GenAI Acceptance Modeling in Islamic Higher Education: An Integration of TAM and EVT Using PLS-SEM
Keywords:
EVT, generative artificial intelligence technology, PLS-SEM, TAM
Abstract
Generative artificial intelligence (GenAI) technology is currently receiving special attention and has numerous benefits. In the education field, this technology can help obtain information quickly to complete a thesis. This research aims to conduct GenAI Modeling based on the Technology Accepted Model (TAM) and Expected Value Theory (EVT) framework using the Partial Least Square Structural Equation Model (PLS-SEM). The research used primary data obtained from surveys. The population was all Tarbiyah faculty students who took a thesis in the Even Semester of the 2023/2024 academic year with a total of 1266. The sample in this research was 191 students who were completing their thesis and had used Gen AI technology to help complete their thesis. The sampling technique used cluster random sampling with a procedure of dividing students into 8 clusters based on the study program. The research instrument used a questionnaire consisting of 5 latent variables: Perceived Usefulness, Perceived Ease of Use, Intrinsic Motivation, Perceived Value, and Behavioral Intention to Use. The results of the analysis using the PLS-SEM method showed that Intrinsic Motivation has a significant relationship with Perceived Ease of Use, and Intrinsic Perceived Usefulness and Perceived Value have a significant relationship with Behavioral Intention to Use. These results show that students choose GenAI Technology to help complete their thesis based on its benefits, such as making it easier to prepare backgrounds, research instruments, and data analysis steps, as well as providing insight into knowledge related to the topic being researched. The research results imply the need for policies regarding the use of GenAI technology for theses so that students are wiser in using GenAI technology.
References
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Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: an empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649. https://doi.org/10.1007/s10639-021-10465-8
Novianti, K. D. P., Putri, N. K. W. L., & Purnamayanti, I. A. G. W. (2022). Analisis Penerimaan Sistem Informasi Menggunakan Technology Acceptance Model (Studi Kasus : Sijalak Desa Pohsanten). INSERT : Information System and Emerging Technology Journal, 2(2), 113. https://doi.org/10.23887/insert.v2i2.43135
Nurqamarani, A. S., Sogiarto, E., & Nurlaeli, N. (2021). Technology Adoption in Small-Medium Enterprises based on Technology Acceptance Model: A Critical Review. Journal of Information Systems Engineering and Business Intelligence, 7(2), 162. https://doi.org/10.20473/jisebi.7.2.162-172
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). Editorial: On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Putu, N., Lina, M., Bali, P. N., & Boonyasana, K. (2024). FOSTERING INTRINSIC MOTIVATION : THE ROLE OF SDT AND TAM THEORY IN SHAPING BEHAVIORAL INTENTIONS FOR SUSTAINABLE. 4(2), 85–95.
Ranellucci, J., & College, H. (2020). Journal of Computer Assisted Learning: Editorioal. Journal of Computer Assisted Learning, 20(1), 1–3. https://doi.org/10.1111/j.1365-2729.2004.00069.x
Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI Models: A Preliminary Review. Future Internet, 15(6). https://doi.org/10.3390/fi15060192
Selano, M. K., Marwaningsih, V. R., & Setyaningrum, N. (2020). Pemeriksaan Gula Darah Sewaktu (GDS) dan Tekanan Darah kepada Masyarakat. Indonesian Journal of Community Services, 2(1), 38. https://doi.org/10.30659/ijocs.2.1.38-45
Senaratne, S. I., & Samarasinghe, S. M. (2019). Factors Affecting the Intention to Adopt M-Learning. International Business Research, 12(2), 150. https://doi.org/10.5539/ibr.v12n2p150
Setiawan, A., & Luthfiyani, U. K. (2023). Penggunaan ChatGPT Untuk Pendidikan di Era Education 4.0: Usulan Inovasi Meningkatkan Keterampilan Menulis. JURNAL PETISI (Pendidikan Teknologi Informasi), 4(1), 49–58. https://doi.org/10.36232/jurnalpetisi.v4i1.3680
Utari, U. A., & Fauziah, Y. (2023). Analysis of Factors Affecting Intention to Use and User Satisfaction of Paylater Using DeLone & McLean Adoption Model. Telematika, 20(3), 343. https://doi.org/10.31315/telematika.v20i3.10643
Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived Ease of Use’s Effects on Behavioral Intention Mediated by Perceived Usefulness and Trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
Wong, R. (2023). Role of generative artificial intelligence in publishing. What is acceptable, what is not. Journal of Extra-Corporeal Technology, 55(3), 103–104. https://doi.org/10.1051/ject/2023033
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4(April), 100147. https://doi.org/10.1016/j.caeai.2023.100147
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00453-6
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
Almufarreh, A. (2024). Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach. Sustainability (Switzerland) , 16(13). https://doi.org/10.3390/su16135354
Bastari, A., Eliyana, A., Syabarrudin, A., Arief, Z., & Emur, A. P. (2020). Digitalization in banking sector: the role of intrinsic motivation. Heliyon, 6(12). https://doi.org/10.1016/j.heliyon.2020.e05801
Castelli, M., & Manzoni, L. (2022). Special Issue: Generative Models in Artificial Intelligence and Their Applications. Applied Sciences (Switzerland), 12(9), 10–12. https://doi.org/10.3390/app12094127
Chan, C. K. Y., & Zhou, W. (2023). An expectancy-value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00284-4
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6(November 2023), 100197. https://doi.org/10.1016/j.caeai.2023.100197
Elizabeth. (2024). The Influence of Perceived Value of Benefits, Perceived Ease of Technology Use and Customer Trust on Intention to Use Digital Banking Applications. Jurnal Informasi Dan Teknologi, 5, 265–271. https://doi.org/10.60083/jidt.v5i4.449
Farhat, F., Silva, E. S., Hassani, H., Madsen, D. Ø., Sohail, S. S., Himeur, Y., Alam, M. A., & Zafar, A. (2023). The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1270749
Fernanda, J. W. (2024). Preliminary Study of Using Generative AI for Thesis Completion.
