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The Follower Competitive Location Problem with Comparison-Shopping

Author

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  • Vladimir Marianov

    (Pontificia Universidad Católica de Chile)

  • H. A. Eiselt

    (University of New Brunswick)

  • Armin Lüer-Villagra

    (Universidad Andres Bello)

Abstract

In competitive settings, firms locate their stores to take advantage of consumers’ behavior to maximize their market share. A common behavior is comparison-shopping: in this behavioral pattern, consumers visit multiple stores that sell non-identical products, which are mutual substitutes, before making their purchase decision. This behavior has never been included in location-prescribing models for competitive firms. Given existing branches of one firm, we address the location problem of a follower firm that locates its own branches. We present insights on the instance used by ReVelle in his maximum capture formulation, provide computational experience with one thousand 100-node instances, and consider a realistic case using a 353-node network of Santiago, Chile. The results are compared in terms of the demand captured by each firm and the locational patterns that result from different consumer behaviors.

Suggested Citation

  • Vladimir Marianov & H. A. Eiselt & Armin Lüer-Villagra, 2020. "The Follower Competitive Location Problem with Comparison-Shopping," Networks and Spatial Economics, Springer, vol. 20(2), pages 367-393, June.
    • Handle: RePEc:kap:netspa:v:20:y:2020:i:2:d:10.1007_s11067-019-09481-6
    DOI: 10.1007/s11067-019-09481-6
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    References listed on IDEAS

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    Cited by:

    1. Tammy Drezner & Morton O’Kelly & Zvi Drezner, 2023. "Multipurpose shopping trips and location," Annals of Operations Research, Springer, vol. 321(1), pages 191-208, February.
    2. H. A. Eiselt & Vladimir Marianov, 2020. "Stability of utility functions and apportionment rules in location models," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 772-792, October.
    3. Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.
    4. Méndez-Vogel, Gonzalo & Marianov, Vladimir & Lüer-Villagra, Armin & Eiselt, H.A., 2023. "Store location with multipurpose shopping trips and a new random utility customers’ choice model," European Journal of Operational Research, Elsevier, vol. 305(2), pages 708-721.

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