FAKTOR-FAKTOR YANG MEMPENGARUHI NIAT DAN PERILAKU MAHASISWA BISNIS DI INDONESIA DALAM MENGADOPSI TEKNOLOGI ARTIFICIAL INTELLIGENCE (AI): PENDEKATAN INTEGRATIF TAM DAN UTAUT
DOI:
https://doi.org/10.22225/kr.18.1.2026.96-109Keywords:
Actual Use , Artificial Intelligence, Jenis Kelamin, Perceived Usefulness, Perceived Ease of Use, Social InfluenceAbstract
The application of artificial intelligence (AI) technology in higher education has become a strategic need, especially for business students who have technology-based professional needs. This study aims to analyze the factors that influence students' intentions and actual behavior in adopting AI by integrating two main theoretical models, namely the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). This study involved 365 business university students as respondents from various regions in Indonesia and was analyzed using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach. The results of the analysis show that Perceived Effectiveness (PU) and Social Influence (SI) have a significant positive effect on Intention to Use (IU), which in turn becomes the main predictor of Actual Use (AU) of AI. In contrast, Perceived Ease of Use (PEOU) does not show a significant effect on IU, indicating that ease of use is no longer a major factor for the digital-native generation. The moderation test shows that gender does not act as a moderator in the relationship between IU and AU. This study confirms that the adoption of AI is not only determined by the functional aspects of technology but also by the perception of benefits and social influence. These findings provide theoretical contributions through the TAM–UTAUT integrative development model, as well as practical implications for educational institutions in designing more effective and adaptive technology adoption strategies to the characteristics of today's student generation
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2021). Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability, 13(14), 7893.
Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies. Scientific Reports, 15(1), 342.
Alyoussef, I. Y., & Omer, O. M. A. (2023). Investigating student satisfaction and adoption of technology-enhanced learning to improve educational outcomes in Saudi higher education. Sustainability, 15(19), 14617.
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44.
Bhasker Reddy Bhimanpati, V., Pandian, P. K. G., & Goel, D. P. (2024). UI/UX Design Principles for Mobile Health Applications. Punit, UI/UX Design Principles for Mobile Health Applications (August 30, 2024).
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295–336). Psychology Press.
Cokins, G., Oncioiu, I., Türke?, M. C., Topor, D. I., C?pu?neanu, S., Pa?tiu, C. A., Deliu, D., & Solov?stru, A. N. (2020). Intention to use accounting platforms in Romania: A quantitative study on sustainability and social influence. Sustainability, 12(15), 6127.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Di Gregorio, M. (2021). Accessible applications-Study and design of user interfaces to support users with disabilities. Proceedings of the 2021 International Conference on Multimodal Interaction, 832–834.
Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., & Welsch, R. (2023). Gender, age, and technology education influence the adoption and appropriation of LLMs. ArXiv Preprint ArXiv:2310.06556.
Duan, S., Wang, Z., Wang, S., Chen, M., & Zhang, R. (2024). Emotion-aware interaction design in intelligent user interface using multi-modal deep learning. 2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), 110–114.
Efilo?lu Kurt, Ö., & Tingöy, Ö. (2017). The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK. International Journal of Educational Technology in Higher Education, 14(1), 26.
Gill, S. S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., Fuller, S., Singh, M., Arora, P., & Parlikad, A. K. (2024). Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots. Internet of Things and Cyber-Physical Systems, 4, 19–23.
Glass, R., & Li, S. (2010). Social influence and instant messaging adoption. Journal of Computer Information Systems, 51(2), 24–30.
Grani?, A., & Maranguni?, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.
Gray, R. (2006). Social, environmental and sustainability reporting and organisational value creation? Whose value? Whose creation? Accounting, Auditing & Accountability Journal, 19(6), 793–819.
Green, G. (2024). Analysis of the mediating effect of resistance to change, perceived ease of use, and behavioral intention to use technology-based learning among younger and older nursing students. Journal of Professional Nursing, 50, 66–72.
He, S., Jiang, S., Zhu, R., & Hu, X. (2023). The influence of educational and emotional support on e-learning acceptance: An integration of social support theory and TAM. Education and Information Technologies, 28(9), 11145–11165.
Hidayat, D. N., Lee, J., Mason, J., & Khaerudin, T. (2022). Digital technology supporting English learning among Indonesian university students. Research and Practice in Technology Enhanced Learning, 17. https://doi.org/10.1186/s41039-022-00198-8
Hussain, A., Zhiqiang, M., Li, M., Jameel, A., Kanwel, S., Ahmad, S., & Ge, B. (2025). The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advanced technology. BMC Nursing, 24(1), 33.
Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36(2), 192–206.
Jain, K. K., & Raghuram, J. N. V. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies, 29(13), 17169–17209.
Jantan, A. H., Yazid, M. A., & Mubin, S. A. (2021). Extended User Experience Model to Support Web Accessibility and Emotional Qualities among Visually Impaired Users. Turkish Journal of Computer and Mathematics Education, 12(3), 2036–2040.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. (2024). Determinants of humanities and social sciences students’ intentions to use artificial intelligence applications for academic purposes. Information, 15(6), 314.
Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79–105.
Li, K. (2023). Determinants of college students’ actual use of AI-based systems: An extension of the technology acceptance model. Sustainability, 15(6), 5221.
Li, Z., Du, N., Wang, B., & Oteng-Darko, C. (2022). Impact of social influence on users’ continuance intention toward sports and fitness applications. Frontiers in Public Health, 10, 1031520.
Luo, Y. (2024). Enhancing educational interfaces: Integrating user-centric design principles for effective and inclusive learning environments. Applied and Computational Engineering, 64, 192–197.
Mahboob, F., & Khaskhely, M. K. (n.d.). Investigating Factors Towards Adoption of Mobile Wallet Payment Services.
Maheshwari, G. (2024). Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies, 29(10), 12167–12195.
Mardhiah, A., Farisha, N., Yuan, W. P., & Tony, F. N. (2022). Investigating the influence of perceived ease of use and perceived usefulness on housekeeping technology intention to use. International Journal of Academic Research in Business and Social Sciences, 12(11), 1306–1314.
Moslehpour, M., Pham, V. K., Wong, W.-K., & Bilgiçli, ?. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234.
Oviana, I., Badrudin, R., & Miswanto, M. (2025). Pengaruh Perceived Usefulness Dan Perceived Ease Of Use Terhadap Niat Menggunakan Digital Game Based Learning Dengan Attitude Sebagai Variabel Mediasi Dan Sex Serta Educational Level Sebagai Variabel Moderasi. Sekolah Tinggi Ilmu Ekonomi YKPN Yogyakarta.
Paganin, G., Apolinário-Hagen, J., & Simbula, S. (2023). Introducing mobile apps to promote the well-being of German and Italian university students. A cross-national application of the Technology Acceptance Model. Current Psychology, 42(31), 27562–27573.
Rahman, M. K., Ismail, N. A., Hossain, M. A., & Hossen, M. S. (2025). Students’ mindset to adopt AI chatbots for effectiveness of online learning in higher education. Future Business Journal, 11(1), 30.
Saha, P., Hossain, M. S., Roy, N. C., Masud, A. Al, & Amin, R. (2025). Unlocking the power of AI in education: students’ intentions and AI tool use driving learning success in an emerging economy. On the Horizon: The International Journal of Learning Futures.
Saifi, S., Tanveer, S., Arwab, M., Lal, D., & Mirza, N. (2025). Exploring the persistence of Open AI Adoption among users in Indian higher education: A fusion of TCT and TTF model. Education and Information Technologies, 1–24.
Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García-Peñalvo, F. J. (2020). Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80.
Saputra, A., & Algifari, A. (2022). ANALISIS PENGARUH PERSEPSI KEMUDAHAN, PERSEPSI KENYAMANAN, DAN PERSEPSI KEMANFAATAN TERHADAP MINAT MENGGUNAKAN E-COMMERCE. Jurnal Ekonomi Dan Bisnis, 17(2).
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), 46.
Soliman, M., Ali, R. A., Mahmud, I., & Noipom, T. (2025). Unlocking AI-Powered Tools Adoption among University Students: A Fuzzy-Set Approach. Journal of Information and Communication Technology, 24(1), 1–28.
Syed, A. Z., Memon, Z. H., Khan, K., Hameed, I., & Nadeem, M. (2025). Examining the behavioral determinants of AI adoption in higher education: a focus on perceptional factors and demographic differences. On the Horizon: The International Journal of Learning Futures.
Trivedi, S. K., Patra, P., Srivastava, P. R., Kumar, A., & Ye, F. (2022). Exploring factors affecting users’ behavioral intention to adopt digital technologies: The mediating effect of social influence. IEEE Transactions on Engineering Management.
Vannoy, S. A., & Palvia, P. (2010). The social influence model of technology adoption. Communications of the ACM, 53(6), 149–153.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003a). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003b). User acceptance of information technology: Toward a unified view1. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.
Wardhani, S. L., Kusuma, M. W., & Winarno, W. W. (2025). Adopsi AI oleh Dosen di Prodi Bisnis. 7(1), 181–198.
Wei, T. T., Marthandan, G., Chong, A. Y., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m?commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388.
Yang, P., & Qian, S. (2025). The factors affecting students’ behavioral intentions to use E-learning for educational purposes: A study of physical education students in China. Sage Open, 15(1), 21582440251313656.
Zaina, L. A. M., Fortes, R. P. M., Casadei, V., Nozaki, L. S., & Paiva, D. M. B. (2022). Preventing accessibility barriers: Guidelines for using user interface design patterns in mobile applications. Journal of Systems and Software, 186, 111213.
Zamrud, N. F., & Abu, M. Y. (2020). Comparative study: activity based costing and time driven activity based costing in electronic industry. Journal of Modern Manufacturing Systems and Technology, 4(1), 68–81.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Frasto Biyanto, Deden Iwan Kesuma, Rudy Badrudin, Yusti Pujisari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
