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Critical Decision-Making on Cloud Computing Adoption in Organizations Based on Augmented Force Field Analysis

Cloud Computing (CC) has become an important milestone information technology that attracts many organizations. With the potential to transform business processes, lower IT expenses, and offer access to unlimited computing resources with minimal management effort, organizations look to cloud-based solutions to achieve business efficiencies. Thus, it would seem that these organizations could easily migrate to CC. However, enterprises are still concerned about moving their business-critical applications to the cloud. Among the reasons are that it is an emerging technology that has not reached a level of maturity; the lack of industry-specific conformity to standards; and a high level of security risks. As a result, there is a big dispute among organizations on the decision of whether it is more business-efficient to embark on the cloud or remain with their interior IT infrastructures. In this paper, we aim to solve this debate by proposing a novel approach that supports decision-making on CC adoption in organizations. Unlike traditional decision-making approaches that pay little or no consideration to organizational high-level business objectives, our proposed approach is driven by the business objectives of the organization. First, we identify driving and restraining forces that influence CC adoption in organizations. Second, a formal decision-making model is proposed based on Force Field Analysis (FFA) augmented by pairwise comparison and Delphi methods, this model estimates the values of the driving and restraining forces based on their impacts on the organization’s objectives. By analyzing the forces for and against CC adoption, organizations can decide whether or not to move forward with the adoption. Alternatively, organizations can use the analysis to think about how they can strengthen the forces that support the adoption and weaken the forces opposing it, so that the adoption is more successful. The proposed model is validated for usability and applicability through a use case scenario.