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

Author

Listed:
  • Coltman, Tim
  • Devinney, Timothy M.
  • Keating, Byron W.

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.

Suggested Citation

  • Coltman, Tim & Devinney, Timothy M. & Keating, Byron W., 2010. "Best-worst scaling approach to predict customer choice for 3PL services," MPRA Paper 40492, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:40492
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    File URL: https://mpra.ub.uni-muenchen.de/40492/1/MPRA_paper_40492.pdf
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    References listed on IDEAS

    as
    1. 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;Academy of International Business, vol. 38(7), pages 1069-1094, December.
    2. 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.
    3. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, March.
    4. Jarl Kampen & Marc Swyngedouw, 2000. "The Ordinal Controversy Revisited," Quality & Quantity: International Journal of Methodology, Springer, vol. 34(1), pages 87-102, February.
    5. 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.
    6. 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.
    7. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    8. 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.
    9. 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.
    10. 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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    Citations

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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.

    More about this item

    Keywords

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

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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