Log in

From Classroom to Screen: Analyzing the Mechanisms Sha** E-Learning Benefits Amidst COVID-19

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

The COVID-19 pandemic has reached global proportions, leading to significant shifts in societal norms, including the adoption of online education as a safety measure. This research delves into the factors that drive the advantages associated with e-learning, with a focus on the influences of attitudes towards social distancing, perceived value, and user contentment. Utilizing partial least squares-structural equation modeling (PLS-SEM), and a sample of 325 university students from two Asian countries actively participating in e-learning platforms, this research empirically validates the theoretical constructs. Findings demonstrate that perceived value and user contentment are potent drivers of e-learning advantages. Additionally, the roles of emotional and cognitive risk awareness in augmenting attitudes towards social distancing are highlighted. The influence of cabin fever syndrome on both types of risk perception is also significantly evident. The elements of system interactivity and response are found to positively influence perceived worth and user satisfaction. The paper culminates with insights into the theoretical significance and practical applications of the research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The data used in this study are available from the corresponding author upon reasonable request.

References

  • Abbad, M. M., Morris, D., & De Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. International Review of Research in Open and Distributed Learning, 10(2), 1–25.

    Article  Google Scholar 

  • Adedoyin, O. B. & Soykan, E. (2020). Covid-19 pandemic and online learning: the challenges and opportunities, Interactive Learning Environments, 1–13.

  • Adeoye, I., Adanikin, A., & Adanikin, A. (2020). COVID-19 and E-learning: Nigeria tertiary education system experience. International Journal of Research and Innovation in Applied Science, 5, 28–31.

    Google Scholar 

  • Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In Action control, 11–39 Springer.

  • Al Amin, M., Razib Alam, M., & Alam, M. Z. (2022). Antecedents of students’e-learning continuance intention during COVID-19: An empirical study. E-Learning and Digital Media, 20(3), 224–254. https://doi.org/10.1177/20427530221103915

    Article  Google Scholar 

  • Al-Fraihat, D., Joy, M., Masa’deh, R. E., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86.

    Article  Google Scholar 

  • Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher Education Studies, 10(3), 16–25.

    Article  Google Scholar 

  • 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), 216.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388–399.

    Article  Google Scholar 

  • Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438.

    Article  Google Scholar 

  • Azodo, C. C., & Ogbebor, O. G. (2019). Social distance towards halitosis sufferers. Swiss Dental Journal, 129(12), 1026–1030.

    Google Scholar 

  • Bae, S. Y., & Chang, P.-J. (2021). The effect of coronavirus disease-19 (COVID-19) risk perception on behavioural intention towards ‘untact’ tourism in South Korea during the first wave of the pandemic (March 2020). Current Issues in Tourism, 24(7), 1017–1035.

    Article  Google Scholar 

  • Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458.

    Article  Google Scholar 

  • Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545.

    Article  Google Scholar 

  • Brug, J., Aro, A. R., Oenema, A., De Zwart, O., Richardus, J. H., & Bishop, G. D. (2004). SARS risk perception, knowledge, precautions, and information sources, the Netherlands. Emerging Infectious Diseases, 10(8), 1486–1489.

    Article  Google Scholar 

  • Butler, G., & Mathews, A. (1987). Anticipatory anxiety and risk perception. Cognitive Therapy and Research, 11(5), 551–565.

    Article  Google Scholar 

  • Chakraborty, T., Kumar, A., Upadhyay, P., & Dwivedi, Y. K. (2020). Link between social distancing, cognitive dissonance, and social networking site usage intensity: A country-level study during the COVID-19 outbreak. Internet Research, 31(2), 419–456.

    Article  Google Scholar 

  • Chandra, Y. (2021). Online education during COVID-19: Perception of academic stress and emotional intelligence co** strategies among college students. Asian Education and Development Studies, 10(2), 229–238.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.

    Article  Google Scholar 

  • Chung, K., Oh, J., Kim, W., & Park, G. (2015). The effects of perceived value of mobile phones on user satisfaction, brand trust, and loyalty. Advanced Science and Technology Letters, 114, 10–14.

    Article  Google Scholar 

  • Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290.

    Article  Google Scholar 

  • DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information system: A ten-year update. Journal of Management Information Systems, 19(4), 3–30.

    Google Scholar 

  • Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22.

    Article  Google Scholar 

  • Ebner, M., Schön, S., Braun, C., Ebner, M., Grigoriadis, Y., Haas, M., Leitner, P., & Taraghi, B. (2020). COVID-19 epidemic as E-learning boost? Chronological development and effects at an Austrian university against the background of the concept of “E-learning readiness.” Future Internet, 12(6), 94.

