Dissertation/Thesis Abstract

Accepting the Cloud: A Quantitative Predictive Analysis of Cloud Trust and Acceptance Among IT Security Professionals
by Peake, Chris, Ph.D., Capella University, 2018, 157; 10980831
Abstract (Summary)

Industry experts recognize the cloud and cloud-based services as advantageous from both operational and economic perspectives, yet the gap is that individuals and organizations hesitate to accept the cloud because of concerns about security and privacy. The purpose of this study is to examine what factors that may influence the cloud acceptance by IT professionals by focusing on the principal research question: To what extent do ease of use, usefulness, attitude, security apprehensions, compatibility, and trust predict IT security professionals’ acceptance of cloud computing. The population for this study consisted of IT security professionals who either had industry security certifications or had been in a security position for at least two years. Sample inclusion criteria consisted IT professionals with the qualification described above and over the age of 18 who were living in the United States. The study survey was administered using SurveyMonkey, which randomly selected and recruited potential participants who met the sample criteria from a participant database, resulting in ninety-seven total study participants. Among the six factors examined, perceived usefulness, attitudes, security apprehensions, and trust were found to significantly predict cloud acceptance. The results indicate that cloud service providers should focus their attention on these factors in order to promote cloud acceptance.

Indexing (document details)
Advisor: Vucetic, Jelena
Commitee: Braye, Rubye, Luo, Wenbin
School: Capella University
Department: Business and Technology
School Location: United States -- Minnesota
Source: DAI-B 80/04(E), Dissertation Abstracts International
Subjects: Information Technology, Computer science
Keywords: Cloud computing, Privacy, Quantitative regression analysis, Utaut
Publication Number: 10980831
ISBN: 9780438736733
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