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Abstract

The digitalization that has occurred has changed the way universities in Indonesia implement the learning process. The government's push for e-learning the best solution for students and lecturers to carry out their learning activities. The emergence of a series of innovative findings related to online learning would certainly provide the government with insight into formulating regulations in the face for Students in the Coastal Area. The findings of research on the usage of information technology are not limited to universities as service providers, as well as on the student intentions of technology users. This paper will investigate how students intend to use e-learning in in the Coastal Area. Quantitative approach was conducted in Riau Island Province, involving 150 students. The main construct of the UTAUT Model, was studied and tested with Structural Equation Modeling. The results of the analysis show that only Facilitating Conditions and Trust have a positive and significant effect on the intention to use e-learning. The study suggests to government should prioritize improving the equitable distribution of technological infrastructure and internet accessibility, particularly in coastal areas, small islands, and border regions within the Riau Islands Province.

Keywords

E-Learning Policy Intention to Use Facilitating Conditions Trust UTAUT

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Afrizal, D., Hildawati, H., Nanda, I. Y., Purnamasari, D., Tazri, M., Gusri, L., … Velandi, Y. (2026). Explaining Student Intention to Use E-Learning Policy in Coastal and Archipelagic Regions: The Role of Facilitating Conditions and Trust. Golden Ratio of Mapping Idea and Literature Format, 6(3), 2068–2077. https://doi.org/10.52970/grmilf.v6i3.2261
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References

  1. Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
  2. Abbas. (2018). Integration of TTF, UTAUT, and ITM for mobile Banking Adoption. International Journal of Advanced Engineering, Management and Science, 4(5).
  3. Abdekhoda, M., Dehnad, A., & Zarei, J. (2022). Factors influencing adoption of e-learning in healthcare: integration of UTAUT and TTF model. BMC Medical Informatics and Decision Making, 22(1), 2. https://doi.org/10.1186/s12911-022-02060-9
  4. Afrizal, D. (2020). E- Government Service Review In Dumai City Indonesia. Jurnal Niara, 13(1), 260–267.
  5. Afrizal, D., Erlianti, D., Hijeriah, E.-M., Hafrida, L., & Luthfi, A. (2024). Unlocking E-Government Success: Key Factors Driving E-Absence Adoption in Public Sector. JAKPP (Jurnal Analisis Kebijakan Dan Pelayanan Publik), 10(2), 181–194. https://doi.org/https://doi.org/10.31947/jakpp.v10i2.34975
  6. Afrizal, D., & Wallang, M. (2021). Attitude on intention to use e-government in Indonesia. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 435–441. https://doi.org/10.11591/ijeecs.v22.i1.pp435-441
  7. Alwahaishi, S. (2021). Student Use of E-Learning During the Coronavirus Pandemic. International Journal of Distance Education Technologies, 19(4), 72–90. https://doi.org/10.4018/ijdet.286742
  8. Ansong-Gyimah, K. (2020). Students’ perceptions and continuous intention to use elearning systems. International Journal of Emerging Technologies in Learning, 15(11), 236–244.
  9. Antoniadis, K., Zafiropoulos, K., & Mitsiou, D. (2022). Measuring Distance Learning System Adoption in a Greek University during the Pandemic Using the UTAUT Model, Trust in Government, Perceived University Efficiency and Coronavirus Fear. Education Sciences, 12(9). https://doi.org/10.3390/educsci12090625
  10. Arif, Ameen & Rafiq, M. (2018). Factors affecting student use of Web-based services: Application of UTAUT in the Pakistani context. The Electronic Library, 36(3), 518–534.
  11. Dwivedi, Y. K., Rana, N. P., Tamilmani, K., & Raman, R. (2020). A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature. Current Opinion in Psychology, 36, 13–18. https://doi.org/10.1016/j.copsyc.2020.03.008
  12. Gupta. (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1).
  13. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  14. Hasan, A., Raghuwanshi, S., Harsora, H., Kumar, P., Gupta, V., & Singh, A. S. (2025). Elearning adoption among millennials: behavioural intentions and the extended UTAUT model. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00813-y
  15. Hildawati, H., Afrizal, D., Hafrida, L., Purnamasari, D., & Luthfi, A. (2026). The Mediating Role of User Satisfaction in the Relationship between UTAUT Constructs and User Behavior in Digital Public Service Applications. West Science Social and Humanities Studies, 4(01), 1–18. https://doi.org/https://doi.org/10.58812/wsshs.v4i01.2545
  16. Jonathan, S. (2006). Metode Penelitian Kuantitatif & Kualitatif (Edisi Pert). Graha Ilmu.
  17. Lallmahomed, M. Z. I., Lallmahomed, N., & Lallmahomed, G. M. (2017). Factors influencing the adoption of e-Government services in Mauritius. Telematics and Informatics, 34(4), 57–72.
  18. Mai, W., & Khairani, A. Z. (2026). Exploring UTAUT Research in Higher Education: A Bibliometric Analysis and Future Directions in the Era of AI and ChatGPT. International Journal of Instruction, 19(2), 59–86. https://doi.org/10.29333/iji.2026.1924a
  19. Odewumi, Yusuf, & Oputa. (2018). Intention to Use Social Media for Learning Interactive Effect of Postgraduate Gender in South West Nigeria. International Journal of Education and Development, 14(3).
  20. Sumak, & Sorgo. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602–620.
  21. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association, 2004.
  22. Tria, J. (2020). The COVID-19 Pandemic through the Lens of Education in the Philippines: The New Normal. Pedagogical Development and Lifelong Learning, 1(1).
  23. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425–478.
  24. Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J., & Brown, S. A. (2011). UTAUT predictors and the role of context. 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x
  25. Weis, L., Bele, J. L., & Erčulj, V. (2026). Acceptance and Use of Generative Artificial Intelligence in Higher Education: A UTAUT-Based Model Integrating Trust and Privacy. Education Sciences, 16(2). https://doi.org/10.3390/educsci16020173
  26. Witarsyah. (2017). The Critical Factors Affecting e-Government Adoption in Indonesia. Advanced Science, Engineering and Information Technology, 7(1), 160–17.
  27. Yusuf, I. M., Astuti, R. S., Kismartini, Afrizal, D., & Saputra, J. (2021). The Role of Collaborative E-Government in Surabaya Intelligent Traffic System: A Case Study of Surabaya, Indonesia. Proceedings of the International Conference on Industrial Engineering and Operations Management, 2472–2479.