Students’ Intention to Take E-Learning Courses During the COVID-19 Pandemic: A Protection Motivation Theory Perspective

Keywords: e-learning, COVID-19, protection motivation theory (PMT), technology acceptance model (TAM), Vietnam, Taiwan


This study proposes a new model for integrating the protection motivation theory (PMT) with the technology acceptance model (TAM) to explore factors affecting students’ intention to attend e-learning courses during the COVID-19 pandemic. A total of 432 valid responses to an online questionnaire were received from freshmen students studying in universities in Vietnam and Taiwan. Structural equation modeling was used to evaluate the proposed research model and test the hypotheses, and model evaluation reflected a good fit between the data and the proposed research model. Differences between perceived vulnerability, perceived severity, and intention to take e-learning courses across two countries were also established, suggesting that both the TAM and the PMT should be considered for use in studies related to technology adoption in the pandemic context. The factors influencing students’ intentions to take online courses can be quite varied when different educational settings are considered; therefore, a more contextual understanding of students’ e-learning intentions during pandemic times should be carefully examined. Suggestions for governments and policy makers are also proposed.

Author Biographies

Hoai Than Nguyen, National Sun Yat-sen University

Hoai Than Nguyen is a PhD candidate under the International Graduate Program of Education and Human Development of the College of Social Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan. His research interests include technology adoption, innovation, human intention and behavior, and educational policy.

Chia Wei Tang, Graduate Institute of Educational Administration and Policy, National Chengchi University, Taiwan

Dr. Chia Wei Tang is an associate professor in National Chengchi University, Taiwan. His work focuses on educational admiration and policy, university rankings, higher education policy and governance.


Adejo, O. W., Ewuzie, I., Usoro, A., & Connolly, T. (2018). E-learning to m-learning: Framework for data protection and security in cloud infrastructure. International Journal of Information Technology and Computer Science, 10(4), 1–9.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the COVID-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Education Sciences, 10(9), Article 216.

Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). A systematic review of social media acceptance from the perspective of educational and information systems theories and models. Journal of Educational Computing Research, 57(8), 2085–2109.

Al-Rasheed, M. (2020). Protective behavior against COVID-19 among the public in Kuwait: An examination of the protection motivation theory, trust in government, and sociodemographic factors. Social Work in Public Health, 35(7), 546–556.

Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2013). IT infrastructure services as a requirement for e-learning system success. Computers & Education, 69, 431–451.

Anderson, C. L., & Agarwal, R. (2010). Practicing safe computing: A multimethod empirical examination of home computer user security behavioral intentions. MIS Quarterly, 34(3), 613–643.

Baby, A., & Kannammal, A. (2020). Network path analysis for developing an enhanced TAM model: A user-centric e-learning perspective. Computers in Human Behavior, 107, Article 106081.

Bashirian, S., Jenabi, E., Khazaei, S., Barati, M., Karimi-Shahanjarini, A., Zareian, S., Rezapur-Shahkolai, F., & Moeini, B. (2020). Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: An application of the protection motivation theory. Journal of Hospital Infection, 105(3), 430–433.

Bish, A., & Michie, S. (2010). Demographic and attitudinal determinants of protective behaviours during a pandemic: A review. The British Journal of Health Psychology, 15(4), 797–824.

Boyraz, G., Legros, D. N., & Tigershtrom, A. (2020). COVID-19 and traumatic stress: The role of perceived vulnerability, COVID-19-related worries, and social isolation. Journal of Anxiety Disorders, 76, Article 102307.

Chang, M., Wang, C.-Y., & Chen, G.-D. (2009). National program for e-learning in Taiwan. Journal of Educational Technology Society, 12(1), 5–17.

Cheng, Y. M. (2011). Antecedents and consequences of e‐learning acceptance. Information Systems Journal, 21(3), 269–299.

Chenoweth, T., Minch, R., & Gattiker, T. (2009, January 5–8). Application of protection motivation theory to adoption of protective technologies [Conference paper]. 2009 42nd Hawaii International Conference on System Sciences, Waikoloa, HI.

