https://orcid.org/0000-0002-7305-1015
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ABSTRACT
Physical closure of educational institutes worldwide due to the COVID-19 pandemic has resulted in the emergence of a new era of video-based learning. The current circumstances are unique that have forced students to use digital technologies for their learning purpose. However, the successful usage of such a system relies on the understanding of the adoption factors, for which an integrated model is proposed based on the Technology Acceptance Model and the Task Technology Fit Model. Additionally, this work considers the moderating effects of gender and digital inequality. Data are collected from 232 students all of whom have taken part in a full-semester video-based online learning course in times of the pandemic. A Partial Least Squares method is used for analyzing the data. Results show that video-based learning positively fits into the student’s perception and their actual use of the system. The effect of individual characteristics is more than the technology characteristics on the task-technology-fit chain. The integrated model can predict 64.6% of the variance in the final dependent variable: actual usage of video-based learning. Further, while the moderating effect of gender is found to be significant, that for digital inequality is non-significant. Finally, the theoretical and practical implications are discussed.
Disclosure of potential conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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Notes on contributors
Debajyoti Pal
Debajyoti Pal is a full-time researcher at King Mongkut’s University of Technology Thonburi, Bangkok, Thailand. He holds a PhD in Information Technology. His current research focuses on human computer interaction, technology acceptance, diffusion of innovative technologies and educational data mining.
Syamal Patra
Syamal Patra is a full-time assistant professor at Camellia Institute of Technology, Madhyamgram, Kolkata, India. His research interests include database management systems, healthcare management, structural equation modeling and optimization.