Dual Aspects of COVID-19 on Facilitating Conditions and Students’ Willingness to Continue Online Learning
Article Number: e2025121 | Published Online: March 2025 | DOI: 10.22521/edupij.2025.15.121
Md.Abu Issa Gazi , Muhammad Khalilur Rahman , Mohammad Bin Amin , Md Arafat Hossain , Moniya Sultana , Abdul Rahman bin S Senathirajah , Veronika Fenyves
Full text PDF |
478 |
181
Abstract
Background/purpose. The crisis caused by the COVID-19 pandemic has altered the direction of education worldwide, emphasizing the prospects and problems of using online learning platforms. This study aims to investigate the dual aspects of COVID-19 (positive and negative) on facilitating conditions for learning quality that affect students’ willingness to continue online learning. Materials/methods. The study’s hypotheses were evaluated using an online survey of 320 respondents who were enrolled in public universities. The analysis used partial least squares structural equation modeling (PLS-SEM). Results. The study found that the positive and negative impacts of COVID-19 predict students' facilitating conditions, which in turn have a significant positive effect on their perceived usefulness, perceived ease of use, and tech competency while being negatively associated with subjective norms. Additionally, perceived usefulness, ease of use, and tech competency were found to have a significant positive relationship with students' attitudes toward online learning. However, the subjective norm was negatively associated with attitudes. The study revealed that students' attitudes toward the quality of online learning have a significant negative impact on their willingness to continue with online learning. Conclusion. The study's empirical contribution lies in its exploration of the positive and negative impacts of COVID-19 on students’ willingness to continue online learning. This is particularly relevant and important in the current educational scenery. By identifying and understanding these factors, educational institutions can improve the quality and accessibility of online learning, ultimately leading to better educational outcomes for students. |
Keywords: COVID-19 pandemic, positive impact, negative impact, facilitating conditions, student willingness, online learning, education policy
ReferencesAbedi, E. A., Ackah-Jnr, F. R., & Ametepey, A. K. (2024). Learning through informal spaces for technology integration: unpacking the nature of teachers’ learning and its implications for classroom pedagogy. Education, 3(13), 1-16.
Adams, D., Chuah, K. M., Devadason, E., & Azzis, M. S. A. (2024). From novice to navigator: Students’ academic help-seeking behaviour, readiness, and perceived usefulness of ChatGPT in learning. Education and Information Technologies, 29(11), 13617-13634.
Abdalla, R. A. (2025). Higher education students' trust and use of ChatGPT: empirical evidence. International Journal of Technology Enhanced Learning, 17(1), 81-105.
Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: the challenges and opportunities. Interactive learning environments, 31(2), 1-13.
Agarwal, P., Swami, S., & Malhotra, S. K. (2024). Artificial intelligence adoption in the post COVID-19 new-normal and role of smart technologies in transforming business: a review. Journal of Science and Technology Policy Management, 15(3), 506-529.
Al Shamsi, J. H., Al-Emran, M., & Shaalan, K. (2022). Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants. Education and Information Technologies, 27(6), 8071-8091.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86.
Alowayr, A. (2022). Determinants of mobile learning adoption: Extending the unified theory of acceptance and use of technology (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12.
Al-Mamary, Y. H. S. (2022). Why do students adopt and use learning management systems?: Insights from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), 1-9.
Arnold, D., & Sangrà, A. (2018). Dawn or dusk of the 5th age of research in educational technology? A literature review on (e-) leadership for technology-enhanced learning in higher education (2013-2017). International Journal of Educational Technology in Higher Education, 15(1), 1-29.
Azizan, S., Lee, A., Crosling, G., Atherton, G., Arulanandam, B., Lee, C., & Rahim, R. A. (2022). Online learning and covid-19 in higher education: the value of it models in assessing students’ satisfaction. International Journal of Emerging Technologies in Learning, 17(3), 245-278.
Bansah, A. K., & Darko Agyei, D. (2022). Perceived convenience, usefulness, effectiveness and user acceptance of information technology: evaluating students’ experiences of a Learning Management System. Technology, Pedagogy and Education, 31(4), 431-449.
Bamoallem, B., & Altarteer, S. (2022). Remote emergency learning during COVID-19 and its impact on university students perception of blended learning in KSA. Education and Information Technologies, 27(1), 157-179.
