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International Journal of Academic Research in Progressive Education and Development

Open Access Journal

ISSN: 2226-6348

Investigating Factors Affecting Continuance Intention of Malaysian Higher Education Students Towards Online Distance Learning for Further Studies

Shishi K.P, Zahir Osman, Rethina V.S. Harvinder Kaur

http://dx.doi.org/10.6007/IJARPED/v12-i3/19082

Open access

Getting students to register for open and distance learning universities is challenging, but keeping them in the system is an even greater challenge. High attrition rates have been a noxious issue in open and distance learning. Hence, this study aims to assess the link between factors that will influence learners’ desire to continue their studies in an open and distance learning university. The research framework is based on the four key independent variables: information quality, system quality, sociability quality, and self-managed learning; satisfaction as a mediating variable; and continuation intention as the dependent variable. The variables have been adapted from the Information System Success Model to fit the context of open and distance learning. Aside from the ISS Model, the mediating variable is derived from the consumer behaviour literature's Expectation Confirmation or Disconfirmation Theory. In addition, Planned Behavior and Social Cognitive Theories serve as the foundation for the study's other elements. The research methodology collected quantitative data using survey instruments. Structural Equation Modeling (SEM) statistics techniques were used for data analysis. Because of its capacity to analyze data, the PLS-SEM approach was used for the second step of data analysis. This study will provide important insights into how open and distance learning institutions may use useful retention tactics to increase the graduation rate of OUM students.

