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Abstract

Purpose: This study examines the effects of ethical awareness, academic integrity, and perceived risk on ethical intention in using Generative Artificial Intelligence (GenAI) among students of the Faculty of Economics at Universitas Tarumanagara.


Research Method: A quantitative approach with an explanatory survey design was employed. Data were collected using a five-point Likert-scale questionnaire distributed to students who had experience using or were familiar with GenAI for academic purposes. A total of 107 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS.


Results and Discussion: The findings reveal that ethical awareness, academic integrity, and perceived risk positively and significantly influence ethical intention in using GenAI. Academic integrity emerged as the strongest predictor, followed by ethical awareness and perceived risk. The model demonstrated substantial explanatory power (R² = 0.797), indicating that students’ ethical intentions are shaped by moral awareness, commitment to academic integrity, and consideration of the potential risks of GenAI misuse.


Implications: Universities should strengthen digital ethics literacy, establish clear GenAI usage policies, and design assessments that promote originality and academic responsibility.


Originality: This study integrates ethical awareness, academic integrity, and perceived risk within an Ethical Decision-Making Theory framework to explain ethical intention toward GenAI use among economics students.

Keywords

generative AI ethical awareness academic integrity perceived risk ethical intention

Article Details

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Kosasih, F. N., Hidajat, N. C., Arifuddin, A., & Usman, A. (2026). Ethical Awareness, Academic Integrity, and Perceived Risk on Ethical Intentions in the Use of Generative AI: A Perspective from Ethical Decision-Making Theory. Advances: Jurnal Ekonomi & Bisnis, 4(3), 793–815. https://doi.org/10.60079/ajeb.v4i3.915
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