Bloomfield, J. G., Fisher, M., Davies, C., Randall, S., & Gordon, C. J. (2023). Registered Nurses’ Attitudes towards E-Learning and Technology in Healthcare: A cross-sectional survey. Nurse Education in Practice, 103597.
Bacaksiz, F. E., Tuna, R., & Alan, H. (2022). Nomophobia, netlessphobia, and fear of missing out in nursing students: A cross-sectional study in distance education. Nurse education today, 118, 1-7.
Barrot, J. S., & Fernando, A. R. R. (2023). Unpacking engineering students’ challenges and strategies in a fully online learning space: The mediating role of teachers. Education and Information Technologies, 28(3), 1-23.
Basuki, R., Tarigan, Z., Siagian, H., Limanta, L., Setiawan, D., & Mochtar, J. (2022). The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6(1), 253-262.
Cattaneo, A. A., Antonietti, C., & Rauseo, M. (2025). How do vocational teachers use technology? The role of perceived digital competence and perceived usefulness in technology use across different teaching profiles. Vocations and Learning, 18(1), 1-26.
Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germny, 2010; pp. 655–690.
Chen, X., Rahman, M. K., Rana, M. S., Gazi, M. A. I., Rahaman, M. A., and Nawi, N. C. (2022). Predicting Consumer Green Product Purchase Attitudes and Behavioral Intention During COVID-19 Pandemic, Frontiers in Psychology, 12, 1-10.
Chen, S., Huang, L., Shadiev, R., & Hu, P. (2024). An extension of UTAUT model to understand elementary school students’ behavioral intention to use an online homework platform. Education and Information Technologies, 29(18), 1-27.
Chung, E., Noor, N. M., & Mathew, V. N. (2020). Are you ready? An assessment of online learning readiness among university students. International Journal of Academic Research in Progressive Education and Development, 9(1), 301-317.
Cohen. J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
Coakes, S., & Sadler, A. (2011). Utilizing a sustainable livelihoods approach to inform social impact assessment practice. In: Vanclay F, Esteves AM, editors. New Directions in Social Impact Assessment: conceptual and methodological advances. Cheltenham: Edward Elgar Publishing; p. 323–340.
Conrad, C., Deng, Q., Caron, I., Shkurska, O., Skerrett, P., & Sundararajan, B. (2022). How student perceptions about online learning difficulty influenced their satisfaction during Canada's Covid‐19 response. British Journal of Educational Technology, 53(3), 534-557.
Ding, L., & Er, E. (2018). Determinants of college students' use of online collaborative help‐seeking tools. Journal of Computer Assisted Learning, 34(2), 129-139.
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., ... & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International journal of information management, 55, 1-20.
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G_ Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.
Frei-Landau, R., & Avidov-Ungar, O. (2022). Educational equity amidst COVID-19: Exploring the online learning challenges of Bedouin and Jewish Female Preservice Teachers in Israel. Teaching and Teacher Education, 53(3), 620-646.
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research, 29(3), 430-447.
Foroughi, B., Iranmanesh, M., Yadegaridehkordi, E., Wen, J., Ghobakhloo, M., Senali, M. G., & Annamalai, N. (2025). Factors Affecting the Use of ChatGPT for Obtaining Shopping Information. International Journal of Consumer Studies, 49(1), 1-17.
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), 1-16.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.
Hemmati, M., Newaz, M. S., Rahman, M. K., Appolloni, A., & Zailani, S. (2024). Sustainability performance of digitalized manufacturing industry in COVID era: a comparative study between developed and developing economies. International Journal of Emerging Markets, 19(10), 3226-3247.
Hewei, T., & Youngsook, L. (2022). Influencing Factors of Online Course Learning Intention of Undergraduates Majoring in Art and Design: Mediating Effect of Flow Experience. SAGE Open, 12(4), 1-13.
Hu, X., Zhang, J., He, S., Zhu, R., Shen, S., & Liu, B. (2022). E-learning intention of students with anxiety: Evidence from the first wave of COVID-19 pandemic in China. Journal of affective disorders, 309, 115-122.
Hopkyns, S. (2022). Cultural and linguistic struggles and solidarities of Emirati learners in online classes during the COVID-19 pandemic. Policy Futures in Education, 20(4), 451-468.