Ahmad, I., & Hussain, I. (2017). Factors affecting students’ usage and adoption of e-learning environment in Pakistan. Journal of Education and Practice, 8(10), 140-146.
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2020). Factors influencing adoption of online learning during COVID-19: A conceptual framework based on extended technology acceptance model. Journal of Educational Technology & Society, 23(4), 1-13.
Albaugh, P., & Duray, R. (2020). Understanding student readiness for online learning: The importance of self-directed learning and motivation. Journal of Information Technology Education: Research, 19, 401-426.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2019). Factors influencing students’ attitudes and satisfaction towards e-learning resources and its effectiveness in higher education. Journal of Computer Assisted Learning, 35(6), 747-762.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Factors influencing the adoption of e-learning in developing countries: A case from Jordan. Education and Information Technologies, 25(3), 1785-1807.
Allen, I. E., & Seaman, J. (2017). Digital learning compass: Distance education enrolment report 2017. Babson Survey Group.
Alqurashi, E. (2019). Exploring factors affecting Saudi learners’ satisfaction in online learning environments: A structural equation model. International Journal of Information and Learning Technology, 36(5), 341-356.
Alqurashi, E. (2019). Investigating factors that affect students’ satisfaction and continuance intention in using learning management systems: A structural equation modelling approach. International Journal of Information and Learning Technology, 36(2), 77-91.
Alqurashi, E. (2019). Online learning in Saudi higher education: Benefits, challenges and limitations. Education Sciences, 9(2), 1-14.
Alqurashi, E. (2019). Predictors of student satisfaction and perceived learning with e-learning in Saudi Arabia. Journal of King Saud University-Computer and Information Sciences, 31(3), 340-350
Al-Rahmi, W. M., & Zeki, A. M. (2014). The effectiveness of educational games for educational purposes: A review of the literature. International Journal of Education and Information Technologies, 8(4), 321-326.
Arbaugh, J. B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses: An exploratory study of two online MBA programs. Management Learning, 33(3), 331-347.
Arbaugh, J. B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses: An exploratory study of two online MBA programs. Management Learning, 33(3), 331-347.
Azilawati, A. A., & Norizan, A. R. (2020). The impact of online learning on students’ academic performance during the Covid-19 pandemic in Malaysia. International Journal of Academic Research in Business and Social Sciences, 10(8), 1-14.
Babakus, E., & Boller, G. W. (1992). An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24, 253-268.
Babson Survey Group. (2020). The 2020 digital learning pulse survey. https://www.babson.edu/about/news-events/babson-announcements/digital-learning-pulse-survey/
Chang, C. C., & Hsu, M. H. (2011). The impact of e-learning on student performance: Evidence from a cross-sectional study. The International Review of Research in Open and Distributed Learning, 12(7), 1-18.
Chang, C., & Hsu, C. (2011). The effect of online learning motivation on learning satisfaction and academic performance. Journal of Information Technology Education: Research, 10(1), 1-21.
Chang, H. H., & Hsu, C. L. (2011). The effect of trust and reputation on the intention to continue using e-services: Evidence from online social networks. International Journal of Human-Computer Studies, 69(10), 669-678.
Chen, C.-H., & Jang, S.-J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741-752.
Chen, Y., Hu, Z., & Liao, J. (2018). Factors affecting the continuance intention to use MOOCs among Chinese learners. Journal of Educational Technology & Society, 21(3), 1-13.
Chiu, C. M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201.
Chu, R. J., & Hwang, Y. (2011). A knowledge management approach to developing intellectual capital for sustainable advantage: Evidence from Taiwanese service industries. Journal of Intellectual Capital, 12(4), 628-644.
Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century: A framework for research and practice. Routledge Falmer.
Hair Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2021). Multivariate data analysis (8th ed.). Cengage Learning.
Hair Jr., J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modelling (PLS-SEM). Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). Partial least squares structural equation modelling (PLS-SEM) using R. Springer.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Huang, R., & Liaw, S. (2018). Factors affecting learner satisfaction in massive open online courses. Educational Technology & Society, 21(2), 42-55.
Hung, H. T., Hwang, G. J., & Huang, I. (2018). A project-based digital storytelling approach for improving students’ learning motivation, problem-solving competence, and learning achievement. Journal of Educational Technology & Society, 21(2), 181-194.
Hung, H. T., Yang, J. C., & Huang, C. Y. (2018). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 122, 22-37.
Hung, J. L., Zhang, K., Teng, Y. T., & Huang, B. (2018). Exploring learners’ satisfaction and learning performance in an augmented reality-based learning environment. Interactive Learning Environments, 26(8), 1027-1041.
Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2018). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 122, 47-57.
Kang, M., & Im, T. (2017). Factors influencing learners’ decision to drop out or persist in online learning. Journal of Educational Technology & Society, 20(1), 1-12.
Kang, M., & Im, T. (2017a). Factors influencing learners’ satisfaction and intention to continue using e-learning in Korea. The International Journal of Information and Learning Technology, 34(1), 21-35.
