دانلود مقاله ISI انگلیسی شماره 82049
ترجمه فارسی عنوان مقاله

یک مرور ادبی سیستماتیک و ارزیابی انتقادی از حمایت تصمیم گیری مبتنی بر مدل برون سپاری فناوری اطلاعات

عنوان انگلیسی
A systematic literature review and critical assessment of model-driven decision support for IT outsourcing
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
82049 2017 44 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 102, October 2017, Pages 42-56

ترجمه کلمات کلیدی
فناوری اطلاعات برون سپاری، ابر استخراج، پشتیبانی تصمیم گیری مبتنی بر مدل، ارزیابی تحقیق، بررسی ادبیات سیستماتیک،
کلمات کلیدی انگلیسی
IT outsourcing; Cloud sourcing; Model-driven decision support; Research evaluation; Systematic literature review;
پیش نمایش مقاله
پیش نمایش مقاله  یک مرور ادبی سیستماتیک و ارزیابی انتقادی از حمایت تصمیم گیری مبتنی بر مدل برون سپاری فناوری اطلاعات

چکیده انگلیسی

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.