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Development of a model-based netsourcing decision support system using a five-stage methodology

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  • Loebbecke, Claudia
  • Huyskens, Claudio

Abstract

Managers increasingly face netsourcing decisions of whether and how to outsource selected software applications over the Internet. This paper illustrates the development of a netsourcing decision support system (DSS) that provides support for the first netsourcing decision of whether to netsource or not to do so. The development follows a five-stage methodology focusing on empirical modeling with internal validation during the development. It begins with identifying potential decision criteria from the literature followed by the collection of empirical data. Logistic regression is then used as a statistical method for selecting relevant decision criteria. Applying the logistic regression analysis to the dataset delivers competitive relevance and strategic vulnerability as relevant decision criteria. The development concludes with designing a core and a complementary DSS module. The paper critiques the developed DSS and its underlying development methodology. Recommendations for further research are offered.

Suggested Citation

  • Loebbecke, Claudia & Huyskens, Claudio, 2009. "Development of a model-based netsourcing decision support system using a five-stage methodology," European Journal of Operational Research, Elsevier, vol. 195(3), pages 653-661, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:653-661
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    References listed on IDEAS

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    1. Korhonen, Pekka & Moskowitz, Herbert & Wallenius, Jyrki, 1992. "Multiple criteria decision support - A review," European Journal of Operational Research, Elsevier, vol. 63(3), pages 361-375, December.
    2. Joyce E. Berg & Thomas A. Rietz, 2003. "Prediction Markets as Decision Support Systems," Information Systems Frontiers, Springer, vol. 5(1), pages 79-93, January.
    3. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    4. Matsatsinis, Nikolaos F. & Siskos, Yannis, 1999. "MARKEX: An intelligent decision support system for product development decisions," European Journal of Operational Research, Elsevier, vol. 113(2), pages 336-354, March.
    5. Ngwenyama, Ojelanki K. & Bryson, Noel, 1999. "Making the information systems outsourcing decision: A transaction cost approach to analyzing outsourcing decision problems," European Journal of Operational Research, Elsevier, vol. 115(2), pages 351-367, June.
    6. Taha, Mahmoud A. & Park, Sang C. & Russell, Jeffrey S., 1995. "Knowledge-based DSS for construction contractor prescreening," European Journal of Operational Research, Elsevier, vol. 84(1), pages 35-46, July.
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    Cited by:

    1. Tjader, Youxu Cai & Shang, Jennifer S. & Vargas, Luis G., 2010. "Offshore outsourcing decision making: A policy-maker's perspective," European Journal of Operational Research, Elsevier, vol. 207(1), pages 434-444, November.
    2. Tjader, Youxu & May, Jerrold H. & Shang, Jennifer & Vargas, Luis G. & Gao, Ning, 2014. "Firm-level outsourcing decision making: A balanced scorecard-based analytic network process model," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 614-623.

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