Mahmoud Ali, Waleed Saeed Mahmoud (2023) Big data engineering and cloud computing adoption model in United Arab Emirates large business organizations. Doctoral thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
The adoption of cloud computing and Big Data Engineering (BDE) in large business organizations in the United Arab Emirates (UAE) can bring numerous benefits, including increased efficiency, flexibility, and scalability of computing resources. Many researchers have proposed that BDE can drive Cloud Computing Adoption (CCA). However, there is a need for more empirical research to determine the extent to which BDE drives CCA in practice. This research could help organizations better understand the relationship between these technologies and develop more effective strategies for adoption. Despite the potential benefits of BDE and CCA in large business entities in the UAE, there is a lack of research or studies on the topic. This has resulted in a limited representation of the adoption of these technologies in UAE. As a result, it is important for organizations to carefully evaluate their readiness for adoption, develop a clear strategy, and seek guidance from experienced service providers. Since developing technologies have strong ties to CCA and BDE, CCA has taken place in both hypothetical and business situations. Due to the technological advancements and changes in the current business landscape, it is important to examine how BDE can affect CCA and its implications. In order to stay relevant in BDE, business organizations must produce advanced results at every level of their organization. The CCA impact model was developed using BDE factors and two widely established models namely the Technology Acceptance Model (TAM) and Technology Organization-Environment (TOE). The CCA was extended by including variables related to BDE. Six independent variables were examined: usefulness, ease of use, security effectiveness, cost-effectiveness, intention to use and need for Big Data technology. A sample size of 250 was used to collect data from large business organizations in the UAE. After data cleaning and removing missing values, the sample size was 204. The data were analyzed using binary logistic regression. In the current business climate, it is important to examine BDE’s impact on CCA, as well as the longerterm implications of BDE and CCA on organizations. BDE requires organizations to produce advanced results at every level of their organization. Based on the findings, CCA is predicted by perceived ease of use, perceived usefulness, security effectiveness, intention to use Big Data, and need to utilize Big Data technology and cannot be predicted by cost-effectiveness. The model correctly predicts whether the participant companies would use cloud computing 90.6% of the time and explained 9.4% of the variance in cloud computing adoption. The results of the study are believed can help managers decide whether to adopt cloud computing.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Information technology, Cloud computing, Big data |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor |
| Divisions: | Library > Tesis > FTMK |
| Depositing User: | Unnamed user with email nuraina0324@gmail.com |
| Date Deposited: | 12 Nov 2024 09:44 |
| Last Modified: | 12 Nov 2024 09:44 |
| URI: | http://eprints.utem.edu.my/id/eprint/27702 |
| Statistic Details: | View Download Statistic |
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