    Article  Google Scholar 

  • Estacio, R. D., Lumibao, D. D., Reyes, E. A. S., & Avila, M. O. (2020). Gender difference in self-reported symptoms of cabin fever among Quezon City University students during the Covid 19 pandemic. International Journal of Scientific and Research Publications, 10(9), 848–860.

    Article  Google Scholar 

  • Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-learning during COVID-19 pandemic. Computer Networks, 176, 107290.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fritscher, L. (2020). How to know if you have cabin fever or fear of isolation, Available at: https://www.verywellmind.com/cabin-fever-fear-of-isolation-2671734. Accessed on 8 June 2021.

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

    Article  Google Scholar 

  • Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959–10966.

    Article  Google Scholar 

  • Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Van Pham, A. T., & 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.

    Article  Google Scholar 

  • Hoq, M. Z. (2020). E-learning during the period of pandemic (COVID-19) in the kingdom of Saudi Arabia: An empirical study. American Journal of Educational Research, 8(7), 457–464.

    Google Scholar 

  • Ibrahim, A. N. H., & Borhan, M. N. (2020). The interrelationship between perceived quality. Perceived Value and User Satisfaction towards Behavioral Intention in Public Transportation: A Review of the Evidence, International Journal on Advanced Science, Engineering and Information Technology, 10, 2048–2056.

    Google Scholar 

  • **, J. C., & Kim, D.-A. (2021). Higher education in South Korea: Recent changes in school competitiveness and research productivity. Journal of Asian Public Policy, 14(3), 291–313. https://doi.org/10.1080/17516234.2019.1622181

    Article  Google Scholar 

  • Jo, H. (2022a). Determinants of continuance intention towards e-learning during COVID-19: An extended expectation-confirmation model. Asia Pacific Journal of Education, 1–21. https://doi.org/10.1080/02188791.2022.2140645

  • Jo, H. (2022b). Effects of psychological discomfort on social networking site (SNS) usage intensity during COVID-19. Frontiers in Psychology, 13, 939726. https://doi.org/10.3389/fpsyg.2022.939726

    Article  Google Scholar 

  • Jo, H. (2022c). What drives university students to practice social distancing? Evidence from South Korea and Vietnam. Asia Pacific Viewpoint, 64(1), 47–59. https://doi.org/10.1111/apv.12351

    Article  Google Scholar 

  • Jo, H., & Baek, E.-M. (2023). Impacts of social isolation and risk perception on social networking intensity among university students during Covid-19. PLOS ONE, 18(4), e0283997. https://doi.org/10.1371/journal.pone.0283997

    Article  Google Scholar 

  • Jo, H., & Park, S. (2022). Success factors of untact lecture system in COVID-19: TAM, benefits, and privacy concerns. Technology Analysis & Strategic Management, 1–13. https://doi.org/10.1080/09537325.2022.2093709

  • Khan, M. A., Nabi, M. K., Khojah, M., & Tahir, M. (2020). Students’ perception towards E-learning during COVID-19 pandemic in India: An empirical study. Sustainability, 13(1), 57.

    Article  Google Scholar 

  • Kim, B. (2012). The diffusion of mobile data services and applications: Exploring the role of habit and its antecedents. Telecommunications Policy, 36(1), 69–81.

    Article  Google Scholar 

  • Kim, B., & Han, I. (2009). What drives the adoption of mobile data services? An Approach from a Value Perspective, Journal of Information Technology, 24(1), 35–45.

    Google Scholar 

  • Kim, B., & Kim, D. (2020). Exploring the key antecedents influencing consumer’s continuance intention toward bike-sharing services: Focus on China. International Journal of Environmental Research and Public Health, 17(12), 4556.

    Article  Google Scholar 

  • Kim, B., Kang, M., & Jo, H. (2014). Determinants of postadoption behaviors of mobile communications applications: A dual-model perspective. International Journal of Human-Computer Interaction, 30(7), 547–559.

    Article  Google Scholar 

  • Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126.

    Article  Google Scholar 

  • Lin, C.-L., **, Y. Q., Zhao, Q., Yu, S.-W., & Su, Y.-S. (2021). Factors influence students’ switching behavior to online learning under COVID-19 pandemic: A push–pull–mooring model perspective. The Asia-Pacific Education Researcher, 30(3), 229–245.

    Article  Google Scholar 

  • Ma, L., Zhang, X., & Wang, G. S. (2017). Identifying the reasons why users in China recommend bike apps. International Journal of Market Research, 59(6), 767–786.

