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Testing Competitive Market Structures

Author

Listed:
  • Glen L. Urban

    (Massachusetts Institute of Technology)

  • Philip L. Johnson

    (Management Decision Systems, Inc.)

  • John R. Hauser

    (Massachusetts Institute of Technology)

Abstract

An accurate understanding of the structure of competition is important in the formulation of many marketing strategies. For example, in new product launch, product reformulation, or positioning decisions, the strategist wants to know which of his competitors will be most affected and hence most likely to respond. Many marketing science models have been proposed to identify market structure. In this paper we examine the managerial problem and propose a criterion by which to judge an identified market structure. Basically, our criterion is a quantification of the intuitive managerial criterion that a “submarket” is a useful conceptualization if it identifies which products are most likely to be affected by “our” marketing strategies. We formalize this criterion within the structure of classical hypothesis testing so that a marketing scientist can use statistical statements to evaluate a market structure identified by: (1) behavioral hypotheses, (2) managerial intuition, or (3) market structure identification algorithms. Mathematically, our criterion is based on probabilities of switching to products in the situation where an individual's most preferred product is not available. ‘Submarkets' are said to exist when consumers are statistically more likely to buy again in that ‘submarket' than would be predicted based on an aggregate “constant ratio” model. For example, product attributes (e.g., brand, form, size), use situations (e.g., coffee in the morning versus coffee at dinner), and user characteristics (e.g., heavy versus light users) are specified as hypotheses for testing alternate competitive structures. Measurement and estimation procedures are described and a convergent approach is illustrated. An application of the methodology to the coffee market is presented and managerial implications of six other applications are described briefly.

Suggested Citation

  • Glen L. Urban & Philip L. Johnson & John R. Hauser, 1984. "Testing Competitive Market Structures," Marketing Science, INFORMS, vol. 3(2), pages 83-112.
  • Handle: RePEc:inm:ormksc:v:3:y:1984:i:2:p:83-112
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    File URL: http://dx.doi.org/10.1287/mksc.3.2.83
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    Citations

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    Cited by:

    1. Ravi Anupindi & Maqbool Dada & Sachin Gupta, 1998. "Estimation of Consumer Demand with Stock-Out Based Substitution: An Application to Vending Machine Products," Marketing Science, INFORMS, vol. 17(4), pages 406-423.
    2. Park, Namgyoo K. & Cho, Dong-Sung, 1997. "The effect of strategic alliance on performance," Journal of Air Transport Management, Elsevier, vol. 3(3), pages 155-164.
    3. Sri Duvvuri & Thomas Gruca, 2010. "A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities Across Categories," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 558-578, September.
    4. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
    5. Siebert Ralph B, 2010. "Learning-by-Doing and Cannibalization Effects at Multi-Vintage Firms: Evidence from the Semiconductor Industry," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-32, May.
    6. repec:kap:jculte:v:42:y:2018:i:1:d:10.1007_s10824-016-9276-7 is not listed on IDEAS
    7. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    8. Nobuhiko Terui & Masataka Ban & Toshihiko Maki, 2010. "Finding market structure by sales count dynamics—Multivariate structural time series models with hierarchical structure for count data—," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 91-107, February.
    9. Peter M. Guadagni & John D. C. Little, 2008. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 27(1), pages 29-48, 01-02.
    10. Urban, Glen L. & Hulland, John S. & Weinberg, Bruce., 1990. "Modeling, categorization, elimination, and consideration for new product forecasting of consumer durables," Working papers 3206-90., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    11. A. Gürhan Kök & Yi Xu, 2011. "Optimal and Competitive Assortments with Endogenous Pricing Under Hierarchical Consumer Choice Models," Management Science, INFORMS, vol. 57(9), pages 1546-1563, February.
    12. Kannan, P. K. & Yim, Chi Kin (Bennett), 2001. "An investigation of the impact of promotions on across-submarket competition," Journal of Business Research, Elsevier, vol. 53(3), pages 137-149, September.
    13. Krishnamurthi, Lakshman & Raj, S. P. & Sivakumar, K., 1995. "Unique inter-brand effects of price on brand choice," Journal of Business Research, Elsevier, vol. 34(1), pages 47-56, September.
    14. Kamalini Ramdas & Mohanbir S. Sawhney, 2001. "A Cross-Functional Approach to Evaluating Multiple Line Extensions for Assembled Products," Management Science, INFORMS, vol. 47(1), pages 22-36, January.
    15. Park, Sehoon & Jain, Dipak & Krishnamurthi, Lakshman, 1998. "A hierarchical elimination modeling approach for market structure analysis," European Journal of Operational Research, Elsevier, vol. 111(2), pages 328-350, December.
    16. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.

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