An empirical study of establishing guidelines for evaluation and adoption of secure and cost effective cloud computing
dc.contributor.advisor | Buckley, Kevan | |
dc.contributor.advisor | Garvey, Mary | |
dc.contributor.advisor | Li, Jun | |
dc.contributor.author | Ullah, Raja Muhammad Ubaid | |
dc.date.accessioned | 2023-05-12T10:56:00Z | |
dc.date.available | 2023-05-12T10:56:00Z | |
dc.date.issued | 2023-04 | |
dc.identifier.citation | Ullah, R.M.U. (2023) An empirical study of establishing guidelines for evaluation and adoption of secure and cost effective cloud computing. University of Wolverhampton. http://hdl.handle.net/2436/625195 | en |
dc.identifier.uri | http://hdl.handle.net/2436/625195 | |
dc.description | A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy. | en |
dc.description.abstract | This research investigates the factors influencing large enterprises, small and medium-sized enterprises (SMEs) behavioural intention toward adopting cloud computing (CC) services. The increasing adoption of CC services is changing how businesses maintain, select, update, and manage information and communication technology. In particular, CC services have the potential to improve IT systems reliability and scalability, allowing large enterprises and SMEs to use their limited resources on their core business and strategy. Many factors and variables influence technology adoption and usage decisions in the large enterprises and SMEs context. Despite the extensive literature, there still needs to be more research on the factors influencing large enterprises and SMEs uptakes CC services adoption. Therefore, examining large enterprises and SMEs adoption of CC is essential for successfully implementing this system. This thesis uses environmental, human, organisational, and technological factors to model the relationship between the variables considered and CC services adoption to increase the probability that large enterprises, and SMEs adopt CC services successfully. The study considers the influence of eleven variables: external support, competitive pressure, senior management support, employee's cloud knowledge, adequate resources, information intensity, relative advantage, complexity, compatibility, security/privacy, and cost-effectiveness. A quantitative research approach was applied using an online questionnaire. A conceptual model of CC services adoption by large enterprises and SMEs has been developed. Research factors and variables identified to influence the likelihood that large enterprises and SMEs would adopt CC services successfully. In particular, we found nine research variables to be statistically significant, and two adequate support and complexity non-significant. It was found that CC services adoption variances among the size of organisations to differ and be statistically significant towards adopting CC services. Hence, this result is important to owners and decision makers of large enterprises, and SMEs enterprises, service providers, service consultants, and governments to assist them in facilitating the adoption of CC services by large enterprises, and SMEs. Further, this may help to establish strategies for large enterprises, and SMEs to confirm a better adoption of CC services. | en |
dc.format | application/pdf | en |
dc.language.iso | en | en |
dc.publisher | University of Wolverhampton | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | cloud computing | en |
dc.subject | cloud | en |
dc.subject | adoption | en |
dc.subject | security | en |
dc.subject | services | en |
dc.subject | evaluation | en |
dc.subject | cyber security | en |
dc.title | An empirical study of establishing guidelines for evaluation and adoption of secure and cost effective cloud computing | en |
dc.type | Thesis or dissertation | en |
dc.contributor.department | School of Engineering, Computing and Mathematical Sciences, Faculty of Science and Engineering | |
dc.type.qualificationname | PhD | |
dc.type.qualificationlevel | Doctoral | |
refterms.dateFOA | 2023-05-12T10:56:01Z |