Hosseini, S. S., Ardabili, B. R., Azarbayjani, M., & Tabkhi, H. (2025). Demographic disparities, service efficiency, safety, and user satisfaction in public bus transit system: A survey-based case study in the city of Charlotte, NC. Transportation Research Interdisciplinary Perspectives, 29, 1-16.
Khan, M. A., Kamal, T., Illiyan, A., & Asif, M. (2021). School students’ perception and challenges towards online classes during COVID-19 pandemic in India: An econometric analysis. Sustainability, 13(9), 1-15.
Karim, M. W., Haque, A., Ulfy, M. A., & Hossin, M. S. (2021). Factors influencing student satisfaction towards distance learning apps during the coronavirus (Covid-19) pandemic in Malaysia. International Journal of Academic Research in Progressive Education and Development, 10(2), 245-260.
Kelm, K., & Johann, M. (2025). Artificial intelligence in corporate communications: determinants of acceptance and transformative processes. Corporate Communications: An International Journal, 30(1), 124-138.
Khlaif, Z. N., Sanmugam, M., & Ayyoub, A. (2022). Impact of technostress on continuance intentions to use mobile technology. The Asia-Pacific Education Researcher, 32(2), 1-12.
Kim, E. J., Kim, J. J., & Han, S. H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 1-14.
Kim, K. J., & Frick, T. W. (2011). Changes in student motivation during online learning. Journal of Educational Computing Research, 44(1), 1-23.
Khan, E.A., Cram A., Wang, X., Tran, K., Cavaleri, M. and Rahman, M.J. (2023), "Modelling the impact of online learning quality on students' satisfaction, trust and loyalty", International Journal of Educational Management, 37(2), 281-299.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Kock, N. (2017). Common method bias: A full collinearity assessment method for PLS-SEM. In Partial Least Squares Path Modeling; Springer: Cham, Switzerland pp. 245–257.
Luo, C., Yuan, R., Mao, B., Liu, Q., Wang, W., & He, Y. (2024). Technology Acceptance of Socially Assistive Robots Among Older Adults and the Factors Influencing It: A Meta-Analysis. Journal of Applied Gerontology, 43(2), 115-128.
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555.
MacNeill, H., Masters, K., Nemethy, K., & Correia, R. (2024). Online learning in health professions education. Part 1: Teaching and learning in online environments: AMEE Guide No. 161. Medical Teacher, 46(1), 4-17.
Martin, F., & Borup, J. (2022). Online learner engagement: Conceptual definitions, research themes, and supportive practices. Educational Psychologist, 57(3), 162-177.
Mertens, G., Gerritsen, L., Duijndam, S., Salemink, E., & Engelhard, I. M. (2020). Fear of the coronavirus (COVID-19): Predictors in an online study conducted in March 2020. Journal of anxiety disorders, 74, 1-8.
Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment, 31(5), 2068-2081.
Naznen, F., Al Mamun, A., & Rahman, M. K. (2023). Modelling social entrepreneurial intention among university students in Bangladesh using value-belief-norm framework. Current Psychology, 42(35), 31110-31127.
Newaz, M.S., Hemmati, M., Rahman, M.K., Appolloni, A., Zailani, S., Martins, F.P. and Botelho Junior, A.B. (2025). Impact of Industry 4.0 on sustainability of Malaysia’s manufacturing industry in post-COVID era. Foresight, 1-28. https://doi.org/10.1108/FS-03-2023-0043
Nuseir, M., & Elrefae, G. (2022). The effect of social media marketing, compatibility and perceived ease of use on marketing performance: Evidence from hotel industry. International Journal of Data and Network Science, 6(3), 885-894.
Ngah, A. H., Kamalrulzaman, N. I., Mohamad, M. F. H., Rashid, R. A., Harun, N. O., Ariffin, N. A., & Osman, N. A. A. (2022). The sequential mediation model of students’ willingness to continue online learning during the COVID-19 pandemic. Research and Practice in Technology Enhanced Learning, 17(1), 1-17.
Palaniappan, K., & Noor, N. M. (2022). Gamification strategy to support self-directed learning in an online learning environment. International Journal of Emerging Technologies in Learning (iJET), 17(3), 104-116.
