INFORMATION SYSTEMS OUTSOURCING DECISIONS UNDER FUZZY GROUP DECISION MAKING APPROACH

摘要

During the last decade, information system (IS) outsourcing has emerged as a major issue for organizations. As outsourcing decisions are often based on multicriteria approaches and group decisions, this paper proposes a structured methodology based on Fuzzy group decision making approach to evaluate and select the appropriate information system project (ISP) in an actual case. To achieve our purpose, we argue that seven criteria consisting of risk, management, economics, technology, resource, quality, and strategy and five ISPs should be considered for outsourcing decisions. Fuzzy analytic hierarchy process (fuzzy AHP) to find the priority and ranking of each ISP is considered. Using fuzzy theory for selecting the proper IS project can reduce ambiguities and uncertainties that are inherent in the selection procedure. A sensitivity analysis is performed to check the steadiness of the priority ranking and help decision makers to understand different scenarios that show alternative potential developments or different viewpoints concerning the relative importance of the criteria. Our proposed methodology can be applied by a cross-functional team of practitioners and IT managers to select the proper IS project. Also, the proposed approach can effectively consider complex and qualitative decision variables involved in the IS outsourcing decision-making problem. Finally, conclusion and potential issues for future research are presented. The main findings of this research have showed that the combination of AHP method and fuzzy concept to rank and prioritize of IS projects to outsource is a useful and practical tool to make traceable and reliable decision. According to results, a priority of overall score for the five candidates of IS projects reflects that project of "development of the supplier relationship management information system" performs the best, and project of "facilities management" performs the worst according to the experts' judgment in case study.