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

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.

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

1] Naser Chowdhury, “Factors Influencing the Adoption of Cloud Computing Driven by Big Data Technology: A Quantitative Study,” Capella University ProQuest Dissertations, 2018.
[2] Mohd Hadyan Wardhana et al., “Novel Hybrid Trauma Injury Classification based on Trauma Revise Injury Severity Score (TRISS) and Visum et Repertum (VeR) Features,” International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 462-470, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P248
[3] Wan Zulaikha Wan Yaacob et al., “Enhancement Face Detection using Viola-Jones and Multi-Block Local Binary Pattern,” International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 114–119, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P213
[4] Hayder Salah Hashim, Zainuddin Bin Hassan, and Ali Salah Hashim, “Factors Influence the Adoption of Cloud Computing: A Comprehensive Review,” International Journal of Education and Research, vol. 3, no. 7, pp. 295–306, 2015.
[5] Huan Liu, “Big Data Drives Cloud Adoption in Enterprise,” IEEE Internet Computing, vol. 17, no. 4, pp. 68–71, 2013. Crossref, https://doi.org/10.1109/MIC.2013.63
[6] Hemalatha S, Kokila M. S. and Krithika S, “Cloud Computing in Big Data Analytics,” International Journal of Engineering and Management Research, vol. 6, no. 1, pp. 381–383, 2016.
[7] Xiang T.R. Kong, “Robot-Enabled Execution System for Perishables Auction Logistics,” Industrial Management and Data Systems, vol. 117, no. 9, pp. 1954-1971, 2017. Crossref, https://doi.org/10.1108/IMDS-03-2016-0114
[8] Djuro Mirkovic, “Improved Internet Resource Allocation and Performance through the use of Network Coordinate Systems,” Thesis (PhD), Swinburne University of Technology, 2021.
[9] Raja Muhammad Ubaid Ullah et al., “Cloud Computing Adoption in Enterprise: Challenges and Benefits,” International Journal of Computer Trends and Technology, vol. 67, no. 6, pp. 93-104, 2019. Crossref, https://doi.org/10.14445/22312803/IJCTT-V67I6P116
[10] Youlan Lu, “Analysis of the Impact of the Internet on Farmers’ Intelligent Selection under the Era of Big Data and Cloud Computing,” Journal of Physics: Conference Series, p. 012211, 1982. Crossref, https://doi.org/10.1088/1742-6596/1982/1/012025
[11] Adhi Prakosa, and Ahsan Sumantika, “An Analysis of Online Shoppers’ Acceptance and Trust toward Electronic Marketplace using TAM Model,” Journal of Physics: Conference Series, p. 012008, 2021. Crossref, https://doi.org/10.1088/1742-6596/1823/1/012008
[12] Saraswati Koppad et al., “Cloud Computing Enabled Big Multi-Omics Data Analytics,” Bioinformatics and Biology Insights, vol. 15, 2021. Crossref, https://doi.org/10.1177/11779322211035921
[13] Lena Gribel, “Drivers of Wearable Computing Adoption: An Empirical Study of Success Factors Including IT Security and Consumer Behaviour-Related Aspects,” University of Plymouth, 2018.
[14] Nishant Katiyar, and Rakesh Bhujade, "A Survey: Adoption of Cloud Computing in Education Sector," International Journal of Computer Trends and Technology, vol. 60, no. 1, pp. 15-25, 2018. Crossref, https://doi.org/10.14445/22312803/IJCTT-V60P102
[15] Shunza R. Williams-Thomas, “English Language Proficiency Assessment and Georgia Milestone English Assessment: A Quantitative Non-Experimental Study among Reclassified Fifth-Graders,” Dissertation, Northcentral University, 2021.
[16] Edgardo Vargas Moya, “Security and Privacy Risks Associated of Cloud Computing: A Correlational Study,” Dissertation, Capella University, 2021.
[17] Robert Challen et al., “Risk of Mortality in Patients Infected with SARS-Cov-2 Variant of Concern 202012/1: Matched Cohort Study,” BMJ, vol. 372, no. 579, 2021. Crossref, https://doi.org/https://doi.org/10.1136/bmj.n579
[18] Uma Sekaran, Research Method for Business a Skill Building Approach, 4th ed., John Wiley & Sons, New York, 2003.
[19] Noorayisahbe Mohd Yaacob, et al., “Factors and Theoretical Framework that Influence User Acceptance for Electronic Personalized Health Records,” Personal and Ubiquitous Computing, pp 1-13, 2021. Crossref, https://doi.org/10.1007/s00779-021-01563-y
[20] R. Santhana Lakshmi, and S. Jayalakshmi, "Factors Influencing SMEs towards Execution of Technology Adoption Model in Cloud Computing," International Journal of Engineering Trends and Technology, vol. 69, no. 3, pp. 189-194, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I3P229
[21] Barbara Roux et al., “Cross-Cultural Adaptation and Psychometric Validation of the Revised Patients’ Attitudes towards Deprescribing (Rpatd) Questionnaire in French,” Research in Social and Administrative Pharmacy, vol. 17, no. 8, pp. 1453–1462, 2021. Crossref, https://doi.org/10.1016/j.sapharm.2020.11.004
[22] Chinonso Nwamaka Igwesi-Chidobe et al., “World Health Organisation Disability Assessment Schedule (WHODAS 2.0): Development and Validation of the Nigerian Igbo Version in Patients with Chronic Low Back Pain,” BMC Musculoskeletal Disorders, vol. 21, no. 1, pp. 1–14, 2020. Crossref, https://doi.org/10.1186/s12891-020-03763-8
[23] M. G. Schultz et al., “Can Deep Learning Beat Numerical Weather Prediction?” Philosophical Transactions of the Royal Society A, vol. 379, no. 2194, p. 20200097, 2021. Crossref, https://doi.org/10.1098/rsta.2020.0097
[24] William Harold Stanley, “Relationships among Dimensions of Information System Success and Benefits of Cloud,” Dissertation, Walden University, 2021.
[25] Noorayisahbe Mohd Yaacob et al., “Electronic Personalized Health Records [E-PHR] Issues towards Acceptance and Adoption,” International Journal of Advanced Science and Technology, vol. 28, no. 8, pp. 1-9, 2019.