Kim, K. J., & Frick, T. W. (2011). Changes in student motivation during online learning. Journal of Educational Computing Research, 44(1), 1-23.
Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
Kuo, Y. C., & Belland, B. R. (2016). An exploratory study of adult learners’ perceptions of online learning: Minority students in continuing education. Educational Technology Research and Development, 64(4), 661-680.
Kuo, Y. C., & Chen, C. H. (2015). The influence of knowledge sharing and learning on the intention to use MOOCs among Taiwanese college students. Journal of Educational Technology & Society, 18(2), 129-140.
Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618.
Lee, Y., & Choi, J. (2011). The impact of information and communication technology service quality and reputation on customer satisfaction and loyalty. Journal of Information Technology Applications & Management, 18(2), 1-18.
Liao, Y., & Chen, C. (2019). Factors influencing learner satisfaction in online courses: A structural equation modeling approach. Journal of Educational Technology & Society, 22(1), 1-13.
Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
Lin, C. H., & Lin, Y. C. (2020). The impact of social presence on learners’ continuance intention in online learning environments. Interactive Learning Environments, 28(5), 633-645.
Lin, C.-H., & Lin, G.-Y. (2020). Antecedents and outcomes of social presence in an online learning environment: A structural model. Computers & Education, 146, 103764. https://doi.org/10.1016/j.compedu.2019.103764
Lin, J. Y., & Lin, C. H. (2020). The effect of social presence on students’ continuance intention in mobile learning. Journal of Computer Assisted Learning, 36(4), 509-519.
Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning (3rd ed.). Wadsworth.
Park, S. Y., & Choi, J. H. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207-217.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544.
Ramayah, T., Yeap, J. A. L., Ignatius, J., & Tan, K. S. (2014). Student readiness for online learning: An empirical examination. Research in Higher Education Journal, 24, 1-14.
Rezaei, M., & Daneshfar, F. (2018). Factors affecting student satisfaction in online higher education in Iran: A case study of Allameh Tabataba’i University. Journal of Applied Research in Higher Education, 10(1), 18-31.
Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS. Retrieved from https://www.smartpls.com
Sachdev, S. B., & Verma, H. V. (2004). The relative importance of service quality. Journal of Services Research, 4(1), 93-116.
Shmueli, G., Ray, S., Velasquez Estrada, J.M., & Chatla, S.B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69, 4552–4564.
Shmueli, G., Sarstedt, M., Hair Jr., J.F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C.M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53, 2322–2347.
Simonson, M., Smaldino, S., & Zvacek, S. (2019). Teaching and learning at a distance: Foundations of distance education (7th ed.). Information Age Publishing.
Sun, A., Chen, X., & Wang, Y. (2018). What drives learners’ continuance intention to use MOOCs? The roles of social factors, learners’ satisfaction, and perceived value. Educational Technology & Society, 21(2), 126-139.
Wang, D., Wang, Y., & Liang, Y. (2020). Students’ continuance intention in MOOCs: Integrating the technology acceptance model (TAM) with the expectation confirmation model (ECM). Computers & Education, 145, 103735.
Wang, Q., & Li, Y. (2017). The Effects of Perceived Usefulness and Perceived Ease of Use on Online Students’ Satisfaction with Course Content. Journal of Educational Technology Development and Exchange (JETDE), 10(1), 1-14.
Wang, Q., Chen, L., & Liang, Y. (2020). The effects of online course quality and online instructors on learners’ satisfaction and continuance intention: An empirical study in China. International Journal of Information Management, 50, 439-449.
Wang, Y., & Li, H. (2017). Factors influencing continuance intention to use ODL among undergraduate students in China: A modified UTAUT model. Educational Technology Research and Development, 65(5), 1273-1291.
Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2020). Investigating the role of information quality, system quality, and service quality in predicting student satisfaction and continuance intention in e-learning. Technological Forecasting and Social Change, 161, 120296. https://doi.org/10.1016/j.techfore.2020.120296
Wu, J. H., & Wu, W. (2019). The effects of social media on college students’ satisfaction with academic life and engagement in academic activities. Journal of Educational Computing Research, 57(6), 1517-1537.
Wu, J. H., & Wu, W. (2019). The roles of satisfaction and web quality in enhancing e-learning continuance intention. Computers & Education, 142, 103641.
Wu, J. H., & Wu, Y. C. J. (2019). The impact of information and sociability quality on online distance learning students’ satisfaction and continuance intention. Computers & Education, 142, 1-11.
Yang, J., & Liu, M. (2017). The relationship between students’ satisfaction and continuance intention to use ODL platforms. Journal of Educational Technology & Society, 20(1), 133-142.
Yeh, Y. F., & Huang, Y. M. (2018). The influence of learners’ intelligence quotient on satisfaction and continuance intention in online distance learning. International Journal of Distance Education Technologies, 16(1), 1-14.
Zheng, B., Chen, Y., & Clark, J. (2021). The impact of instructor technical competence and technology-based pedagogical approaches on online students’ participation and satisfaction. Journal of Educational Computing Research, 59(2), 191-212.

(Shishi et al., 2023)
Shishi, K. P., Osman, Z., & Kaur, R. V. S. H. (2023). Investigating Factors Affecting Continuance Intention of Malaysian Higher Education Students Towards Online Distance Learning for Further Studies. International Journal of Academic Research in Progressive Education and Development, 12(3).