Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study

2015 ◽  
Vol 18 (4) ◽  
pp. 1527-1537 ◽  
Author(s):  
AlAlaa N. Tashkandi ◽  
Ibrahim M. Al-Jabri
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Saudi Arabia ◽  
Exploratory Study ◽  
Higher Education Institutions ◽  
Cloud Computing Adoption

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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