Examining Student Acceptance and Intention to Continue Online Digital Learning: A Case Study at Public Universities in Surakarta

Neerzalla Atha Nafisah, Khansa Bashira Pondavi, Charisma Cintani Adhi Surya, Haiva Putri Palmaisyah, Retno Wulan Damayanti

Abstract

This study aims to examine students’ acceptance and continuance intention toward digital learning platforms, with a focus on Coursera MOOCs (Massive Open Online Courses). The research investigates six variables: Computer Self-Efficacy (CSE), System Interactivity (SI), Content Feature (CF), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Intention to Continue (IC). A quantitative approach was employed using a structured questionnaire distributed to 182 students at Public Universities in Surakarta who had participated in Coursera-based MOOCs. Prior to data analysis, validity, reliability, and classical assumption tests (normality, multicollinearity, heteroscedasticity, and autocorrelation) were conducted to ensure the robustness of the model. The data were analyzed using linear regression to examine the relationships among the variables. The results reveal that CSE and CF have significant effects on PU and PE, whereas SI shows no significant influence. Furthermore, PU and PE significantly affect students’ IC, with PU emerging as the most influential factor driving students’ continuance intention. These findings suggest that students’ ongoing engagement with digital learning platforms is largely determined by their perceived usefulness of the system rather than its interactive features. Despite potential sampling bias due to online group distribution, this limitation was minimized through multi-department dissemination. Overall, the study provides empirical insights into the key determinants supporting the sustainability of MOOC-based learning in higher education.

Keywords

acceptance; digital learning; intention; MOOCs; online platform

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References

Adriyanto, A. R., Santosa, I., Syarief, A., & Irfansyah, I. (2021). Design and multimedia learning principles on MOOC IndonesiaX. Jurnal Cakrawala Pendidikan, 40(1), 92–106. https://doi.org/10.21831/cp.v40i1.34699

Alkhuwaylidee, A. R. (2025). Exploring factors influencing students’ continuance intention to use e-learning system for Iraqi university students. Computers, 14(5), 176. https://doi.org/10.3390/computers14050176

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Edelsbrunner, S., Steiner, K., Schön, S., Ebner, M., & Leitner, P. (2022). Promoting digital skills for Austrian employees through a MOOC: Results and lessons learned from design and implementation. Education Sciences, 12(2), 89. https://doi.org/10.3390/educsci12020089

Ghozali, I. (2021). Aplikasi analisis multivariate dengan IBM SPSS 26. UNDIP.

Gujarati, D. N., & Porter, D. C. (2020). Basic econometrics (6th ed.). McGraw-Hill.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2020). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.

Ho, N. T. T., Abdullah, M. R. T. L., Idrus, H. B., Sivapalan, S., Pham, H.-H., Dinh, V.-H., Pham, H. K., & Nguyen, L. T. M. (2023). Acceptance toward Coursera MOOCs blended learning: A mixed methods view of Vietnamese higher education stakeholders. Sage Open, 13(4). https://doi.org/10.1177/21582440231197997

Huang, C.-H. (2021). Exploring the continuous usage intention of online learning platforms from the perspective of social capital. Information, 12(4), 141. https://doi.org/10.3390/info12040141

Jung, H.-S., & Yoon, H.-H. (2021). Generational effects of workplace flexibility on work engagement, satisfaction, and commitment in South Korean deluxe hotels. Sustainability, 13(16), 9143. https://doi.org/10.3390/su13169143

Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212

Li, R. (2021). Modeling the continuance intention to use automated writing evaluation among Chinese EFL learners. Sage Open, 11(4). https://doi.org/10.1177/21582440211060782

Muti Altalhi, M. (2021). Towards understanding the students’ acceptance of MOOCs: A unified theory of acceptance and use of technology (UTAUT). International Journal of Emerging Technologies in Learning (IJET), 16(2), 237. https://doi.org/10.3991/ijet.v16i02.13639

Nguyen, L. T., Kanjug, I., Lowatcharin, G., Manakul, T., Poonpon, K., Sarakorn, W., Somabut, A., Srisawasdi, N., Traiyarach, S., & Tuamsuk, K. (2023). Digital learning ecosystem for classroom teaching in Thailand high schools. Sage Open, 13(1). https://doi.org/10.1177/21582440231158303

Nugroho, A., Mursityo, Y. T., & Hariyanti, U. (2024). Analisis faktor yang mempengaruhi penggunaan berkelanjutan massive open online course pada Gen Z menggunakan technology acceptance model termodifikasi dan task technology fit. Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi, 5(2), 89–99. https://doi.org/10.25126/justsi.v5i2.508

Pachisia, J. (2022). The concept of blended learning mode. International Journal of Home Science, 8(1), 74–81. http://www.homesciencejournal.com

Rohiem, A. F., & Sari, J. (2023). Analisis SWOT sarana pembelajaran digital massive open online course (MOOC) Ruang Guru. Dirasat: Jurnal Manajemen dan Pendidikan Islam, 9(2), 126–136. https://doi.org/10.26594/dirasat.v9i2.3684

Sari, R. P., & Dahnial, I. (2022). Impact of massive open online course (MOOC) as best practice in Indonesia Medan Marelan District Elementary School. Jurnal Pemikiran dan Pengembangan Sekolah Dasar (JP2SD), 10(2), 122–133. https://doi.org/10.22219/jp2sd.v10i2.20379

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2020). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Springer.

Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Al-Rahmi, W. M. (2021). Information and communications technology used in higher education: An empirical study on digital learning as sustainability. Sustainability, 13(13), 7074. https://doi.org/10.3390/su13137074

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Arshad, M. R. (2020). Moderating effects of gender and resistance to change on the adoption of big data analytics in healthcare. Complexity, 2020, 1–13. https://doi.org/10.1155/2020/2173765

Venkatesh, V., & Davis, F. D. (2020). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

DOI: https://doi.org/10.20961/ictte.2025.114524

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