Dissertation/Thesis Abstract

Factors Influencing the Adoption of Cloud Computing Driven by Big Data Technology: A Quantitative Study
by Chowdhury, Naser, Ph.D., Capella University, 2018, 114; 10846772
Abstract (Summary)

A renewed interest in cloud computing adoption has occurred in academic and industry settings because emerging technologies have strong links to cloud computing and Big Data technology. Big Data technology is driving cloud computing adoption in large business organizations. For cloud computing adoption to increase, cloud computing must transition from low-level technology to high-level business solutions. The purpose of this study was to develop a predictive model for cloud computing adoption that included Big Data technology-related variables, along with other variables from two widely used technology adoption theories: technology acceptance model (TAM), and technology-organization-environment (TOE). The inclusion of Big Data technology-related variables extended the cloud computing's mix theory adoption approach. The six independent variables were perceived usefulness, perceived ease of use, security effectiveness, the cost-effectiveness, intention to use Big Data technology, and the need for Big Data technology. Data collected from 182 U.S. IT professionals or managers were analyzed using binary logistic regression. The results showed that the model involving six independent variables was statistically significant for predicting cloud computing adoption with 92.1% accuracy. Independently, perceived usefulness was the only predictor variable that can increase cloud computing adoption. These results indicate that cloud computing may grow if it can be leveraged into the emerging Big Data technology trends to make cloud computing more useful for its users.

Indexing (document details)
Advisor: Lucarelli, Christopher
Commitee: GORRIARAN, ADOLFO, ROBINSON, GARY
School: Capella University
Department: School of Business and Technology
School Location: United States -- Minnesota
Source: DAI-B 79/12(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer Engineering, Information Technology, Computer science
Keywords: Artificial intelligence, Big data, Cloud computing, Machine learning, Predictive analytics, Technology adoption models
Publication Number: 10846772
ISBN: 9780438278752
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