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An alternative efficient representation of demand‐based competitive asymmetry

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  • Wayne S. DeSarbo
  • Rajdeep Grewal

Abstract

Competitive asymmetry is defined in terms of the directional level of competition among brands/firms (i.e., unit of analysis), where the degree to which brand/firm A may compete with brand/firm B does not equal the degree to which brand/firm B competes with brand/firm A. Such a market structure phenomenon is quite commonplace in virtually every market, e.g., where there exists distinct market leaders and followers. DeSarbo, Grewal, and Wind recently (2006) proposed a new spatial methodology in SMJ to assess these competitive asymmetries based on information on consumer choice sets (i.e., a demand‐based approach). However, the approach espoused by DeSarbo et al. results in as many competitive maps as there are brands/firms in a dataset. In this research, the authors devise a distance‐based unfolding multidimensional scaling procedure for deriving joint spaces of brands/firms both as givers and takers of consumer consideration with the objective to have a more efficient representation of competitive asymmetries (i.e., one map irrespective of the number of brands/firms under study). An application is provided for an actual commercial study undertaken by a major U.S. automobile manufacturer examining the mid‐size car marketplace. The strategic implications of the results are detailed. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Wayne S. DeSarbo & Rajdeep Grewal, 2007. "An alternative efficient representation of demand‐based competitive asymmetry," Strategic Management Journal, Wiley Blackwell, vol. 28(7), pages 755-766, July.
  • Handle: RePEc:bla:stratm:v:28:y:2007:i:7:p:755-766
    DOI: 10.1002/smj.601
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    Cited by:

    1. Zhou, Meihua & Angelopoulos, Spyros & Ou, Carol & Liu, Hongwei & Liang, Zhouyang, 2023. "Optimization of dynamic product offerings on online marketplaces: A network theory perspective," Other publications TiSEM 75d71155-88bf-4ff7-aba1-9, Tilburg University, School of Economics and Management.
    2. Yang Qian & Yuanchun Jiang & Yanan Du & Jianshan Sun & Yezheng Liu, 2020. "Segmenting market structure from multi-channel clickstream data: a novel generative model," Electronic Commerce Research, Springer, vol. 20(3), pages 509-533, September.
    3. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.

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