A systematic literature review and critical assessment of model-driven decision support for IT outsourcing

作者:

Highlights:

• IT sourcing decision support researchers adopted diverse decision analysis methods.

• Use of naturalistic evaluation & reference theories is limited in IT sourcing research.

• Recommendation for development of IT sourcing decision support artifacts presented.

摘要

Information technology outsourcing (ITO) is a widely-adopted strategy for IT governance. The decisions involved in IT outsourcing are complicated. Empirical research confirms that a rational and formalized decision-making process results in better decision outcomes. However, formal and systematic approaches for making ITO decisions appear to be scarce in practice. To support organizational decision-makers involved in IT outsourcing (including cloud sourcing), researchers have suggested several decision support methods. To date there is no comprehensive review and assessment of the research in this domain. In this study 133 model-driven decision support research articles for IT outsourcing and cloud sourcing were identified through a systematic literature review and assessed based on a highly-regarded research framework. An analysis of these 133 research articles suggested a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to support different IT outsourcing decisions. Our findings raise concerns about the limited use of reference design theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported in ITO decision support literature. Based on the review, we provide future research directions, as well as a number of recommendations to enhance the rigor and relevance of ITO Decision Support Systems research.

论文关键词:IT outsourcing,Cloud sourcing,Model-driven decision support,Research evaluation,Systematic literature review

论文评审过程:Received 12 November 2016, Revised 31 May 2017, Accepted 6 July 2017, Available online 8 July 2017, Version of Record 18 September 2017.

论文官网地址:https://doi.org/10.1016/j.dss.2017.07.002