Phillips, L. G., Cain, M., Ritchie, J., Campbell, C., Davis, S., Brock, C., & Joosa, E. (2024). Surveying and resonating with teacher concerns during COVID-19 pandemic. Teachers and Teaching, 30(7-8), 900-917.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
Pérez-Fuentes, M. D. C., Molero Jurado, M. D. M., Oropesa Ruiz, N. F., Martos Martínez, Á., Simón Márquez, M. D. M., Herrera-Peco, I., & Gázquez Linares, J. J. (2020). Questionnaire on Perception of Threat from COVID-19. Journal of Clinical Medicine, 9(4), 1-10.
Rafique, H., Ul Islam, Z., & Shamim, A. (2024). Acceptance of e-learning technology by government school teachers: application of extended technology acceptance model. Interactive Learning Environments, 32(6), 2970-2988.
Rahman, M. K., Gazi, M. A. I., Bhuiyan, M. A., & Rahaman, M. A. (2021a). Effect of Covid-19 pandemic on tourist travel risk and management perceptions. Plos One, 16(9), 1-18.
Rahman, M. K., Bhuiyan, M. H., & Zailani, S. (2021b). Healthcare Services: Patient Satisfaction and Loyalty Lessons from Islamic Friendly Hospitals. Patient Preference and Adherence, 15, 2633–2646.
Rashid, S., & Yadav, S. S. (2020). Impact of Covid-19 pandemic on higher education and research. Indian Journal of Human Development, 14(2), 340-343.
Rahman, M. K., Bhuiyan, M. A., Mainul Hossain, M., & Sifa, R. (2023). Impact of technology self-efficacy on online learning effectiveness during the COVID-19 pandemic. Kybernetes, 52(7), 2395-2415, https://doi.org/10.1108/K-07-2022-1049
Razami, H. H., & Ibrahim, R. (2021). Distance education during COVID-19 pandemic: The perceptions and preference of university students in Malaysia towards online learning. International Journal of Advanced Computer Science and Applications, 12(4). 118-126.
Rahman, M. K. (2019). Medical tourism: tourists’ perceived services and satisfaction lessons from Malaysian hospitals. Tourism Review, 74(3), 739-758.
Ramezaninia, M., Panahifar, F., & Sarhangi, N. H. (2022). Significant factors affecting m-banking adoption case study: higher education institutions in Tehran. International Journal of Electronic Business, 17(1), 61-86.
Rahman, M., Moghavvemi, S., Thirumoorthi, T., & Rahman, M. K. (2020). The impact of tourists’ perceptions on halal tourism destination: a structural model analysis. Tourism Review, 75(3), 575-594.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.
Rokhim, R., Mayasari, I., Wulandari, P., & Haryanto, H. C. (2022). Analysis of the extrinsic and intrinsic aspects of the technology acceptance model associated with the learning management system during the COVID-19 pandemic. VINE Journal of Information and Knowledge Management Systems, 1-26. https://doi.org/10.1108/VJIKMS-04-2022-0113
Sarosa, S. (2022). The effect of perceived risks and perceived cost on using online learning by high school students. Procedia Computer Science, 197, 477-483.
Salas‐Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593-619.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing.
Sharma, S., & Saini, J. R. (2022). On the Role of Teachers’ Acceptance, Continuance Intention and Self-Efficacy in the Use of Digital Technologies in Teaching Practices. Journal of Further and Higher Education, 46(6), 721-736.
Shahreki, J., & Lee, J. Y. (2024). Adopting human resource information system and work-related outcomes in emerging market SMEs: unified theory of acceptance and use of technology. Cross Cultural & Strategic Management, 31(1), 116-142.
Sheridan, J.; Coakes, C. O. (2011). SPSS: Analysis without Anguish (Version 18); John Wiley & Sons: New York, NY, USA, 2011.
Simamora, R. M., De Fretes, D., Purba, E. D., & Pasaribu, D. (2020). Practices, challenges, and prospects of online learning during Covid-19 pandemic in higher education: Lecturer perspectives. Studies in Learning and Teaching, 1(3), 185-208.
Sidek, S., Hasbolah, H., Rahman, M. K., Samad, N. S. A., Abdullah, Z., Zoraimi, N. H. N., ... & Hassin, N. H. (2024). Analyzing Barriers to Cyberpreneurship Adoption. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 1-12. https://doi.org/10.1016/j.joitmc.2024.100313
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), 1-9.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & education, 50(4), 1183-1202.
