Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2023 | 14 | 1 | 650-671

Article title

Learning Continuity during COVID-19: An Analysis of the Higher Education Sector of Bangladesh

Content

Title variants

Languages of publication

Abstracts

EN
Aim. This study aims to understand the factors determining university students’ behavioural intentions toward online learning in Bangladesh. Specifically, this study investigates the relationship between performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioural intention (BI). Moreover, this study explores the influence of pandemic fear (PF) as a moderator in the relationship between exogenous and endogenous factors. Methods. The study is cross-sectional and followed a quantitative research approach with purposive sampling. Data were collected at a single point using a sample size of 578 respondents who studied online during the various phases of lockdown at five public and five private universities in Bangladesh. Regarding multivariate analysis, the Partial Least Squares - Structural Equation Modeling (PLS-SEM) is applied in this study to test the causal relationships in the structural model, as it is considered a second-generation technique. Results. Statistically, a positive significance was found between PE, EE, SI, and BI in online learning participation. Whereas the FC and the BI exhibited a negative relationship, a positive relationship was found between PE, EE, and the SI on BI. In addition, a moderating role for PF was investigated, and EE and FC were found to influence BI significantly. Conclusion. This study presents an extended UTAUT model by integrating pandemic fear as the moderator to study students' behavioural intention to adopt an online learning system under a disruptive situation. Practitioners, especially academicians and policymakers, will find this model useful while developing andragogic interventions for the higher education sector in Bangladesh.  

Year

Volume

14

Issue

1

Pages

650-671

Physical description

Dates

published
2023

Contributors

  • Jamia Millia Islamia University, Department of Commerce and Business Studies, Jamia Nagar, New Delhi-110025, India
  • Department of Business Management, Tripura University, Suryamaninagar, Tripura 799022, India