    Article  Google Scholar 

  • Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alharbi, H. (2022). The COVID-19 pandemic and E-learning: Challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34(1), 21–38. https://doi.org/10.1007/s12528-021-09274-2

    Article  Google Scholar 

  • Martínez-Torres, M. R., Toral Marín, S., García, F. B., Vázquez, S. G., Oliva, M. A., & Torres, T. (2008). A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area. Behaviour & Information Technology, 27(6), 495–505.

    Article  Google Scholar 

  • Muzaffar, A. W., Tahir, M., Anwar, M. W., Chaudry, Q., Mir, S. R. & Rasheed, Y. (2021). A systematic review of online exams solutions in E-learning: techniques, tools, and global adoption, IEEE Access, 9, 32689–32712.

  • Nambiar, D. (2020). The impact of online learning during COVID-19: Students’ and teachers’ perspective. The International Journal of Indian Psychology, 8(2), 783–793.

    Google Scholar 

  • Nguyen, M.-N. (2022). Higher education in Vietnam - statistics & facts. statista. Retrieved May 10 from https://www.statista.com/topics/6227/higher-education-in-vietnam/

    Google Scholar 

  • Palloff, R. M., & Pratt, K. (1999). Building learning communities in cyberspace: Effective strategies for the online classroom. Jossey-Bass Publishers.

    Google Scholar 

  • Pituch, K. A., & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.

    Article  Google Scholar 

  • Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31(6), 797–805.

    Article  Google Scholar 

  • Redcross. (2020). What social distancing means. Available at https://www.redcross.org/about-us/news-and-events/news/. Accessed on 20 June 2021.

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2014). Smartpls 3. Hamburg: SmartPLS available at https://www.smartpls.com. Accessed on 4 August 2021.

  • Rosenblatt, P. C., Anderson, R. M., & Johnson, P. A. (1984). The meaning of “cabin fever.” The Journal of Social Psychology, 123(1), 43–53. https://doi.org/10.1080/00224545.1984.9924512

    Article  Google Scholar 

  • Saxena, C., Baber, H., & Kumar, P. (2021). Examining the moderating effect of perceived benefits of maintaining social distance on e-learning quality during COVID-19 pandemic. Journal of Educational Technology Systems, 49(4), 532–554.

    Article  Google Scholar 

  • Seitz, D. (2019). Yes, cabin fever is real—here’s how to prevent it. Don’t let winter isolation ruin your mood. Popular Science, Available at https://www.popsci.com/prevent-cabin-fever/. Accessed on 13 July 2021.

  • Seta, H. B., Wati, T., Muliawati, A., & Hidayanto, A. N. (2018). E-learning success model: An extention of DeLone & McLean IS’Success model. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 6(3), 281–291.

    Article  Google Scholar 

  • Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality & Quantity, 55(3), 805–826. https://doi.org/10.1007/s11135-020-01028-z

    Article  Google Scholar 

  • Simamora, R. M. (2020). The challenges of online learning during the COVID-19 pandemic: An essay analysis of performing arts education students. Studies in Learning and Teaching, 1(2), 86–103.

    Article  Google Scholar 

  • Sobaih, A. E. E., Hasanein, A., & Elshaer, I. A. (2022). Higher education in and after COVID-19: The impact of using social network applications for e-learning on students’ academic performance. Sustainability, 14(9), 5195.

    Article  Google Scholar 

  • Soni, V. D. (2020). Global impact of E-learning during COVID 19 (June 18 2020), Available at https://ssrn.com/abstract=3630073. Accessed on 4 August 2121 3630073.

  • Spreng, R. A., Mackenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15–32.

    Article  Google Scholar 

  • Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220.

    Article  Google Scholar 

  • Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243–257.

    Article  Google Scholar 

  • Trumbo, C. W., Peek, L., Meyer, M. A., Marlatt, H. L., Gruntfest, E., Mcnoldy, B. D., & Schubert, W. H. (2016). A cognitive-affective scale for hurricane risk perception. Risk Analysis, 36(12), 2233–2246.

    Article  Google Scholar 

  • World Health Organization. (2021). WHO Coronavirus Disease (COVID-19) dashboard. Available online: https://covid19.who.int/. Accessed on 22 June 2021.

  • Zayabalaradjane, Z. (2020). COVID-19: Strategies for online engagement of remote learners. Online Submission, 9(246), 1–11.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyeon Jo.

Ethics declarations

Informed Consent

The author has the informed consent of the students participating in the survey.

Competing Interest

The author declares no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 1 Constructs and items

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jo, H. From Classroom to Screen: Analyzing the Mechanisms Sha** E-Learning Benefits Amidst COVID-19. J Knowl Econ (2023). https://doi.org/10.1007/s13132-023-01614-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13132-023-01614-0

Keywords

Navigation