models
artificial intelligence chatbots

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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.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2023, Volume 19, Issue 12, Article No: em2366

https://doi.org/10.29333/ejmste/13839

Publication date: 01 Dec 2023

Online publication date: 27 Oct 2023

Article Views: 1673

Article Downloads: 927

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How to cite this article

APA

Saxena, A., & Doleck, T. (2023). A structural model of student continuance intentions in ChatGPT adoption. Eurasia Journal of Mathematics, Science and Technology Education, 19(12), em2366. https://doi.org/10.29333/ejmste/13839

Vancouver

Saxena A, Doleck T. A structural model of student continuance intentions in ChatGPT adoption. EURASIA J Math Sci Tech Ed. 2023;19(12):em2366. https://doi.org/10.29333/ejmste/13839

AMA

Saxena A, Doleck T. A structural model of student continuance intentions in ChatGPT adoption. EURASIA J Math Sci Tech Ed. 2023;19(12), em2366. https://doi.org/10.29333/ejmste/13839

Chicago

Saxena, Anoop, and Tenzin Doleck. "A structural model of student continuance intentions in ChatGPT adoption". Eurasia Journal of Mathematics, Science and Technology Education 2023 19 no. 12 (2023): em2366. https://doi.org/10.29333/ejmste/13839

Harvard

Saxena, A., and Doleck, T. (2023). A structural model of student continuance intentions in ChatGPT adoption. Eurasia Journal of Mathematics, Science and Technology Education, 19(12), em2366. https://doi.org/10.29333/ejmste/13839

MLA

Saxena, Anoop et al. "A structural model of student continuance intentions in ChatGPT adoption". Eurasia Journal of Mathematics, Science and Technology Education, vol. 19, no. 12, 2023, em2366. https://doi.org/10.29333/ejmste/13839