Svihus, C. L. (2024). Online teaching in higher education during the COVID-19 pandemic. Education and Information Technologies, 29(3), 3175-3193.
Szymkowiak, A., & Jeganathan, K. (2022). Predicting user acceptance of peer‐to‐peer e‐learning: An extension of the technology acceptance model. British Journal of Educational Technology, 53(6), 1993-2011.
Sorokowski, P., Groyecka, A., Kowal, M., Sorokowska, A., Białek, M., Lebuda, I., ... & Karwowski, M. (2020). Can information about pandemics increase negative attitudes toward foreign groups? A case of COVID-19 outbreak. Sustainability, 12(12), 1-10.
Skantz-Åberg, E., Lantz-Andersson, A., Lundin, M., & Williams, P. (2022). Teachers’ professional digital competence: an overview of conceptualisations in the literature. Cogent Education, 9(1), 1-24.
Teng, S. L., Zailani, S., Rahman, M. K., Bhuiyan, M. A., & Mamun, A. A. (2024). Impact of service innovation and digital supply chain capability on risk protection in supporting online foods delivery. Kybernetes, 53(7), 2483-2501.
Thi, H. P., Tran, Q. N., La, L. G., Doan, H. M., & Vu, T. D. (2022). Factors motivating students' intention to accept online learning in emerging countries: the case study of Vietnam. Journal of Applied Research in Higher Education, 15(2), 324-341.
Turale, S., Meechamnan, C., & Kunaviktikul, W. (2020). Challenging times: ethics, nursing and the COVID‐19 pandemic. International nursing review, 67(2), 164-167.
Weerathunga, P. R., Samarathunga, W. H. M. S., Rathnayake, H. N., Agampodi, S. B., Nurunnabi, M., & Madhunimasha, M. M. S. C. (2021). The COVID-19 pandemic and the acceptance of e-learning among university students: The role of precipitating events. Education Sciences, 11(8), 1-23.
Wu, C. H., You, A. H., Dong, T. P., & Liu, C. H. (2024). Analysis of Factors Influencing Intention to Engage in Online Learning in Chinese Calligraphy and Their Mediation Effects. The Asia-Pacific Education Researcher, 33(6), 1-15.
Wu, L., Hsieh, P. J., & Wu, S. M. (2022). Developing effective e-learning environments through e-learning use mediating technology affordance and constructivist learning aspects for performance impacts: Moderator of learner involvement. The Internet and Higher Education, 55, 1-16.
Wong, T.A., Tan, K.T.L., Darmaraj, S.R., Loo, J.T.K. and Ng, A.H.H. (2025), "Social capital development in online education and its impact on academic performance and satisfaction", Higher Education, Skills and Work-Based Learning, Vol. 15 No. 1, pp. 205-221. https://doi.org/10.1108/HESWBL-12-2023-0332
Yao, Y., Wang, P., Jiang, Y., Li, Q., & Li, Y. (2022). Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. Journal of Innovation & Knowledge, 7(4), 1-9.
Yang, M., Al Mamun, A., Gao, J., Rahman, M. K., Salameh, A. A., & Alam, S. S. (2024). Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology. Scientific Reports, 14(1), 1-18. https://doi.org/10.1038/s41598-023-50436-2
Yuan, D., Rahman, M. K., Gazi, M. A. I., Rahaman, M. A., Hossain, M. M. & Akter, S. (2021). Analyzing of User Attitudes Toward Intention to Use Social Media for Learning. SAGE Open, 11(4), 1-13.
Zaman, U., Aktan, M., Baber, H., & Nawaz, S. (2024). Does forced-shift to online learning affect university brand image in South Korea? Role of perceived harm and international students’ learning engagement. Journal of Marketing for Higher Education, 34(1), 390-414.
Zhang, J., & Yu, S. (2023). Reconceptualising digital pedagogy during the COVID-19 pandemic: A qualitative inquiry into distance teaching in China. Innovations in Education and Teaching International, 60(2), 174-184.
Zhu, W., Liu, Q., & Hong, X. (2022). Implementation and Challenges of Online Education during the COVID-19 Outbreak: A National Survey of Children and Parents in China. Early childhood research quarterly, 61, 209-219.
Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. Sage Open, 12(1), 1-15.