Development and Evaluation of a Cloud Computing Adoption Framework (CCAFF) for Retail Banks in Bahrain

Abstract

The Information Technology era supports the adoption of Cloud Computing (CC) on a large scale in many business fields, at which the Financial Institutions (i.e., Banking sector) is not an exception. Several gaps were found during an attempted literature reviewing. While several CC methods, strategies, or frameworks have been proposed or utilized for CC adoption in many economic sectors including the Financial institutes (FI)- Banking, it is still a relatively new initiative in many other countries including Bahrain. While Enterprise Architecture (EA) is claimed to be a leading information and business management discipline to develop an architecture that guides the transformation to CC adoption of an enterprise from a baseline state to a target state. However, there is an evident lack of scholarly articles on the development of CC adoption frameworks from EA perspective. Also, it was found that scholarly articles in the evaluation of CC adoption framework are scarce. To address those gaps, this paper aims at developing and evaluating a CCAFF for the CC adoption in Bahraini Banks by adapting a tailored version of the Open Group Architecture Framework (TOGAF) and embedding a tailored version of an enterprise cloud adoption strategy (ECAS) and then evaluating the CCAFF based on several criterions. A six phased Design Science Research Methodology (DSRM) is employed to design the research report, guide, and develop the CCAFF, while the exploratory nature of the research necessitates the employment of a holistic single Case Study strategy for an FI-01 bank, based on semi structured interviews and document analysis data collection techniques. Alongside, the data was analyzed using pattern matching technique. For the CCAFF evaluation, Delphi technique was employed at which seven experts in two rounded panels contribute to the evaluation findings based on six criterions. The evaluation findings demonstrated promising results in terms of ease of use (86.4%), usefulness (84.6%), decision making support (86.6%), comprehensiveness (85.8%), time efficiency (84.8%), and usage intention (84.8%).