A conceptual model of e-learning based on cloud computing adoption in higher education institutions

2017 ◽  
Author(s):  
Qasim Al Ajmi ◽  
Ruzaini Abdullah Arshah ◽  
Adzhar Kamaludin ◽  
Ali Safa Sadiq ◽  
Mohammed A. Al-Sharafi
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Conceptual Model ◽  
Higher Education Institutions ◽  
Cloud Computing Adoption ◽  
E Learning

scholarly journals Continuance Use of Cloud Computing in Higher Education Institutions: A Conceptual Model

2020 ◽  
Vol 10 (19) ◽  
pp. 6628
Author(s):  
Yousef A. M. Qasem ◽  
Rusli Abdullah ◽  
Yusmadi Yaha ◽  
Rodziah Atana
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Conceptual Model ◽  
Service Providers ◽  
Higher Education Institutions ◽  
Information And Communications Technology ◽  
Organizational Level ◽  
Innovative Technology ◽  
Organizational Setting ◽  
Adoption Of Technology

Resource optimization is a key concern for Higher Education Institutions (HEIs). Cloud Computing, as the recent generation in computing technology of the fourth industrial revolution, has emerged as the main standard of service and resource delivery. As cloud computing has grown into a mature technology and is being rapidly adopted in many HEIs across the world, retaining customers of this innovative technology has become a challenge to the cloud service providers. Current research trends on cloud computing have sought to study the acceptance or adoption of technology; however, little research has been devoted to the continuance use in an organizational setting. To address this gap, this study aims to investigate the antecedents of cloud computing continuance use in HEIs. Hence, drawing on the prior literature in organizational-level continuance, this research established a conceptual model that extends and contextualizes the IS continuance model through the lens of the TOE framework (i.e., technological, organizational, and environmental influences). The results of a pilot study, conducted through a survey with information and communications technology (ICT) decision makers, and based on the proposed conceptual model, indicate that the instrument is both reliable and valid, and so point the way towards further research. The paper closes with a discussion of the research limitations, contribution, and future directions.

scholarly journals Cloud Computing Adoption in Higher Education Institutions: A Systematic Review

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63722-63744 ◽  
Author(s):  
Yousef A. M. Qasem ◽  
Rusli Abdullah ◽  
Yusmadi Yah Jusoh ◽  
Rodziah Atan ◽  
Shahla Asadi
Keyword(s):  
Higher Education ◽  
Systematic Review ◽  
Cloud Computing ◽  
Higher Education Institutions ◽  
Cloud Computing Adoption

scholarly journals A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

2020 ◽  
Vol 10 (14) ◽  
pp. 4905 ◽  
Author(s):  
Yousef A. M. Qasem ◽  
Shahla Asadi ◽  
Rusli Abdullah ◽  
Yusmadi Yah ◽  
Rodziah Atan ◽  
...  
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Structural Equation ◽  
Higher Education Institutions ◽  
Sem Analysis ◽  
Decision Makers ◽  
Equation Modeling ◽  
Managerial Decision ◽  
Cloud Computing Adoption ◽  
Analytical Approaches

Cloud computing (CC) delivers services for organizations, particularly for higher education institutions (HEIs) anywhere and anytime, based on scalability and pay-per-use approach. Examining the factors influencing the decision-makers’ intention towards adopting CC plays an essential role in HEIs. Therefore, this study aimed to understand and predict the key determinants that drive managerial decision-makers’ perspectives for adopting this technology. The data were gathered from 134 institutional managers, involved in the decision making of the institutions. This study applied two analytical approaches, namely variance-based structural equation modeling (i.e., PLS-SEM) and artificial neural network (ANN). First, the PLS-SEM approach has been used for analyzing the proposed model and extracting the significant relationships among the identified factors. The obtained result from PLS-SEM analysis revealed that seven factors were identified as significant in influencing decision-makers’ intention towards adopting CC. Second, the normalized importance among those seven significant predictors was ranked utilizing the ANN. The results of the ANN approach showed that technology readiness is the most important predictor for CC adoption, followed by security and competitive pressure. Finally, this study presented a new and innovative approach for comprehending CC adoption, and the results can be used by decision-makers to develop strategies for adopting CC services in their institutions.

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