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Value-Driven Strategic Sourcing Based on Service-Dominant Logic

Author

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  • Laleh Rafati

    (Center for Service Intelligence, Faculty of Economics and Business Administration, Ghent University, 9000 Ghent, Belgium)

  • Geert Poels

    (Center for Service Intelligence, Faculty of Economics and Business Administration, Ghent University, 9000 Ghent, Belgium)

Abstract

Currently, procurement is approached as a tactical process focused on spend management. The aim of this process is the identification of cost savings. A new paradigm of procurement is emerging that recognizes procurement as a value creation practice. This paradigm, referred to as value-driven strategic sourcing, lacks instruments for implementation. This paper presents a new conceptual modeling approach for exploring and evaluating sourcing alternatives that is based on a systemic view of value cocreation. Our approach, called CARS (which stands for capability, actor, resource, and service), is the result of a design science research project. This paper presents the underlying conceptualization of CARS, which was constructed through a mapping between service ecosystem concepts grounded in service-dominant logic and the viable system approach and strategic sourcing concepts derived from the resource-based view theory of competitive advantage, the dynamic capability theory, and the relational view theory of cooperation and competition. Apart from presenting the theoretical foundation of CARS, we also demonstrate by means of a case study of sustainable procurement in a global materials technology company how a model-based approach based on CARS helps in implementing value-driven strategic sourcing. The case study provides a proof of concept of the potential utility of our approach as it addresses specific problems with the company’s current procurement practices.

Suggested Citation

  • Laleh Rafati & Geert Poels, 2017. "Value-Driven Strategic Sourcing Based on Service-Dominant Logic," Service Science, INFORMS, vol. 9(4), pages 275-287, December.
    • Handle: RePEc:inm:orserv:v:9:y:2017:i:4:p:275-287
    DOI: 10.1287/serv.2017.0190
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    References listed on IDEAS

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    1. John Venable & Jan Pries-Heje & Richard Baskerville, 2016. "FEDS: a Framework for Evaluation in Design Science Research," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(1), pages 77-89, January.
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    Cited by:

    1. William A. Muir & Daniel Reich, 2021. "Using Machine Learning to Improve Public Reporting on U.S. Government Contracts," Interfaces, INFORMS, vol. 51(6), pages 463-479, November.

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