This study investigates the determinants influencing students' continuous intention to use e-learning platforms in open online flexible higher education institutions. The aim is to examine the roles of effort expectancy, facilitating conditions, and performance expectancy in shaping students' engagement with e-learning platforms and the mediating effects of e-satisfaction and learning self-efficacy. A sample of 377 students from open online flexible higher education institutions participated in the study. The methodology followed the guidelines for structural equation modeling (SEM) analysis. Data were collected using a Likert scale questionnaire and analyzed using Partial Least Squares (PLS) method. Smartpls4 was utilized to analyze the data. The findings reveal that effort expectancy, facilitating conditions, and performance expectancy significantly influence students' continuous intention to use e-learning platforms. Additionally, e-satisfaction and learning self-efficacy mediate the relationships between these determinants and continuous intention to use. Specifically, user-friendly interfaces, comprehensive support services, and the perceived usefulness of e-learning platforms contribute to students' intention to engage with the platform continuously. The study also highlights the relevance of theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (UTAUT) in understanding technology adoption and usage behavior in higher education. The study's implications suggest that institutions should focus on enhancing usability, providing support services, and demonstrating the practical benefits of e-learning platforms to foster students' engagement and satisfaction. These findings offer practical guidance for institutions seeking to optimize their online learning environments and improve students' learning experiences in open online flexible higher education settings.
Ahmad, S., Noor, M. A. S., Alwan, A. A., Gulzar, Y., Khan, W. Z., & Reegu, F. A. (2023). eLearning acceptance and adoption challenges in Highertion . Sustainability, 15(7), 6190.
Al Amin, M., Alam, R. M., & Alam, M. Z. (2023). Antecedents of students’ e-learning continuance intention during COVID-19: An empirical study. E-learning and Digital Media, 20(3), 224-254.
Al Arif, T. Z. Z., Kurniawan, D., & Handayani, R. (2024). EFL University Students’ Acceptance and Readiness for e-Learning: A Structural Equation Modeling Approach. Electronic Journal of e-Learning, 22(1), 01-16.
Alalwan, A. A. (2020). Mobile food ordering apps: an empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. Int. J. Inf. Manag. 50, 28–44. doi: 10.1016/j.ijinfomgt.2019.04.008
AL-Hawamleh, A. (2024). Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems: An Integration of the Information Systems Success Model and the Technology Acceptance Model. International journal of electrical and computer engineering systems, 15(2), 201-214.
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.
Anthony Jnr, B. (2024). Examining Blended Learning Adoption Towards Improving Learning Performance in Institutions of Higher Education. Technology, Knowledge and Learning, 1-35.
Bushra, K., & Devi, B. N. (2023). Fostering Digital Sustainability in Higher Education Through e-Learning Concepts. In Preparing for a Sustainable Future (pp. 3-25). Singapore: Springer Nature Singapore.
Chen, M., Wang, X., Wang, J., Zuo, C., Tian, J., & Cui, Y. (2021). Factors affecting college students’ continuous intention to use online course platform. SN Computer Science, 2, 1-11.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-
2909.112.1.155
Da?han, G., and Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Comput. Hum. Behav. 60, 198–211. doi: 10.1016/j.chb.2016.02.066
Dash, G., Akmal, S., Mehta, P., & Chakraborty, D. (2022). COVID-19 and e-learning adoption in higher education: A multi-group analysis and
recommendation. Sustainability, 14(14), 8799.
Deng, P., Chen, B., & Wang, L. (2023). Predicting students’ continued intention to use E-learning platform for college English study: the mediating effect of E-satisfaction and habit. Frontiers in Psychology, 14, 1182980.
Deshpande, A., Raut, R., Gupta, K., Mittal, A., Raheja, D., Ekbote, N., & Kaul, N. (2024). Predictors of continued intention of working professionals for pursuing e-learning courses for career advancement. Information Discovery and Delivery, 52(2), 175-184.
Gurban, M. A., & Almogren, A. S. (2022). Students’ actual use of E-learning in higher education during the COVID-19 pandemic. SAGE Open, 12(2), 21582440221091250.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: SAGE.
Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling. Thousand Oakes, CA: Sage Publications
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1): 115-135.
Huda, M. (2024). Between accessibility and adaptability of digital platform: investigating learners' perspectives on digital learning infrastructure. Higher Education, Skills and Work-Based Learning, 14(1), 1-21.
