Proposing a model for cloud computing deployment to enhance higher education at Mazandaran University of Medical Sciences

Subject Areas :

Seyed Mahmoud Hosseini ¹ , mahsa Gholamhoseinzadeh ² , فرشیده ضامنی ³ , Hasan Siamian ⁴

1 - M.A. Graduate of Educational Sciences, Islamic Azad University, Sari Branch
2 - عضو هیات علمی دانشگاه آزاد اسلامی واحد ساری
3 - دانشیار گروه علوم تربيتي دانشگاه ازاد اسلامي واحد ساري
4 - Department of Health Information Technology, Health Sciences Research Center, Addiction Institute, School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari, Iran.

Received: 2024-04-27 Accepted : 2024-06-02 Published : 2025-07-10

Keywords: Model presentation, cloud computing deployment, higher education development,

Abstract :

This research sought to develop a model for integrating cloud computing into higher education at Mazandaran University of Medical Sciences. Employing an exploratory mixed-methods design, the study began with a qualitative phase. In this stage, ten experienced university professors, holding PhDs and ranking as associate professors or higher in medical sciences, were purposively selected. Their insights were gathered through semi-structured interviews and analyzed using qualitative content analysis. The quantitative phase involved 276 faculty members and guests from Mazandaran University of Medical Sciences, chosen via stratified random sampling based on Cochran's formula from a total of 975 individuals. Data was collected using a custom-designed questionnaire, which demonstrated strong face and content validity, along with a Cronbach's Alpha above 0.7. Structural Equation Modeling (SEM) was then applied for data analysis. The qualitative findings pinpointed five key cloud computing elements for implementation: Planning, Model Selection, Service Vendor Selection, Service Level Agreement (SLA), and Optimization. Additionally, three primary categories of factors influencing higher education development were identified: Intra-organizational, Extra-organizational, and Infrastructural. Quantitatively, the results highlighted "Model Selection" as having the strongest relationship between the two variables, exhibiting the highest path coefficient (0.866) and an R2 value of 0.749.

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