Conner, M., & Norman, P. (2015). Predicting and changing health behaviour: Research and practice with social cognition models (3rd ed.). McGraw-Hill Education.

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral thesis, Massachusetts Institute of Technology, Sloan School of Management].

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Transaction Publishers.

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21.

Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-learning during COVID-19 pandemic. Computer Networks, 176, 107–290.

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130–132.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ experiences, learning outcomes and satisfaction in e-learning. Journal of e-Learning Knowledge Society, 13(2).

Gohiya, P., & Gohiya, A. (2020, May 21). E-learning during COVID 19 pandemic. Research Square.

Gurcan, F., Ozyurt, O., & Cagitay, N. E. (2021). Investigation of emerging trends in the e-learning field using latent dirichlet allocation. The International Review of Research in Open Distributed Learning, 22(2), 1–18.

Hamaidi, D. A., Arouri, Y. M., Noufal, R. K., & Aldrou, I. (2021). Parents’ perceptions of their children’s experiences with distance learning during the COVID-19 pandemic. The International Review of Research in Open Distributed Learning, 22(2), 224–241.

Hammouri, Q., & Abu-Shanab, E. (2018). Exploring factors affecting users’ satisfaction toward e-learning systems. International Journal of Information Communication Technology Education, 14(1), 44–57.

Hanus, B., & Wu, Y. A. (2016). Impact of users’ security awareness on desktop security behavior: A protection motivation theory perspective. Information Systems Management, 33(1), 2–16.

Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Pham, A. T. V., & Dinh, H. V. (2020). Students’ adoption of e-learning in emergency situation: The case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education, 18(2), 246–269.

Ifinedo, P. (2012). Understanding information systems security policy compliance: An integration of the theory of planned behavior and the protection motivation theory. Computers and Security, 31(1), 83–95.

Kelly, T. M., & Bauer, D. K. (2003). Managing intellectual capital—via e-learning—at Cisco. In C. W. Holsapple (Ed.), Handbook on knowledge management: Knowledge directions (pp. 511–532). Springer Berlin Heidelberg.

Khan, S., Umer, R., Umer, S., & Naqvi, S. (2021). Antecedents of trust in using social media for e-government services: An empirical study in Pakistan. Technology in Society, 64, Article 101400.

Lee, B., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320–1329.

Lee, Y., & Larsen, K. R. (2009). Threat or coping appraisal: Determinants of SMB executives’ decision to adopt anti-malware software. European Journal of Information Systems, 18(2), 177–187.

Li, J.-B., Yang, A., Dou, K., Wang, L.-X., Zhang, M.-C., & Lin, X.-Q. (2020). Chinese public's knowledge, perceived severity, and perceived controllability of COVID-19 and their associations with emotional and behavioural reactions, social participation, and precautionary behaviour: a national survey. BMC Public Health, 20(1), 1-14.

Liang, H.-F., Wu, Y.-C., & Wu, C.-Y. (2021). Nurses’ experiences of providing care during the COVID-19 pandemic in Taiwan: A qualitative study. International Journal of Mental Health Nursing, 30(6), 1684–1692.

Liu, S.-H., Liao, H.-L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599–607.

Martin, F., Bolliger, D. U., & Flowers, C. (2021). Design matters: Development and validation of the online course design elements (OCDE) instrument. The International Review of Research in Open Distributed Learning, 22(2), 46–71.

Meso, P., Ding, Y., & Xu, S. (2013). Applying protection motivation theory to information security training for college students. Journal of Information Privacy Security, 9(1), 47–67.

Ministry of Health. (2021a). COVID-19 update as of 12pm on May 24, 2021. Retrieved May 24, 2021, from

Ministry of Health. (2021b). The report on the COVID-19 situation at the regular meeting of Vietnamese Government in June 2021 to discuss the socio-economic situation in the first six months of the year and directions and tasks for the last six months of 2021.

Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.

Mortelmans, D., & Dehertogh, B. (2008). Factoranalyse [Factor Analysis]. Leuven: Acco.

Ngai, E. W., Poon, J., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267.

Pang, S. M., Tan, B. C., & Lau, T. C. (2021). Antecedents of consumers’ purchase intention towards organic food: Integration of theory of planned behavior and protection motivation theory. Sustainability, 13(9), Article 5218.

Pham, H.-H., & Ho, T.-T.-H. (2020). Toward a “new normal” with e-learning in Vietnamese higher education during the post COVID-19 pandemic. Higher Education Research Development, 39(7), 1327–1331.

Prasetyo, Y. T., Castillo, A. M., Salonga, L. J., Sia, J. A., & Seneta, J. A. (2020). Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during enhanced community quarantine in Luzon, Philippines: Integrating protection motivation theory and extended theory of planned behavior. International Journal of Infectious Diseases, 99, 312–323.

Radha, R., Mahalakshmi, K., Kumar, V. S., & Saravanakumar, A. (2020). E-learning during lockdown of COVID-19 pandemic: A global perspective. International Journal of Control Automation, 13(4), 1088–1099.

Rana, H., & Lal, M. (2014). E-learning: Issues and challenges. International Journal of Computer Applications, 97(5), 20–24.

Rather, R. A. (2021). Demystifying the effects of perceived risk and fear on customer engagement, co-creation and revisit intention during COVID-19: A protection motivation theory approach. Journal of Destination Marketing Management, 20, Article 100564.

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114.

Sathish, R., Manikandan, R., Priscila, S. S., Sara, B. V., & Mahaveerakannan, R. (2020, December 3–5). A report on the impact of information technology and social media on COVID–19. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 224–230). IEEE.

Sharifirad, G., Yarmohammadi, P., Sharifabad, M. A. M., & Rahaei, Z. (2014). Determination of preventive behaviors for pandemic influenza A/H1N1 based on protection motivation theory among female high school students in Isfahan, Iran. Journal of Education and Health Promotion, 3, Article 7.

Shokoohi, M., Osooli, M., & Stranges, S. (2020). COVID-19 pandemic: What can the West learn from the East? International Journal of Health Policy and Management, 9(10), 436–438.

Singh, S., Orwat, J., & Grossman, S. (2011). A protection motivation theory application to date rape education. Psychology, Mealth & Medicine, 16(6), 727–735.

Taiwan Centers for Disease Control. (2021). Confirmed COVID-19 cases on 27 of June. CDC. Retrieved June 30, 2021, from,

Tarhini, A., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164–182.

van der Weerd, W., Timmermans, D. R., Beaujean, D. J., Oudhoff, J., & van Steenbergen, J. E. (2011). Monitoring the level of government trust, risk perception and intention of the general public to adopt protective measures during the influenza A (H1N1) pandemic in the Netherlands. BMC Public Health, 11, Article 575.

Wang, J., Liu-Lastres, B., Ritchie, B. W., & Mills, D. J. (2019). Travellers’ self-protections against health risks: An application of the full protection motivation theory. Annals of Tourism Research, 78, Article 102743.

West, R., Michie, S., Rubin, G. J., & Amlôt, R. (2020). Applying principles of behaviour change to reduce SARS-CoV-2 transmission. Natural Human Behaviour, 4(5), 451–459.

Zhang, X., Guo, X., Guo, F., & Lai, K.-H. (2014). Nonlinearities in personalization–privacy paradox in mHealth adoption: The mediating role of perceived usefulness and attitude. Technology and Health Care, 22, 515–529.

How to Cite
Nguyen, H. T., & Tang, C. W. (2022). Students’ Intention to Take E-Learning Courses During the COVID-19 Pandemic: A Protection Motivation Theory Perspective. The International Review of Research in Open and Distributed Learning, 23(3), 21-42.
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