Best Leadership Practices of Multinational Corporations in the use of Automated Migration Tools in Adoption of Commercial Cloud Computing Platforms: A Meta-Analysis

Ethan M Sneider, Purdue University

Abstract

Transitioning to cloud computing is a complex and major effort for large multinational corporations (MNCs). Automated cloud migration tools (ACMTs) have been developed and are evolving to streamline this process. The potential benefits of their use are reported to be significant in terms of cost, time, and business innovation. Academic research on ACMTs and the best leadership practices for their use has been limited.The purpose of the research was to identify the best leadership practices of MNCs in the use of automated migration tools for the adoption of commercial cloud computing platforms. Adoption of cloud computing is a major technological shift occurring globally, and is still in early stages of growth. Major providers of commercial cloud computing platforms include technological giants such as Microsoft, Amazon Web Services, Google, Oracle and IBM.A meta-analysis designed research approach focusing on the triangulation of case studies, cloud computing industry data and trends from cloud service providers (CSP) revealed that best practices of leaders within MNCs fall under three main categories: awareness, impact and actions. Further, it was determined that the ACMTs with the most advanced capabilities do not necessarily equate to faster realization of cloud value for the MNC.With the continued development of ACMTs and their growing adoption, further study on the role of automation in cloud migration solution deployment will be critical, as ACMT capabilities will continue to mature. No longer the sole domain of becoming a market leader alone, organizations that utilize ACMTs are increasingly doing so just to maintain competitive parity, as the true differentiator in organizational excellence is now in cloud optimization and not simply just getting to the cloud.

Degree

Ph.D.

Advisors

Naimi, Purdue University.

Subject Area

Artificial intelligence|Management|Statistics

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