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Best-worst scaling approach to predict customer choice for 3PL services

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  • Coltman, Tim
  • Devinney, Timothy M.
  • Keating, Byron W.
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    Abstract

    This study describes a simple, theoretically based methodology to analyze the nature of customer demand for third-party logistics provider service components. The method overcomes limitations in prior studies and enables us to examine the relative importance of product and service attributes as they pertain to the choice of third-party logistics providers. Two distinct types of customers populate our data: those professing operational attributes and those seeking relational attributes. The theoretical and practical implications are that improved supply chain models can be developed when separate demand structures are taken into account.

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    File URL: http://mpra.ub.uni-muenchen.de/40492/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 40492.

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    Date of creation: 2010
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    Publication status: Published in Journal of Business Logistics 2.32(2011): pp. 139-152
    Handle: RePEc:pra:mprapa:40492

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    Related research

    Keywords: best–worst scaling; customer demand structures; third-party logistics;

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    References

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    1. Kim, Changsu & Yang, Kyung Hoon & Kim, Jaekyung, 2008. "A strategy for third-party logistics systems: A case analysis using the blue ocean strategy," Omega, Elsevier, vol. 36(4), pages 522-534, August.
    2. Danielis, Romeo & Marcucci, Edoardo & Rotaris, Lucia, 2005. "Logistics managers' stated preferences for freight service attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(3), pages 201-215, May.
    3. Peter J Buckley & Timothy M Devinney & Jordan J Louviere, 2007. "Do managers behave the way theory suggests? A choice-theoretic examination of foreign direct investment location decision-making," Journal of International Business Studies, Palgrave Macmillan, vol. 38(7), pages 1069-1094, December.
    4. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer, vol. 12(1), pages 21-55, March.
    5. Jarl Kampen & Marc Swyngedouw, 2000. "The Ordinal Controversy Revisited," Quality & Quantity: International Journal of Methodology, Springer, vol. 34(1), pages 87-102, February.
    6. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
    7. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Empirical test of a constrained choice discrete model: Mode choice in São Paulo, Brazil," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 103-115, April.
    8. Olhager, Jan & Selldin, Erik, 2004. "Supply chain management survey of Swedish manufacturing firms," International Journal of Production Economics, Elsevier, vol. 89(3), pages 353-361, June.
    9. Marasco, Alessandra, 2008. "Third-party logistics: A literature review," International Journal of Production Economics, Elsevier, vol. 113(1), pages 127-147, May.
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    Cited by:
    1. Anderson, Edward & Coltman, Tim & Devinney, Timothy M. & Keating, Byron W., 2010. "What Drives the Choice of Third Party Logistics Provider?," MPRA Paper 40508, University Library of Munich, Germany.

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