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Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
by
Mailizar, Mailizar
, Burg, Damon
, Maulina, Suci
in
College Students
/ Coronaviruses
/ COVID-19
/ Data Analysis
/ Intention
/ Online instruction
/ Pandemics
/ Technology Acceptance Model
/ University students
2021
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Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
by
Mailizar, Mailizar
, Burg, Damon
, Maulina, Suci
in
College Students
/ Coronaviruses
/ COVID-19
/ Data Analysis
/ Intention
/ Online instruction
/ Pandemics
/ Technology Acceptance Model
/ University students
2021
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Do you wish to request the book?
Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
by
Mailizar, Mailizar
, Burg, Damon
, Maulina, Suci
in
College Students
/ Coronaviruses
/ COVID-19
/ Data Analysis
/ Intention
/ Online instruction
/ Pandemics
/ Technology Acceptance Model
/ University students
2021
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Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
Journal Article
Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model
2021
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Overview
This present study aims to investigate factors that impact behavioural intention of university students on e-learning use during the COVID-19 pandemic. An online questionnaire was utilised to gather data from 109 students enrolled in one of the universities in Indonesia. The Technology Acceptance Model (TAM) was the primary framework employed for analysis, in which system quality and e-learning experience were included as external constructs to seek out a much better model to improve the understanding of students’ intention to adopt e-learning. An extended TAM model was developed and tested in this study. The model consists of six constructs: system quality, e-learning experience, perceived ease of use, perceived usefulness, attitude toward use, and behavioural intention. Structural Equation Modelling (SEM) and SMART PLS 3.0 software were applied for data analysis. The findings informed that the proposed model has been successfully explained factors university students use of e-learning during the pandemic in Indonesia. It suggested that attitude toward e-learning use was the most prominent construct to predict university students’ behavioural intention to use e-learning during the pandemic. Finally, this study offers recommendations for future research and practices.
Publisher
Springer Nature B.V
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