IJBTT

Logistic Regression Model to Examine the Impact of Big Data Engineering for Cloud Computing Adoption in UAE

Logistic Regression Model to Examine the Impact of Big Data Engineering for Cloud Computing Adoption in UAE
	© 2022 by IJETT Journal
	Volume-70 Issue-12
	Year of Publication : 2022
	Author : Waleed Saeed Mahmoud Mahmoud Ali, Abd Samad Hasan Basari, Zeratul Izzah Mohd Yusoh
	DOI : 10.14445/22315381/IJETT-V70I12P239

How to Cite?

Waleed Saeed Mahmoud Mahmoud Ali, Abd Samad Hasan Basari, Zeratul Izzah Mohd Yusoh, "Logistic Regression Model to Examine the Impact of Big Data Engineering for Cloud Computing Adoption in UAE," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 415-420, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P239

Abstract
This paper proposed an impact model for Cloud Computing Adoption (CCA), developed based on Big Data Engineering (BDE) factors. There are two established models, namely Technology Acceptance Model (TAM) and Technology Organization-Environment (TOE), to support the CCA prediction with selected variables. Six independent variables are identified to be included as a prediction of CCA: usefulness, ease of use, security effectiveness, cost-effectiveness, intention to use Big Data technology and the need for Big Data technology. This data is collected via a sample size of 250 from large businesses organization in the UAE. In order to analyze the data, binary logistic regression is utilized. Due to technological advancements and changes in the current business climate, it is important to examine BDE's impact on CCA, as well as the longer-term implications of BDE and CCA on organizations. The result of this study shows that six independent variables are statistically significant in predicting CCA. The outcomes are useful for managers considering the adoption of cloud computing. It is important to study because of the technological advancement and changes in the current business landscape, BDE's effect on CCA can be recognized, and the broader high impact of BDE and CCA on large business organizations. Furthermore, large business organizations must produce impactful outcomes for the whole organization to ensure pertinent in the BDE. The result shows that CCA can be predicted independently by all factors except cost-effectiveness.

Keywords
Cloud Computing Adoption (CCA), Big Data Engineering (BDE), Technology Acceptance Models (TAM), Technology Organization Environment (TOE)

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