Technology Enabling the New Normal: How Students Respond to Classes


  • Muhammad Shariat Ullah Department of Organization Strategy & Leadership, Faculty of Business Studies, University of Dhaka
  • Md. Shariful Alam Khandakar Department of Tourism and Hospitality Management, Faculty of Business Studies, University of Dhaka
  • Muhammad Abdul Aziz Department of Organization Strategy & Leadership, Faculty of Business Studies, University of Dhaka
  • Daisy Mui Hung Kee School of Management, Universiti Sains Malaysia



COVID-19, online class intention, technology acceptance model, theory of planned behavior, Bangladesh


This cross-sectional study investigates the online education intention of undergraduate students in the largest and oldest public university in Bangladesh during the COVID-19 pandemic. Under convenient sampling, 843 undergraduate students with rural and urban backgrounds participated in an online self-administered questionnaire. Partial least squares structural equation modelling (PLS-SEM) was employed to examine the hypothesized relationships. We found that students’ online class intention is significantly influenced by their attitude towards online classes (AOC), perceived usefulness (PU), and facilitating conditions (FC). We further identified that external antecedents have significant indirect effects on the outcome variables. Our findings provide new insights and contribute to a learners’ community on online classes during the COVID-19 pandemic. This study extends the technology acceptance model (TAM) and the theory of planned behavior (TPB) to depict the factors influencing undergraduate students’ intention to attend online classes (IOC) during the COVID-19 pandemic.


Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63(10), 75–90.

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

Al-Amin, M., Zubayer, A. Al, Deb, B., & Hasan, M. (2021). Status of tertiary level online class in Bangladesh: Students’ response on preparedness, participation and classroom activities. Heliyon, 7(1), Article e05943.

Aldás-Manzano, J., Lassala-Navarré, C., Ruiz-Mafé, C., & Sanz-Blas, S. (2009). Key drivers of internet banking services use. Online Information Review, 33(4), 672–695.

Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102(1), 67–86.

Anwar, S., Nasrullah, M., & Hosen, M. J. (2020). COVID-19 and Bangladesh: Challenges and how to address them. Frontiers in Public Health, 8(154).

Bauer, H. H., Reichardt, T., Barnes, S. J., & Neumann, M. M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181–192.

Binyamin, S. S., Rutter, M., & Smith, S. (2019). Extending the technology acceptance model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning, 14(3), 4–21.

Bodrud-Doza, M., Shammi, M., Bahlman, L., Islam, A. R. M. T., & Rahman, M. M. (2020). Psychosocial and socio-economic crisis in Bangladesh due to COVID-19 pandemic: A perception-based assessment. Frontiers in Public Health, 8(341).

Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and application (pp. 645–689). Springer.

Chowhan, S., & Ghosh, S. R. (2020). Role of ICT on agriculture and its future scope in Bangladesh. Journal of Scientific Research and Reports, 26(5), 20–35.

Chuo, Y.-H., Tsai, C.-H., Lan, Y.-L., & Tsai, C.-S. (2011). The effect of organizational support, self-efficacy, and computer anxiety on the usage intention of e-learning system in hospital. African Journal of Business Management, 5(14), 5518–5523.

Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52(13), 22–28.

Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Routledge.

Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 1–20.

Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1–2), 91–96.

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

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.

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

Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.

Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

Hajiyev, J. (2018). Assessing students’ attitude and intention to use m-learning in higher education. Journal of Contemporary Educational Research, 2(2), 17–25.

Hao, Y.-W. (2004). Students’ attitudes toward interaction in online learning: Exploring the relationship between attitudes, learning styles, and course satisfaction [Unpublished doctoral dissertation]. The University of Texas at Austin.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Jan, A. U., & Contreras, V. (2016). Success model for knowledge management systems used by doctoral researchers. Computers in Human Behavior, 59(7), 258–264.

Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological Bulletin, 77(5), 361–372.

Khan, A. H., Sultana, M. S., Hossain, S., Hasan, M. T., Ahmed, H. U., & Sikder, M. T. (2020). The impact of COVID-19 pandemic on mental health & wellbeing among home-quarantined Bangladeshi students: A cross-sectional pilot study. Journal of Affective Disorders, 277(18), 121–128.

Lee, J., Song, H.-D., & Hong, A. J. (2019). Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability, 11(4), Article 985.

Lin, T. T. C., & Bautista, J. R. (2017). Understanding the relationships between mHealth apps’ characteristics, trialability, and mHealth literacy. Journal of Health Communication, 22(4), 346–354.

Liñán, F., & Chen, Y.-W. (2009). Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice, 33(3), 593–617.

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.

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26, 7057–7077.

Mannan, A. (2017, February 25). Achieving our higher education targets. The Daily Star.

Murphy, M. P. A. (2020). COVID-19 and emergency eLearning: Consequences of the securitization of higher education for post-pandemic pedagogy. Contemporary Security Policy, 41(3), 492–505.

Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109(6), 56–73.

Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), Article 6468.

Saadé, R. G., & AlSharhan, J. (2015). Discovering the motivations of students when using an online learning tool. Journal of Information Technology Education: Research, 14(1), 283–296.

Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640.

Sarker, M. F. H., Mahmud, R. A., Islam, M. S., & Islam, M. K. (2019). Use of e-learning at higher educational institutions in Bangladesh: Opportunities and challenges. Journal of Applied Research in Higher Education, 11(2), 210–223.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. Handbook of Market Research, 26(1), 1–40.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103.

Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3), S72–S89.

Sintema, E. J. (2020). Effect of COVID-19 on the performance of grade 12 students: Implications for STEM education. Eurasia Journal of Mathematics, Science and Technology Education, 16(7), Article em1851.

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), Article e05410.

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312.

Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253–262.

Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3–18.

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67(3), 749–766.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143.

Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), social motivation, and task technology fit (TTF). IEEE Access, 8, 21473–21484.

Venkatesh, A., & Edirappuli, S. (2020). Social distancing in Covid-19: What are the mental health implications? BMJ, 369, Article m1379.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

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.

Wong, K.-T., Russo, S., & McDowall, J. (2013). Understanding early childhood student teachers’ acceptance and use of interactive whiteboard. Campus-Wide Information Systems, 30(1), 4–16.

Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142.

Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393–426.

Zia, A. (2020). Exploring factors influencing online classes due to social distancing in COVID-19 pandemic: A business students perspective. The International Journal of Information and Learning Technology, 37(4), 197–211.



How to Cite

Shariat Ullah, M., Md. Shariful Alam Khandakar, Muhammad Abdul Aziz, & Daisy Mui Hung Kee. (2022). Technology Enabling the New Normal: How Students Respond to Classes. The International Review of Research in Open and Distributed Learning, 23(4), 35–56.



Research Articles