Sustainability in virtual education: A case study of virtual university of Pakistan
Main Article Content
Abstract
The penetration of technology has helped in better online learning and training around the world by minimizing the time and space issues coupled with traditional education and training. Regardless of the several benefits, keeping students on online platforms is an arduous task. Taking the case study of the Virtual University of Pakistan, this paper explores the impact of personal and environmental factors on online Leaner’s intention to continue through satisfaction. Primary data was gathered from 361 students of 3 regional campuses through a self-administrated closed-ended questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data in SmartPLS software. The study results indicate that only the provision of satisfying personal and environmental factors by distance learning higher education institutions can help them in attracting and retaining students. The post-COVID-19 situation has made the student intake and retention task even harder as the traditional education institution has also built the capabilities to provide online education and diminishing the niche market of virtual education institutions.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licenced under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).
How to Cite
References
Allen, I. E., & Seaman, J. (2007). Online nation: Five years of growth in online learning. Sloan Consortium. PO Box 1238, Newburyport, MA 01950.
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175–1184. https://doi.org/10.1037/0003-066X.44.9.1175
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Chang, B., & Kang, H. (2016). Challenges facing group work online. Distance education, 37(1), 73-88. https://doi.org/10.1080/01587919.2016.1154781
Chang, V. (2016). Review and discussion: E-learning for academia and industry. International Journal of Information Management, 36(3), 476-485. https://doi.org/10.1016/j.ijinfomgt.2015.12.007
Cheng, Y. M. (2012). Effects of quality antecedents on e‐learning acceptance. Internet Research, 22(3), 361-390. https://doi.org/10.1108/10662241211235699
Cheung, C. M., Chiu, P. Y., & Lee, M. K. (2011). Online social networks: Why do students use facebook?. Computers in human behavior, 27(4), 1337-1343. https://doi.org/10.1016/j.chb.2010.07.028
Chiu, C. M., Sun, S. Y., Sun, P. C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224-1245. https://doi.org/10.1016/j.compedu.2006.01.010
Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198-211. https://doi.org/10.1016/j.chb.2016.02.066
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
El Mhouti, A., Erradi, M., & Nasseh, A. (2018). Using cloud computing services in e-learning process: Benefits and challenges. Education and Information Technologies, 23(2), 893-909.
Hanley, M. (2018). Elearning adoption in organizations 2: characteristics of the diffusion process.
Hsu, M. H., Chang, C. M., & Yen, C. H. (2011). Exploring the antecedents of trust in virtual communities. Behaviour & Information Technology, 30(5), 587-601. https://doi.org/10.1080/0144929X.2010.549513
Hu, P. J. H., & Hui, W. (2012). Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decision support systems, 53(4), 782-792. https://doi.org/10.1016/j.dss.2012.05.014
Jin, B., Park, J. Y., & Kim, H. S. (2010). What makes online community members commit? A social exchange perspective. Behaviour & Information Technology, 29(6), 587-599. https://doi.org/10.1080/0144929X.2010.497563
Jolliffe, A., Ritter, J., & Stevens, D. (2012). The online learning handbook: Developing and using web-based learning. Routledge.
Kang, Y. S., Hong, S., & Lee, H. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in human behavior, 25(1), 111-122. https://doi.org/10.1016/j.chb.2008.07.009
Khan, A., Egbue, O., Palkie, B., & Madden, J. (2017). Active learning: Engaging students to maximize learning in an online course. Electronic Journal of e-learning, 15(2), pp107-115.
Kim, B., & Oh, J. (2011). The difference of determinants of acceptance and continuance of mobile data services: A value perspective. Expert Systems with Applications, 38(3), 1798-1804. https://doi.org/10.1016/j.eswa.2010.07.107
Kim, J., Kwon, Y., & Cho, D. (2011). Investigating factors that influence social presence and learning outcomes in distance higher education. Computers & Education, 57(2), 1512-1520. https://doi.org/10.1016/j.compedu.2011.02.005
Knight, S. A., & Burn, J. M. (2011). A preliminary introduction to the OTAM: Exploring users’ perceptions of their on-going interaction with adopted technologies. Australasian Journal of Information Systems, 17(1). https://doi.org/10.3127/ajis.v17i1.541
Limayem, M., & Cheung, C. M. (2008). Understanding information systems continuance: The case of Internet-based learning technologies. Information & management, 45(4), 227-232. https://doi.org/10.1016/j.im.2008.02.005
Nazir, U., Davis, H., & Harris, L. (2015). First day stands out as most popular among MOOC leavers.
Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43, 1-14. https://doi.org/10.1016/j.ijinfomgt.2018.05.005
Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., & Evans, C. (2014). Moving through MOOCs: Understanding the progression of users in massive open online courses. Educational Researcher, 43(9), 421-432. https://doi.org/10.3102/0013189X14562423
Research and Markets Global E-learning Market 2018–2023: Market is Expected to Reach $65.41 Billion. (2018a).
Research and Markets Global Learning Management System (LMS) Market Analysis and Forecasts 2017–2025 – Need for LMS in HEO Driving Market Growth. (2018b).
Ros, S., Hernández, R., Caminero, A., Robles, A., Barbero, I., Maciá, A., & Holgado, F. P. (2015). On the use of extended TAM to assess students' acceptance and intent to use third‐generation learning management systems. British Journal of Educational Technology, 46(6), 1250-1271. https://doi.org/10.1111/bjet.12199
Shiau, W. L., & Luo, M. M. (2013). Continuance intention of blog users: the impact of perceived enjoyment, habit, user invo lvement and blogging time. Behaviour & Information Technology, 32(6), 570-583. https://doi.org/10.1080/0144929X.2012.671851
Shin, D. H., Biocca, F., & Choo, H. (2013). Exploring the user experience of three-dimensional virtual learning environments. Behaviour & Information Technology, 32(2), 203-214. https://doi.org/10.1080/0144929X.2011.606334
Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. Mis Quarterly, 37(4), 1013-1041. https://www.jstor.org/stable/43825780
Sun, H., Fang, Y., & Hsieh, J. P. A. (2014). Consuming information systems: An economic model of user satisfaction. Decision support systems, 57, 188-199. https://doi.org/10.1016/j.dss.2013.09.002
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision support systems, 56, 513-524. https://doi.org/10.1016/j.dss.2012.11.008