Analisis faktor penerimaan pengguna terhadap adopsi Artificial Intelligence di Perguruan Tinggi melalui Extended UTAUT2

Jessica Nathania Kho; Hindriyanto Dwi Purnomo; Yessica Nataliani

doi:10.24246/aiti.v23i3.449-464

Authors

Jessica Nathania Kho Universitas Kristen Satya Wacana https://orcid.org/0009-0009-1016-7676
Hindriyanto Dwi Purnomo Universitas Kristen Satya Wacana
Yessica Nataliani Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.24246/aiti.v23i3.449-464

Keywords:

artificial intelligence, higher education, UTAUT2, technology acceptance, PLS-SEM

Abstract

Artificial Intelligence (AI) has increasingly been used to support scientific writing, offering various benefits while simultaneously raising ethical challenges for higher education institutions. This situation highlights the need for targeted AI integration based on a comprehensive understanding of students’ acceptance and actual use. This study investigates the factors influencing users’ acceptance and actual use of AI in research and scientific writing using the Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework and Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected through a survey of 129 respondents from the Faculty of Information Technology, Satya Wacana Christian University (SWCU). The results indicate that Trust has the strongest influence on Behavioral Intention, followed by Habit and Social Influence. Meanwhile, Habit is the strongest predictor of Use Behavior, whereas Facilitating Conditions and Behavioral Intention do not significantly influence actual use. These findings provide evidence of an Intention–Behavior Gap, suggesting that actual AI use in scientific writing is influenced more strongly by established usage habits than by conscious behavioral intentions alone.

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References

M. Alier, F.-J. García-Peñalvo, and J. D. Camba, “Generative Artificial Intelligence in Education: From Deceptive to Disruptive,” Int. J. Interact. Multimed. Artif. Intell., vol. 8, no. 5, pp. 5–14, 2024, doi: 10.9781/ijimai.2024.02.011.

Chegg, “Global Student Survey 2025,” Chegg, Santa Clara, United States, 2025. [Online]. Available: https://www.chegg.org/global-student-survey-2025

T. Wulandari, “Panduan Resmi Pakai AI Generatif di Kampus: Etika dan Daftar Apps,” detikEdu, Oct. 12, 2024. [Online]. Available: https://www.detik.com/edu/perguruan-tinggi/d-7586800/panduan-resmi-pakai-ai-generatif-di-kampus-etika-dan-daftar-apps

S. R. Fortin, “Impact of AI Chatbots on Student Support Services : Quantitative Insights from Higher Education Institutions, Using a UTAUT Framework,” Seoul National University, 2024. [Online]. Available: https://hdl.handle.net/10371/209984

O. V. Sergeeva, M. R. Zheltukhina, T. Shoustikova, L. R. Tukhvatullina, D. A. Dobrokhotov, and S. V. Kondrashev, “Understanding Higher Education Students’ Adoption of Generative AI Technologies: an Empirical Investigation Using UTAUT2,” Contemp. Educ. Technol., vol. 17, no. 2, pp. 1–21, Apr. 2025, doi: 10.30935/cedtech/16039.

A. Strzelecki, “Students’ Acceptance of ChatGPT in Higher Education: an Extended Unified Theory of Acceptance and Use of Technology,” Innov. High. Educ., vol. 49, no. 2, pp. 223–245, 2024, doi: 10.1007/s10755-023-09686-1.

A. Habibi, M. Muhaimin, B. K. Danibao, Y. G. Wibowo, S. Wahyuni, and A. Octavia, “ChatGPT in Higher Education Learning: Acceptance and Use,” Comput. Educ. Artif. Intell., vol. 5, no. 100190, pp. 1–10, 2023, doi: 10.1016/j.caeai.2023.100190.

A. M. Momani, “The Unified Theory of Acceptance and Use of Technology :,” Int. J. Sociotechnology Knowl. Dev., vol. 12, no. 3, pp. 79–98, 2020, doi: 10.4018/IJSKD.2020070105.

J. Cabero-Almenara, A. Palacios-Rodríguez, M. I. Loaiza-Aguirre, and P. S. Andrade-Abarca, “The Impact of Pedagogical Beliefs on the Adoption of Generative AI in Higher Education: Predictive Model from UTAUT2,” Front. Artif. Intell., vol. 7, pp. 1–11, Oct. 2024, doi: 10.3389/frai.2024.1497705.

B. G. Acosta-Enriquez et al., “Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory,” Heliyon, vol. 10, no. 19, p. e38315, Oct. 2024, doi: 10.1016/j.heliyon.2024.e38315.

