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A Model of Search Intermediaries and Paid Referrals

Author

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  • Thomas A. Weber

    (Department of Management Science and Engineering, Terman Engineering Center, Stanford University, Stanford, California 94305)

  • Zhiqiang (Eric) Zheng

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

Abstract

In this paper we pursue three main objectives: (1) to develop a model of an intermediated search market in which matching between consumers and firms takes place primarily via paid referrals; (2) to address the question of designing a suitable mechanism for selling referrals to firms; and (3) to characterize and analyze the firms' bidding strategies given consumers' equilibrium search behavior. To achieve these objectives we develop a two-stage model of search intermediaries in a vertically differentiated product market. In the first stage an intermediary chooses a search engine design that specifies to which extent a firm's search rank is determined by its bid and to which extent it is determined by the product offering's performance. In the second stage, based on the search engine design, competing firms place their open bids to be paid for each referral by the search engine. We find that the revenue-maximizing search engine design bases rankings on a weighted average of product performance and bid amount. Nonzero pure-strategy equilibria of the underlying discontinuous bidding game generally exist but are not robust with respect to noisy clicks in the system. We determine a unique nondegenerate mixed-strategy Nash equilibrium that is robust to noisy clicks. In this equilibrium firms of low product performance fully dissipate their rents, which are appropriated by the search intermediary and the firm with the better product. The firms' expected bid amounts are generally nonmonotonic in product performance and depend on the search engine design parameter. The intermediary's profit-maximizing design choice, by attributing a positive weight to the firms' bids, tends to obfuscate search results and reduce overall consumer surplus compared to the socially optimal design of fully transparent results ranked purely on product performance.

Suggested Citation

  • Thomas A. Weber & Zhiqiang (Eric) Zheng, 2007. "A Model of Search Intermediaries and Paid Referrals," Information Systems Research, INFORMS, vol. 18(4), pages 414-436, December.
  • Handle: RePEc:inm:orisre:v:18:y:2007:i:4:p:414-436
    DOI: 10.1287/isre.1070.0139
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    4. Ashish Agarwal & Kartik Hosanagar & Michael D. Smith, 2015. "Do Organic Results Help or Hurt Sponsored Search Performance?," Information Systems Research, INFORMS, vol. 26(4), pages 695-713, December.
    5. Xiaomeng Du & Meng Su & Xiaoquan (Michael) Zhang & Xiaona Zheng, 2017. "Bidding for Multiple Keywords in Sponsored Search Advertising: Keyword Categories and Match Types," Information Systems Research, INFORMS, vol. 28(4), pages 711-722, December.
    6. Yi-Jen (Ian) Ho & Sanjeev Dewan & Yi-Chun (Chad) Ho, 2020. "Distance and Local Competition in Mobile Geofencing," Information Systems Research, INFORMS, vol. 31(4), pages 1421-1442, December.
    7. Juan Feng & Jinhong Xie, 2012. "Research Note ---Performance-Based Advertising: Advertising as Signals of Product Quality," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 1030-1041, September.
    8. Alvin Chung Man Leung & Ashish Agarwal & Prabhudev Konana & Alok Kumar, 2017. "Network Analysis of Search Dynamics: The Case of Stock Habitats," Management Science, INFORMS, vol. 63(8), pages 2667-2687, August.
    9. Karthik Kannan & Rajib L. Saha & Warut Khern-am-nuai, 2022. "Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms," Information Systems Research, INFORMS, vol. 33(2), pages 464-475, June.
    10. De Liu & Jianqing Chen & Andrew B. Whinston, 2010. "Ex Ante Information and the Design of Keyword Auctions," Information Systems Research, INFORMS, vol. 21(1), pages 133-153, March.
    11. Park, Chang Hee & Agarwal, Manoj K., 2018. "The order effect of advertisers on consumer search behavior in sponsored search markets," Journal of Business Research, Elsevier, vol. 84(C), pages 24-33.
    12. Animesh Animesh & Vandana Ramachandran & Siva Viswanathan, 2010. "Research Note ---Quality Uncertainty and the Performance of Online Sponsored Search Markets: An Empirical Investigation," Information Systems Research, INFORMS, vol. 21(1), pages 190-201, March.
    13. Vibhanshu Abhishek & Kartik Hosanagar & Peter S. Fader, 2015. "Aggregation Bias in Sponsored Search Data: The Curse and the Cure," Marketing Science, INFORMS, vol. 34(1), pages 59-77, January.
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