Fog Computing Adoption for Higher Education Institutions: A Perspective of Multi-Analytical Structural Equation Modeling and Artificial Neural Networks

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Ammar Thabit Zahary
Baker Ali Alzenni

Abstract

Fog computing is employed in widespread sectors; fog computing in higher education will probably enhance the operations of higher education institutions. However, the adoption of fog computing in this context is still in its early stages, and research in this area is limited. So, there appears to be a deficiency in the adoption of fog computing to enhance higher education institutions’ operations. The current paper implements a quantitative methodology the data was gathered through a questionnaire, where 103 surveys were collected from academic and information technology staff at nine universities in Sana’a, Yemen. This study aims to build a conceptual framework to identify factors that affect in adoption of fog technology by higher education institutes and investigate them. where the current study utilizes the Technological, Organizational, and Environmental (TOE) framework as a basis for analysis. Nine factors have been tested in this study, where the factors relative advantage, Compatibility, Security, privacy, Technology Readiness, TOP management, Regulatory Policy, Competitive Pressure, and Socio Culture have a positive effect. The proposed framework was empirically validated using multi-analytical structural equation modelling and artificial neural network (SEM-ANN) method. We identify the relative advantage of the first ranking (100%), Socio-Culture (67%), security (55%), compatibility (49%), privacy (45%), competitive pressure, and technology readiness are in the same ranking, with a score of 31%. Finally, the government regulation and top management support have (23%) and (29%), respectively, as the significant factors that affect in adoption of fog computing by higher education institutes.

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How to Cite
Zahary, A. T., & Alzenni, B. A. (2025). Fog Computing Adoption for Higher Education Institutions: A Perspective of Multi-Analytical Structural Equation Modeling and Artificial Neural Networks. Sana’a University Journal of Applied Sciences and Technology, 3(3), 777–794. https://doi.org/10.59628/jast.v3i3.1558
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