Technology Enabling the New Normal: How Students Respond to Classes

Authors

  • 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

DOI:

https://doi.org/10.19173/irrodl.v23i4.6295

Keywords:

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

Abstract

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.

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Published

2022-11-01

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. https://doi.org/10.19173/irrodl.v23i4.6295

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