The impact of ai ethics, attitudes, and perceptions on Chatgpt usage behavior
Published: July 28, 2026
Abstract
This study examined the relationship between artificial intelligence (AI) ethics, attitudes towards ChatGPT, perceived behavioral control, and students' usage behavior of ChatGPT. With a survey sample of 310 observations used to test the research hypotheses, the results of the PLS-SEM analysis showed that AI ethics, attitudes towards ChatGPT, and perceived behavioral control have positive impacts on the student’s ChatGPT usage behavior. Additionally, AI ethics also positively influence attitudes towards ChatGPT and perceived behavioral control. This study is expected to make significant contributions to the understanding of the impact of AI ethics on attitudes, perceived behavioral control, and the behavior of using AI technology, specifically ChatGPT. Based on the study’s findings, several implications were proposed to help organizations and businesses develop and implement AI systems, such as ChatGPT, more effectively while also enhancing user acceptance and technology usage.
Keywords
AI ethicsattitudes towards ChatGPTperceived behavioral controlChatGPT usage behavior
References
1.
Ajlouni, A. O., Wahba, F. A.-A., & Almahaireh, S. A. (2023). Students’ Attitudes Towards Using ChatGPT as a Learning Tool: The Case of the University of Jordan. International Journal of Interactive Mobile Technologies, 17(18), 99-117.
2.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
3.
Atabek, O., & Burak, S. (2020). Pre-school and primary school pre-service teachers’ attitudes towards using technology in music education. Eurasian Journal of Educational Research, 2020(87), 47-68.
4.
Bain, R. (1928). An attitude on attitude research. American Journal of Sociology, 33(6), 940-957.
5.
Binns, R., & Kirkham, R. (2021). How Could Equality and Data Protection Law Shape AI Fairness for People with Disabilities? ACM Transactions on Accessible Computing, 14(3), 1-37.
6.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers and Education, 59(3), 1054-1064.
7.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates,Publishers.
8.
Doãn Hồng Nhung, Nguyễn Xuân Bảo & Vũ Thị Hồng Hà (2024). Cơ hội và thách thức của trí tuệ nhân tạo trong giáo dục đại học, một số khuyến nghị đối với hoạt động đào tạo nghề Luật ở Việt Nam. Tạp chí Công Thương, 7(4), 4-83.
9.
Doll, J., & Ajzen, I. (1992). Accessibility and Stability of Predictors in the Theory of Planned Behavior. Journal of Personality and Social Psychology, 63(5), 754-765.
10.
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37-56.
11.
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. International Journal of Management Education, 18(1).
12.
Hair, Joe F, Ringle, C. M., Sarstedt, M., Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
13.
Hair, Joseph F, M.Hult, G. T., M.Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares
Hair Jr, J. F., Black, W. C., Babin, arry J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.).Cengage.
16.
Henseler, J, Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
17.
Henseler, Jorg, Ringle, C. M., & Sinkovics, R. R. (2009). The Use Of Partial Least Squares Path Modeling In International Marketing. In New Challenges to International Marketing. Emerald Group Publishing Limited.
18.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1-35.
19.
Murphy, G. D. (2009). Improving the quality of manually acquired data: applying the theory of planned behavior to data quality. Reliability Engineering and System Safety, 94(12), 1881-1886.
20.
Nguyễn Đức Thủy (2023). Đạo đức trong ứng dụng trí tuệ nhân tạo. Tạp chí Thông tin và Truyền thông, 4, 36-45.
21.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.