Students’ Intention to Take E-Learning Courses During the COVID-19 Pandemic: A Protection Motivation Theory Perspective
DOI:
https://doi.org/10.19173/irrodl.v23i3.6178Keywords:
e-learning, COVID-19, protection motivation theory (PMT), technology acceptance model (TAM), Vietnam, TaiwanAbstract
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.
References
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. https://doi.org/10.5815/ijitcs.2018.04.01
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
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. https://doi.org/10.3390/educsci10090216
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. https://doi.org/10.1177/0735633118817879
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. https://doi.org/10.1080/19371918.2020.1806171
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. https://doi.org/10.1016/j.compedu.2013.07.035
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. https://doi.org/10.2307/25750694
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. https://doi.org/10.1016/j.chb.2019.07.024
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. https://doi.org/10.1016/j.jhin.2020.04.035
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. https://doi.org/10.1348/135910710X485826
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. https://doi.org/10.1016/j.janxdis.2020.102307
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. https://www.jstor.org/stable/jeductechsoci.12.1.5
Cheng, Y. M. (2011). Antecedents and consequences of e‐learning acceptance. Information Systems Journal, 21(3), 269–299. https://doi.org/10.1111/j.1365-2575.2010.00356.x
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. https://doi.org/10.1109/HICSS.2009.74
Conner, M., & Norman, P. (2015). Predicting and changing health behaviour: Research and practice with social cognition models (3rd ed.). McGraw-Hill Education. https://www.mheducation.co.uk/predicting-and-changing-health-behaviour-research-and-practice-with-social-cognition-models-9780335263783-emea-group
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]. http://hdl.handle.net/1721.1/15192
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. https://doi.org/10.1287/mnsc.35.8.982
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. https://doi.org/10.1016/S0378-7206(98)00101-3
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. https://doi.org/10.1016/j.comnet.2020.107290
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130–132. https://philpapers.org/rec/FISBAI
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
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). https://www.learntechlib.org/p/188116/
Gohiya, P., & Gohiya, A. (2020, May 21). E-learning during COVID 19 pandemic. Research Square. https://doi.org/10.21203/rs.3.rs-29575/v1
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. https://doi.org/10.19173/irrodl.v22i2.5358
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. https://doi.org/10.19173/irrodl.v22i2.5154
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. https://doi.org/10.4018/IJICTE.2018010104
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. https://doi.org/10.1080/10580530.2015.1117842
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. https://doi.org/10.1108/ITSE-08-2020-0164
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. https://doi.org/10.1016/j.cose.2011.10.007
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. https://doi.org/10.1007/978-3-540-24748-7_24
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. https://doi.org/10.1016/j.techsoc.2020.101400
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. https://doi.org/10.1016/j.compedu.2009.06.014
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. https://doi.org/10.1057/ejis.2009.11
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. https://doi.org/10.1186/s12889-020-09695-1
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. https://doi.org/https://doi.org/10.1111/inm.12921
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. https://doi.org/10.1016/j.compedu.2008.11.002
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. https://doi.org/10.19173/irrodl.v22i2.5187
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. https://doi.org/10.1080/15536548.2013.10845672
Ministry of Health. (2021a). COVID-19 update as of 12pm on May 24, 2021. Retrieved May 24, 2021, from https://vncdc.gov.vn/ban-tin-cap-nhat-covid-19-tinh-den-12h00-ngay-2452021-nd16074.html
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. https://ncov.moh.gov.vn/web/guest/-/6847912-293
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/https://doi.org/10.1016/j.chb.2014.07.044
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. https://doi.org/10.1016/j.compedu.2004.11.007
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. https://doi.org/10.3390/su13095218
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. https://doi.org/10.1080/07294360.2020.1823945
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. https://doi.org/10.1016/j.ijid.2020.07.074
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. http://sersc.org/journals/index.php/IJCA/article/view/26035
Rana, H., & Lal, M. (2014). E-learning: Issues and challenges. International Journal of Computer Applications, 97(5), 20–24. https://doi.org/10.5120/17004-7154
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. https://doi.org/10.1016/j.jdmm.2021.100564
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114. https://doi.org/10.1080/00223980.1975.9915803
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. https://doi.org/10.1109/ICISS49785.2020.9316046
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. https://doi.org/10.4103/2277-9531.127556
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. https://doi.org/10.34172/ijhpm.2020.85
Singh, S., Orwat, J., & Grossman, S. (2011). A protection motivation theory application to date rape education. Psychology, Mealth & Medicine, 16(6), 727–735. https://doi.org/10.1080/13548506.2011.579983
Taiwan Centers for Disease Control. (2021). Confirmed COVID-19 cases on 27 of June. CDC. Retrieved June 30, 2021, from, https://www.cdc.gov.tw/En/Bulletin/Detail/BfYetzBF23lIlj7dGFgnTw?typeid=158
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. https://doi.org/10.1108/JIEB-09-2016-0032
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. https://doi.org/10.1186/1471-2458-11-575
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. https://doi.org/10.1016/j.annals.2019.102743
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. https://doi.org/10.1038/s41562-020-0887-9
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. https://doi.org/10.3233/THC-140811
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.