Jing, H. F., Noh, M. A. M., Ibrahim, R., Ilias, M. F., & Pisol, M. I. M. (2024). INTEGRATING OPEN DISTANCE LEARNING IN HIGHER EDUCATION INSTITUTIONS AS A TRANSFORMATIVE SHIFT IN TEACHING AND LEARNING. Journal of Nusantara Studies (JONUS), 9(1), 121-138.
Khan, W., Sohail, S., Roomi, M. A., Nisar, Q. A., & Rafiq, M. (2023). Opening a new horizon in digitalization for e-learning in Malaysia: Empirical evidence of Covid-19. Education and Information Technologies, 1-30.
Khera, D. (2024). Investigation of Students' Intention and Related Determinants for E-Learning Continuance in Education after COVID-19. In The Role of Sustainability and Artificial Intelligence in Education Improvement (pp. 42-65). Chapman and Hall/CRC.
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
Looi, K. H., Wye, C. K., & Abdul Bahri, E. N. (2022). Achieving learning outcomes of emergency remote learning to sustain higher education during crises: An empirical study of Malaysian undergraduates. Sustainability, 14(3), 1598.
Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705-10730.
Ramasamy, S. P., Shahzad, A., & Hassan, R. (2023). COVID-19 pandemic impact on students intention to use E-learning among Malaysian higher education institutions. Journal of Education, 203(3), 596-604.
Rankapola, M. E., & Zuva, T. (2023). The Effect of E-Learning Quality, Self-efficacy and E-Learning Satisfaction on the Students’ Intention to Use the E-Learning System. In Computer Science On-line Conference (pp. 640-653). Cham: Springer International Publishing.
Ringle, C. M., and Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems. 116: 1865–1886.
Al-Maroof, S. R., Alhumaid, K., & Salloum, S. (2020). The continuous intention to use e-learning, from two different perspectives. Education Sciences, 11(1), 6.
Shmueli, G., Ray, S., Estrada, J. M. V., and Chatla, S. B. (2016). The elephant in theroom: predictive performance of PLS models. Journal of Business Research, 69: 4552–4564.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., and Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 53: 2322–2347.
Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W., &
Habes, M. (2023). Analysis of E-learning system use using combined TAM and ECT factors. Sustainability, 15(14), 11100.
Tewari, A., Singh, R., Mathur, S., & Pande, S. (2023). A modified UTAUT framework to predict students' intention to adopt online learning: moderating role of openness to change. The International Journal of Information and Learning Technology, 40(2), 130-147.
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713-737.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). Unified Theory of Acceptance and Use of Technology (UTAUT) [Database record]. APA PsycTests. https://doi.org/10.1037/t57185-000
Wang, Y. (2023). Determinants of Undergraduate Students’ Continuance Intention to Use E-learning of at a Public University in Chengdu, China. ABAC ODI Journal Vision. Action. Outcome, 11(1), 326-344.
Xu, J., & Qiu, X. (2021). The influence of self-regulation on learner’s behavioral intention to reuse e-learning systems: a moderated mediation model. Frontiers in psychology, 12, 763889.
Ye, J. H., Lee, Y. S., & He, Z. (2022). The relationship among expectancy belief, course satisfaction, learning effectiveness, and continuance intention in online courses of vocational-technical teachers college students. Frontiers in Psychology, 13, 904319.
Younas, M., Noor, U., Zhou, X., Menhas, R., & Qingyu, X. (2022). COVID-19, students satisfaction about e-learning and academic achievement: mediating analysis of online influencing factors. Frontiers in psychology, 13, 948061.
Zeng, L., & Cleesuntorn, A. (2024). Factors Influencing Undergraduates' Perceived Learning and Continuance Intention Towards Using M-Learning. AU-GSB e-JOURNAL, 17(1), 1-10.
Zheng, H., Qian, Y., Wang, Z., & Wu, Y. (2023). Research on the Influence of E-Learning Quality on the Intention to Continue E-Learning: Evidence from SEM and fsQCA. Sustainability, 15(6), 5557.
(Osman & Yatam, 2024)
Osman, Z., & Yatam, M. (2024). Fostering Continuous Intention to Use E-learning Platforms among Students of Open Online Flexible Distance Learning Higher Education Institutions. International Journal of Academic Research in Business & Social Sciences, 14(6), 1798–1815.
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