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The Covid-19 Pandemic and the Acceptance of E-Learning among University Students

The Role of Precipitating Events

Bibliographic Data

ID22045337
AuthorsP R Weerathunga (0000-0001-6417-442X, Rajarata University of Sri Lanka, corresponding author), W H M S Samarathunga (0000-0001-9533-3884, Rajarata University of Sri Lanka, corresponding author), H N Rathnayake (Rajarata University of Sri Lanka), Suneth Agampodi (0000-0001-7810-1774, Rajarata University of Sri Lanka), S B Agampodi (Rajarata University of Sri Lanka), Mohammad Nurunnabi (0000-0003-0848-3556, Prince Sultan University), M M S C Madhunimasha (Rajarata University of Sri Lanka)
Year2021
Volume11
Issue8
Pages436
Publication date2021-08-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci11080436
OpenAlexW3194063435
LanguageEN
Citations received4
References cited56

This study examined the effect of the COVID- 19 pandemic and related events on the use of e-learning, as well as other key determinants of it. The data were collected from 1039 university students in Sri Lanka. To examine the influence of the COVID-19 pandemic, which was viewed through the lens of precipitating events, on the intention–behaviour relationship, we employed the Technology Acceptance Model (TAM) with the inclusion of a moderating variable. While the findings indicated that the COVID-19 pandemic had clearly increased the usage of e-learning, we found no evidence to establish a moderating impact on the intention–behaviour relationship. The empirical model, however, was well fitted to the data, and the key components of the TAM were likewise adequately described by the relevant predictors. Furthermore, attitudes toward e-learning and perceived ease of use emerged as the most important factors in explaining behavioural intention, whereas relevance and experience were shown to be more significant in relation to perceived usefulness and perceived ease of use. Our work is significant because it adds to the existing empirical evidence on e-learning and supports the relevance of TAM in understanding the usage of e-learning even in extreme situations such as the COVID-19 pandemic. Our research has significant implications for educators and other higher education authorities

Empirical evidence · Empirical research · Higher education · Moderation · Pandemic · Political science · Structural equation modeling · Technology Acceptance Model · Usability · Computer Science · Digital Marketing and Social Media · Medicine · Organizational and Employee Performance · Psychology · Social Psychology · Technology Adoption and User Behaviour

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  • Influence of confidence factors on e-learning acceptance for future use by university students

    Open Access•Subrata K Roy, Saiful Islam•SN Social Sciences•2023

  • Measuring Distance Learning System Adoption in a Greek University during the Pandemic Using the UTAUT Model, Trust in Government, Perceived University Efficiency and Coronavirus Fear

    Open Access•Konstantinos Antoniadis, Kostas Zafiropoulos et al.•Education Sciences•2022

  • Developing a General Extended Technology Acceptance Model for E-Learning (Getamel) by analysing commonly used external factors

    Open Access•Fazil Abdullah, Rupert Ward•Computers in Human Behavior•2016

  • Convergence of Structural Equation Modeling and Multilevel Modeling

    Rex B Kline•The Sage Handbook of Innovation…•2014

  • PLS path modeling

    Open Access•Michel Tenenhaus, Vincenzo Esposito Vinzi et al.•Computational Statistics & Data…•2005

  • Examining the students’ behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning approach

    Open Access•Ching-Ter Chang, Ching‐Ter Chang et al.•Computers & Education•2017

  • Web-Based Virtual Learning Environments

    Gabriele Piccoli, Rami Ahmad et al.•MIS Quarterly•2001

  • University students' behavioral intention to use mobile learning

    Open Access•Sung Youl Park, Min‐Woo Nam et al.•British Journal of Educational…•2012

  • Factors Determining the Behavioral Intention to Use Mobile Learning

    Open Access•Cheng-Min Chao•Frontiers in Psychology•2019

  • A meta-analysis of e-learning technology acceptance

    Open Access•Boštjan Šumak, Marjan Heričko et al.•Computers in Human Behavior•2011

  • Understanding Information Technology Usage

    Shirley Taylor, Peter A Todd et al.•Information Systems Research•1995

  • Assessing measurement model quality in PLS-SEM using confirmatory composite analysis

    Open Access•Joe F Hair, M C Howard et al.•Journal of Business Research•2020

  • PLS-Sem

    Joe F Hair, Christian M Ringle et al.•Journal of Marketing Theory and…•2011

  • The theory of planned behavior

    Open Access•Icek Ajzen•Organizational Behavior and Human…•1991

  • User Acceptance of Information Technology

    Viswanath Venkatesh, Venkatesh Venkatesh et al.•MIS Quarterly•2003

  • Consumer Acceptance and Use of Information Technology

    Viswanath Venkatesh, Venkatesh Venkatesh et al.•MIS Quarterly•2012

  • When to use and how to report the results of PLS-SEM

    Open Access•Joseph F Hair, Jeffrey J Risher et al.•European Business Review•2019

  • A Theoretical Extension of the Technology Acceptance Model

    Open Access•Vivek Venkatesh, Viswanath Venkatesh et al.•Management Science•2000

  • Technology Acceptance Model 3 and a Research Agenda on Interventions

    Open Access•Vivek Venkatesh, Viswanath Venkatesh et al.•Decision Sciences•2008

  • Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

    Fred D Davis•MIS Quarterly•1989

  • Before and during Covid-19

    Open Access•Maria-Dorinela Dascalu, Stefan Ruseti et al.•Computers in Human Behavior•2021

  • Covid ‐19 and online teaching in higher education

    Open Access•Bao Wei•Human Behavior and Emerging…•2020

  • The Sage Handbook of Innovation in Social Research Methods

    W Paul Vogt, Malcolm Williams et al.•The Sage Handbook of Innovation…•2011

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    Open Access•Nicole Richter, Nicole Franziska Richter et al.•Industrial Management & Data…•2020

  • The Consequences of Unintended Pregnancy for Maternal and Child Health in Rural India

    Open Access•Abhishek Singh, Ashish Singh et al.•Maternal and Child Health Journal•2012

  • Self-efficacy mechanism in human agency

    Albert Bandura•American Psychologist•1982

  • Forming attitudes that predict future behavior

    Laura R Glasman, Dolores Albarracin•Psychological Bulletin•2006

Unique citing works4
Citations per year1
Citation span2022 - 2023 (2)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 4

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