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Usability factors predicting continuance of intention to use cloud e-learning application

In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable,...

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Autores principales: Wang, Lillian-Yee-Kiaw, Lew, Sook-Ling, Lau, Siong-Hoe, Leow, Meng-Chew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556850/
https://www.ncbi.nlm.nih.gov/pubmed/31198866
http://dx.doi.org/10.1016/j.heliyon.2019.e01788
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author Wang, Lillian-Yee-Kiaw
Lew, Sook-Ling
Lau, Siong-Hoe
Leow, Meng-Chew
author_facet Wang, Lillian-Yee-Kiaw
Lew, Sook-Ling
Lau, Siong-Hoe
Leow, Meng-Chew
author_sort Wang, Lillian-Yee-Kiaw
collection PubMed
description In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable, and scalable learning methodology. Despite plentiful Cloud e-learning frameworks have been proposed across literature, limited researches have been conducted to study the usability factors predicting continuance intention to use Cloud e-learning applications. In this study, five usability factors namely Computer Self Efficacy (CSE), Enjoyment (E), Perceived Ease of Use (PEU), Perceived Usefulness (PU), and User Perception (UP) have been identified for factor analysis. All the five independent variables were hypothesized to be positively associated to a dependent variable namely Continuance Intention (CI). A survey was conducted on 170 IT students in one of the private universities in Malaysia. The students were given one trimester to experience the usability of Cloud e-Learning application. As an instrument to analyse the usability factors towards continuance intention of the application, a questionnaire consisting thirty questions was formulated and used. The collected data were analysed using SMARTPLS 3.0. The results obtained from this study observed that computer self-efficacy and enjoyment as intrinsic motivations significantly predict continuance intention, while perceived ease of use, perceived usefulness and user perception were insignificant. This outcome implies that computer self-efficacy and enjoyment significantly affect the willingness of students to continue using Cloud e-learning application in their studies. The discussions and implications of this study are vital for researchers and practitioners of educational technologies in higher education.
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spelling pubmed-65568502019-06-13 Usability factors predicting continuance of intention to use cloud e-learning application Wang, Lillian-Yee-Kiaw Lew, Sook-Ling Lau, Siong-Hoe Leow, Meng-Chew Heliyon Article In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable, and scalable learning methodology. Despite plentiful Cloud e-learning frameworks have been proposed across literature, limited researches have been conducted to study the usability factors predicting continuance intention to use Cloud e-learning applications. In this study, five usability factors namely Computer Self Efficacy (CSE), Enjoyment (E), Perceived Ease of Use (PEU), Perceived Usefulness (PU), and User Perception (UP) have been identified for factor analysis. All the five independent variables were hypothesized to be positively associated to a dependent variable namely Continuance Intention (CI). A survey was conducted on 170 IT students in one of the private universities in Malaysia. The students were given one trimester to experience the usability of Cloud e-Learning application. As an instrument to analyse the usability factors towards continuance intention of the application, a questionnaire consisting thirty questions was formulated and used. The collected data were analysed using SMARTPLS 3.0. The results obtained from this study observed that computer self-efficacy and enjoyment as intrinsic motivations significantly predict continuance intention, while perceived ease of use, perceived usefulness and user perception were insignificant. This outcome implies that computer self-efficacy and enjoyment significantly affect the willingness of students to continue using Cloud e-learning application in their studies. The discussions and implications of this study are vital for researchers and practitioners of educational technologies in higher education. Elsevier 2019-06-07 /pmc/articles/PMC6556850/ /pubmed/31198866 http://dx.doi.org/10.1016/j.heliyon.2019.e01788 Text en © 2019 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Wang, Lillian-Yee-Kiaw
Lew, Sook-Ling
Lau, Siong-Hoe
Leow, Meng-Chew
Usability factors predicting continuance of intention to use cloud e-learning application
title Usability factors predicting continuance of intention to use cloud e-learning application
title_full Usability factors predicting continuance of intention to use cloud e-learning application
title_fullStr Usability factors predicting continuance of intention to use cloud e-learning application
title_full_unstemmed Usability factors predicting continuance of intention to use cloud e-learning application
title_short Usability factors predicting continuance of intention to use cloud e-learning application
title_sort usability factors predicting continuance of intention to use cloud e-learning application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556850/
https://www.ncbi.nlm.nih.gov/pubmed/31198866
http://dx.doi.org/10.1016/j.heliyon.2019.e01788
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