Fernanda, J. W., Luthifiana, V., & Akhyar, M. K. (2022). Analisis Partial Least Square Structural Equation Model (PLS-SEM) untuk Pemodelan Penerimaan Sistem Jaringan Informasi Bersama Antar Sekolah (JIBAS). J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 15(2), 292–297. https://doi.org/10.36456/jstat.vol15.no2.a6436
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Joseph F. Hair, J. . G. T. M. H. C. M. R. M. S. (2021). A primer on partial least squares structural equation modeling (PLS-SEM)-Third Edition.
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024). University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education. Education Sciences, 14(10). https://doi.org/10.3390/educsci14101090
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5(July), 100178. https://doi.org/10.1016/j.caeai.2023.100178
Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model. Scientific Reports, 14(1), 1–17. https://doi.org/10.1038/s41598-024-64363-3
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
Liu, L., Qu, S., Zhao, H., Kong, L., Xie, Z., Jiang, Z., & Zou, P. (2024). Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study. Frontiers in Medicine, 11(May), 1–17. https://doi.org/10.3389/fmed.2024.1406842
Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: an empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649. https://doi.org/10.1007/s10639-021-10465-8
Novianti, K. D. P., Putri, N. K. W. L., & Purnamayanti, I. A. G. W. (2022). Analisis Penerimaan Sistem Informasi Menggunakan Technology Acceptance Model (Studi Kasus : Sijalak Desa Pohsanten). INSERT : Information System and Emerging Technology Journal, 2(2), 113. https://doi.org/10.23887/insert.v2i2.43135
Nurqamarani, A. S., Sogiarto, E., & Nurlaeli, N. (2021). Technology Adoption in Small-Medium Enterprises based on Technology Acceptance Model: A Critical Review. Journal of Information Systems Engineering and Business Intelligence, 7(2), 162. https://doi.org/10.20473/jisebi.7.2.162-172
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). Editorial: On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Putu, N., Lina, M., Bali, P. N., & Boonyasana, K. (2024). FOSTERING INTRINSIC MOTIVATION : THE ROLE OF SDT AND TAM THEORY IN SHAPING BEHAVIORAL INTENTIONS FOR SUSTAINABLE. 4(2), 85–95.
Ranellucci, J., & College, H. (2020). Journal of Computer Assisted Learning: Editorioal. Journal of Computer Assisted Learning, 20(1), 1–3. https://doi.org/10.1111/j.1365-2729.2004.00069.x
Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI Models: A Preliminary Review. Future Internet, 15(6). https://doi.org/10.3390/fi15060192
Selano, M. K., Marwaningsih, V. R., & Setyaningrum, N. (2020). Pemeriksaan Gula Darah Sewaktu (GDS) dan Tekanan Darah kepada Masyarakat. Indonesian Journal of Community Services, 2(1), 38. https://doi.org/10.30659/ijocs.2.1.38-45
Senaratne, S. I., & Samarasinghe, S. M. (2019). Factors Affecting the Intention to Adopt M-Learning. International Business Research, 12(2), 150. https://doi.org/10.5539/ibr.v12n2p150
Setiawan, A., & Luthfiyani, U. K. (2023). Penggunaan ChatGPT Untuk Pendidikan di Era Education 4.0: Usulan Inovasi Meningkatkan Keterampilan Menulis. JURNAL PETISI (Pendidikan Teknologi Informasi), 4(1), 49–58. https://doi.org/10.36232/jurnalpetisi.v4i1.3680
Utari, U. A., & Fauziah, Y. (2023). Analysis of Factors Affecting Intention to Use and User Satisfaction of Paylater Using DeLone & McLean Adoption Model. Telematika, 20(3), 343. https://doi.org/10.31315/telematika.v20i3.10643
Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived Ease of Use’s Effects on Behavioral Intention Mediated by Perceived Usefulness and Trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
Wong, R. (2023). Role of generative artificial intelligence in publishing. What is acceptable, what is not. Journal of Extra-Corporeal Technology, 55(3), 103–104. https://doi.org/10.1051/ject/2023033
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4(April), 100147. https://doi.org/10.1016/j.caeai.2023.100147
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00453-6
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
Published
2025-01-10
How to Cite
Fernanda, J., Donasari, R., Anuraga, G., & Rahman, F. (2025). GenAI Acceptance Modeling in Islamic Higher Education: An Integration of TAM and EVT Using PLS-SEM. Southeast Asian Journal of Islamic Education, 7(2), 189–202. https://doi.org/10.21093/sajie.v7i2.9815
Section
Articles