References

  • Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205-7224.
  • Ahmed, O., Griffiths, M. D., & Hossain, M. A. (2022). Psychometric Assessment of the 18-Item Bangla Mental Health Inventory (Bangla MHI-18). Psychological Studies, 67(1), 110-122.
  • Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The fear of COVID-19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 20, 1537–1545. https://doi.org/10.1007/s11469-020-00270-8
  • Ajzen, I. (1985). ‘From intentions to actions: A theory of planned behavior’. In J. Kuhl & J. Beckmann. Action Control: From Cognition to Behavior, (pp. 11-39). Springer Berlin Heidelberg.
  • 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.
  • Almisad, B., & Alsalim, M. (2020). Kuwaiti female university students' acceptance of the integration of smartphones in their learning: An investigation guided by a modified version of the unified theory of acceptance and use of technology (UTAUT). International Journal of Technology Enhanced Learning, 12(1), 1-19.
  • Altameemi, A. F., & Al-Slehat, Z. A. F. (2021). Exploring the students’ behavior intentions to adopt e-learning technology: A survey study based on COVID-19 crisis. International Journal of Business and Management, 16(6), 31-41.
  • Al-Hamad, M., Mbaidin, H., AlHamad, A., Alshurideh, M., Kurdi, B., & Al-Hamad, N. (2021). Investigating students' behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data and Network Science, 5(3), 321-330.
  • Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2020.1830121
  • Amin, K., & Zaman, M. (2021). Assessing the adoption behavior of e-learning in a developing country in South East Asia: Predicting an alternative path to behavioral intention to use. International Journal of Education and Development using Information and Communication Technology, 17(3), 38-56.
  • Azam, M. S., Morsalin, Md., Rakib, Md. R. H. K., & Pramanik, S. A. K. (2021). Adoption of electronic commerce by individuals in Bangladesh. Information Development, 0(0). https://doi.org/10.1177/02666669211052523
  • Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2020). Exploring factors influencing US millennial consumers’ use of tap-and-go payment technology. The International Review of Retail, Distribution and Consumer Research, 30(2), 143–163.
  • Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180. https://doi.org/10.1016/J.TECHFORE.2022.121692
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. NJ: Prentice-Hall.
  • Chen, P. Y., & Hwang, G. J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79-95.
  • Davis, S. F., Grover, C. A., Becker, A. H., & McGregor, L. N. (1992). Academic dishonesty: Prevalence, determinants, techniques, and punishments. Teaching of Psychology, 19(1), 16–20.
  • Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22.
  • Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130-132.
  • Gasparro, R., Scandurra, C., Maldonato, N. M., Dolce, P., Bochicchio, V., Valletta, A. & Marenzi, G. (2020). Perceived job insecurity and depressive symptoms among Italian dentists: The moderating role of fear of COVID-19. International journal of environmental research and public health, 17(15), 5338.
  • Guppy, N., Verpoorten, D., Boud, D., Lin, L., Tai, J., & Bartolic, S. (2022). The post‐COVID‐19 future of digital learning in higher education: Views from educators, students, and other professionals in six countries. British Journal of Educational Technology, 53(6), 1750-1765.
  • Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). i. Springer Nature.
  • 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.
  • Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Sage publications.
  • Hasan, K. K., Mukherjee, D., & Saha, M. (2021). Learning continuity during COVID-19 pandemic using the virtual xlassroom–A Ccross-border experimental Multi Case Approach. Journal of Education Culture and Society, 12(1), 335-345.
  • Islam, M. A., Barna, S. D., Raihan, H., Khan, M. N. A., & Hossain, M. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey. PloS one, 15(8), e0238162.
  • Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
  • Koçak, O., Koçak, Ö. E., & Younis, M. Z. (2021). The psychological consequences of COVID-19 fear and the moderator effects of individuals’ underlying illness and witnessing infected friends and family. International journal of environmental research and public health, 18(4), 1836.
  • Lai, H. J. (2020). Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology. Interactive Learning Environments, 28(7), 890–901.
  • Lin, C. Y., Hou, W. L., Mamun, M. A., Aparecido da Silva, J., Broche‐Pérez, Y., Ullah, I., & Pakpour, A. H. (2021). Fear of COVID‐19 Scale (FCV‐19S) across countries: Measurement invariance issues. Nursing open, 8(4), 1892-1908.
  • Lolic, T., Dionisio, R., Ciric, D., Ristic, S., & Stefanovic, D. (2020). Factors influencing students usage of an e-learning system: Evidence from IT students. In International Joint Conference on Industrial Engineering and Operations Management (pp. 205-215). Springer, Cham.
  • Mahmood, Q. K., Jafree, S. R., Mukhtar, S., & Fischer, F. (2021). Social Media Use, Self-Efficacy, Perceived Threat, and Preventive Behavior in Times of COVID-19: Results of a Cross-Sectional Study in Pakistan. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.562042
  • Memon, M. A., Ramayah, T., Cheah, J. H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: a review. Journal of Applied Structural Equation Modeling, 5(1), 1-14.
  • 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, 102258. https://doi.org/10.1016/J.JANXDIS.2020.102258
  • Md Yunus, M., Ang, W. S., & Hashim, H. (2021). Factors affecting teaching English as a Second Language (TESL) postgraduate students’ behavioural intention for online learning during the COVID-19 pandemic. Sustainability, 13(6), 3524.
  • Miranda, J., Navarrete, C., Noguez, J., Molina-Espinosa, J. M., Ramírez-Montoya, M. S., Navarro-Tuch, S. A., Bustamante-Bello, M. R., Rosas-Fernández, J. B., & Molina, A. (2021). The core components of education 4.0 in higher education: Three case studies in engineering education. Computers & Electrical Engineering, 93, 107278. https://doi.org/10.1016/J.COMPELECENG.2021.107278
  • Mukherjee, D., & Hasan, K. K. (2020). Challenges in Learning Continuity during the COVID-19 Pandemic: A Methodological and Thematic Review. South Asian Journal of Management, 27(3), 56-78.
  • Naveed, Q. N., Alam, M. M., Qahmash, A. I., & Quadri, K. M. (2021). Exploring the determinants of service quality of cloud e-learning system for active system usage. Applied Sciences, 11(9), 4176.
  • Persada, S. F., Miraja, B. A., & Nadlifatin, R. (2019). Understanding the Generation Z Behavior on D-Learning: A Unified Theory of Acceptance and Use of Technology (UTAUT) Approach. International Journal of Emerging Technologies in Learning, 14(5), 20–33.
  • Qiao, P., Zhu, X., Guo, Y., Sun, Y., & Qin, C. (2021). The Development and Adoption of Online Learning in Pre-and Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of Technology. Journal of Risk and Financial Management, 14(4), 162.
  • Rahman, M. H. A., Uddin, M. S., & Dey, A. (2021). Investigating the mediating role of online learning motivation in the COVID‐19 pandemic situation in Bangladesh. Journal of computer assisted learning, 37(6), 1513-1527.
  • Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2020). Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity. Postdigital Science and Education, 2(3), 923–945. https://doi.org/10.1007/s42438-020-00155-y
  • Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183-208.
  • Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637-2650.
  • Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press.
  • Rouf, Md. A., Hossain, M. S., Habibullah, Md., & Ahmed, T. (2022). Online classes for higher education in Bangladesh during the COVID-19 pandemic: a perception-based study. PSU Research Review. Advance online publication. https://doi.org/10.1108/prr-05-2021-0026
  • Sangeeta & Tandon, U. (2020). Factors influencing adoption of online teaching by school teachers: A study during COVID‐19 pandemic. Journal of Public Affairs, 21(4), e2503.
  • Schreiber, B., Luescher, T. M., Perozzii, B., & Moscaritolo, L. B. (2021). Student Affairs and Services during Covid-19 in Africa: Mitigating the Pandemic’s Impact on Student Success. Journal of Student Affairs in Africa, 9(1), 1-21.
  • Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). 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.
  • Teo, T., & Huang, F. (2019). Investigating the influence of individually espoused cultural values on teachers’ intentions to use educational technologies in Chinese universities. Interactive Learning Environments, 27(5-6), 813–829.
  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176
  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilisation. MIS Quarterly, 15(1), 125–143.
  • Uddin U. (2020). Effects of the pandemic on the education sector in Bangladesh. The Financial Express. https://thefinancialexpress.com.bd/views/effects-of-the-pandemic-on-the-education-sector-in-bangladesh-1592061447
  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
  • Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The lancet, 395(10223), 470-473.
  • Williams, M. L., Saunderson, I. P., & Dhoest, A. (2021). Students’ Perceptions of the Adoption and Use of Social Media in Academic Libraries: A UTAUT Study. Communicatio, 47(1), 76–94.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
18662501

YADDA identifier

bwmeta1.element.ojs-doi-10_15503_jecs2023_1_650_671
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.