M. Faraon, K. Rönkkö, M. Milrad, and E. Tsui, “International Perspectives on Artificial Intelligence in Higher Education: An Explorative Study of Students’ Intention to Use ChatGPT Across the Nordic Countries and the USA,” Educ. Inf. Technol., vol. 30, no. 13, pp. 17835–17880, Aug. 2025, doi: 10.1007/s10639-025-13492-x.

T. Tanantong and P. Wongras, “A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand,” Systems, vol. 12, no. 1, pp. 1–27, Jan. 2024, doi: 10.3390/systems12010028.

A. Alamäki, U. A. Khan, J. Kauttonen, and S. Schlögl, “An Experiment of AI-based Assessment: Perspectives of Learning Preferences, Benefits, Intention, Technology Affinity, and Trust,” Educ. Sci., vol. 14, no. 12, pp. 1–25, Dec. 2024, doi: 10.3390/educsci14121386.

K. T. Chandra and A. R. Tanaamah, “Pengaruh Technology Acceptance Model (TAM) terhadap Minat Investasi FinTech dengan Persepsi Risiko sebagai Faktor Moderasi,” AITI, vol. 22, no. 1, pp. 61–72, Mar. 2025, doi: 10.24246/aiti.v22i1.61-72.

G. García-Murillo, P. Novoa-Hernández, and R. Serrano Rodríguez, “On the Technological Acceptance of Moodle by Higher Education Faculty—A Nationwide Study Based on UTAUT2,” Behav. Sci. (Basel)., vol. 13, no. 5, pp. 1–27, May 2023, doi: 10.3390/bs13050419.

M. García de Blanes Sebastián, J. R. Sarmiento Guede, and A. Antonovica, “Application and Extension of the UTAUT2 Model for Determining Behavioral Intention Factors in Use of the Artificial Intelligence Virtual Assistants,” Front. Psychol., vol. 13, no. 993935, pp. 1–18, Oct. 2022, doi: 10.3389/fpsyg.2022.993935.

J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to Use and How to Report the Results of PLS-SEM,” Eur. Bus. Rev., vol. 31, no. 1, pp. 2–24, Jan. 2019, doi: 10.1108/EBR-11-2018-0203.

C. M. Ringle, S. Wende, and J.-M. Becker, “SmartPLS 4.” SmartPLS, Bönningstedt, 2024. [Online]. Available: https://www.smartpls.com/

J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in Second Language and Education Research: Guidelines Using an Applied Example,” Res. Methods Appl. Linguist., vol. 1, no. 3, pp. 1–16, Dec. 2022, doi: 10.1016/j.rmal.2022.100027.

M. Wirzberger, L. Bareiß, V. Herbst, A. Stock, and J. Kembitzky, “Performance Expectancy Benefits Acceptance Towards Digital Support for Self-Regulation,” Acta Psychol. (Amst)., vol. 258, no. 105273, pp. 1–10, Aug. 2025, doi: 10.1016/j.actpsy.2025.105273.

H. Moradi, “Integrating AI in Higher Education: Factors Influencing ChatGPT Acceptance Among Chinese University EFL Students,” Int. J. Educ. Technol. High. Educ., vol. 22, no. 1, p. 30, Jun. 2025, doi: 10.1186/s41239-025-00530-4.

H. Joshi, “Integrating Trust and Satisfaction into the UTAUT model to Predict Chatbot Adoption – A Comparison between Gen-z and Millennials,” Int. J. Inf. Manag. Data Insights, vol. 5, no. 1, pp. 1–16, Jun. 2025, doi: 10.1016/j.jjimei.2025.100332.

M. A. Bhat, C. K. Tiwari, P. Bhaskar, and S. T. Khan, “Examining ChatGPT Adoption Among Educators in Higher Educational Institutions Using Extended UTAUT Model,” J. Information, Commun. Ethics Soc., vol. 22, no. 3, pp. 331–353, Oct. 2024, doi: 10.1108/JICES-03-2024-0033.

D. Jekauc, M. C. Voelkle, M. Giurgiu, and C. R. Nigg, “Unveiling the Multidimensional Nature of the Intention–Behavior Gap,” Eur. J. Heal. Psychol., vol. 32, no. 1, pp. 34–50, Dec. 2025, doi: 10.1027/2512-8442/a000162.

M. Conner and P. Norman, “Understanding the Intention-Behavior Gap: The Role of Intention Strength,” Front. Psychol., vol. 13, pp. 1–16, Aug. 2022, doi: 10.3389/fpsyg.2022.923464.