https://orcid.org/0000-0003-0987-6730
![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
The study aims to explore the factors that influence university students’ behavioral intention (BI) and use behavior (UB) of generative AI products from an ethical perspective. Referring to ethical decision-making theory, the research model extends the UTAUT2 model with three influencing factors: ethical awareness (EA), perceived ethical risks (PER), and AI ethical anxiety (AIEA). A sample of 226 university students was analysed using the Partial Least Squares Structural Equation Modelling technique (PLS-SEM). The research results further validate the effectiveness of UTAUT2. Furthermore, performance expectancy, hedonistic motivation, price value, and social influence all positively influence university students’ BI to use generative AI products, except for effort expectancy. Facilitating conditions and habit show no significant impact on BI, but they can determine UB. The three extended factors from the ethical perspective play significant roles as well. AIEA and PER are not key determinants of BI. However, AIEA can directly inhibit UB. From the mediation analysis, although PER do not have a direct impact on UB, it inhibits UB indirectly through AIEA. Ethical awareness can positively influence BI. Nevertheless, it can also increase PER. These findings can help university students better accept and ethically use generative AI products.
Acknowledgements
We would like to express our sincere gratitude to Professor Junfeng Yang and Dr. Wenjing Zeng for their invaluable guidance and insightful suggestions throughout the course of this research. Their expertise and support were instrumental in shaping our work and helping us achieve our research objectives.
Ethical approval
This study was approved by the Ethics Committee of the School of Education in Hangzhou Normal University (China).
Informed consent
The students surveyed in the study gave informed consent to participate voluntarily. The data collected from the questionnaires were confidential and used only for academic research without any potential risk.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Wenjuan Zhu
Wenjuan Zhu is a Lecturer at the Jing Hengyi School of Education, Hangzhou Normal University. She received her Ph.D. from Educational Technology, Central China Normal University. And her research interests include artificial intelligence in education and intelligent learning analytics.
Lei Huang
Lei Huang is a master's degree student in modern education technology at the Jing Hengyi School of Education, Hangzhou Normal University. He is the corresponding author of this study.
Xinni Zhou
Xinni Zhou is a master's degree student in TESOL (Teaching English to Speakers of Other Languages) at the Moray House School of Education and Sport, the University of Edinburgh.
Xiaoya Li
Xiaoya Li is a master's degree student in modern education technology at the Jing Hengyi School of Education, Hangzhou Normal University.
Gaojun Shi
Gaojun Shi is a research assistant at the Jing Hengyi School of Education, Hangzhou Normal University. He earned Master's degree in Educational Technology from Hangzhou Normal University.
Jingxin Ying
Jingxin Ying is an undergraduate student of educational technology at the Jing Hengyi School of Education, Hangzhou Normal University.
Chaoyue Wang
Chaoyue Wang is a master's degree student in modern education technology at the Jing Hengyi School of Education, Hangzhou Normal University.