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Cloud Adoption: A Conceptual Model to Assess the Maturity of South African Large Enterprises

Published:02 August 2018Publication History

ABSTRACT

Cloud adoption has gained momentum recently, however cloud adoption is still a maturing concept. Cloud maturity frameworks are therefore required by organizations to assess their cloud maturity. This study focuses on the business impact and management of cloud adoption rather than on the technical aspects of server virtualization, optimization and integration. The aim of this study to propose a framework, which enterprises could use to evolve their cloud maturity to a higher level, improving business efficiency and effectiveness. Higher levels of maturity allow organizations to leverage the benefits of cloud by aligning technology adoption to business requirements, whilst managing the risks. This theoretical model could aid IT managers and decision makers use evidence-based management principles determine their maturity of cloud adoption i.e. support managers of large enterprises close the 'research-practice gap' by basing business management practices on research evidence. The method of this study is to use an in-depth, qualitative approach. We are studying South African large enterprise cases by interacting with respondents associated with the cloud adoption journey (i.e. CIOs, CTOs, technology heads, and IT managers). A semi-structured questionnaire has been prepared and is being administered through in-depth interviews. The researcher has completed 12 in-depth interviews and it is still necessary to shortly address the testing and evaluation of the model. The test results phase is still being conducted and is not being presented in this contribution. This paper presents the conceptual model of the study.

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    • Published in

      cover image ACM Other conferences
      ICEME '18: Proceedings of the 2018 9th International Conference on E-business, Management and Economics
      August 2018
      169 pages
      ISBN:9781450365147
      DOI:10.1145/3271972

      Copyright © 2018 ACM

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      • Published: 2 August 2018

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