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How Does Ranking Affect User Choice in Online Search?

Author

Listed:
  • Mark Glick
  • Greg Richards
  • Margarita Sapozhnikov
  • Paul Seabright

Abstract

This paper investigates whether a search engine’s ordering of algorithmic results has an important effect on website traffic. A website’s ranking on a search engine results page is positively correlated with the clicks that it receives. This could result from the search engine’s accurately predicting the websites relevance to users. Or it could result from users merely clicking on the highest ranked links, regardless of the website’s relevance. Using a unique dataset, we find that a website’s rank, not just its relevance, strongly and significantly affects the likelihood of a click. We also find evidence that rank influences CTRs partly by controlling access to the scarce attention of users, but primarily by substituting the reputational capital of the search engine for the reputation of individual websites. Copyright The Author(s) 2014

Suggested Citation

  • Mark Glick & Greg Richards & Margarita Sapozhnikov & Paul Seabright, 2014. "How Does Ranking Affect User Choice in Online Search?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 45(2), pages 99-119, September.
    • Handle: RePEc:kap:revind:v:45:y:2014:i:2:p:99-119
    DOI: 10.1007/s11151-014-9435-y
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    References listed on IDEAS

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    1. Zhou, Jidong, 2009. "Prominence and Consumer Search: The Case With Multiple Prominent Firms," MPRA Paper 12554, University Library of Munich, Germany.
    2. Mark Armstrong & John Vickers & Jidong Zhou, 2009. "Prominence and consumer search," RAND Journal of Economics, RAND Corporation, vol. 40(2), pages 209-233, June.
    3. Mark Armstrong & Jidong Zhou, 2011. "Paying for Prominence," Economic Journal, Royal Economic Society, vol. 121(556), pages 368-395, November.
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    5. Baye, Michael R. & De los Santos, Babur & Wildenbeest, Matthijs R., 2016. "What’s in a name? Measuring prominence and its impact on organic traffic from search engines," Information Economics and Policy, Elsevier, vol. 34(C), pages 44-57.
    6. Kinshuk Jerath & Liye Ma & Young-Hoon Park & Kannan Srinivasan, 2011. "A "Position Paradox" in Sponsored Search Auctions," Marketing Science, INFORMS, vol. 30(4), pages 612-627, July.
    7. Anindya Ghose & Sha Yang, 2007. "An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets," Working Papers 07-35, NET Institute, revised Sep 2007.
    8. Xu, Yunjie (Calvin) & Kim, Hee-Woong, 2008. "Order Effect and Vendor Inspection in Online Comparison Shopping," Journal of Retailing, Elsevier, vol. 84(4), pages 477-486.
    9. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters, in: E-commerce, pages 541-558, National Bureau of Economic Research, Inc.
    10. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Lihi Dery & Dror Hermel & Artyom Jelnov, 2021. "Cheating in Ranking Systems," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 58(2), pages 303-320, March.
    2. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    3. Samir Mamadehussene, 2020. "The Interplay Between Obfuscation and Prominence in Price Comparison Platforms," Management Science, INFORMS, vol. 66(10), pages 4843-4862, October.
    4. Carsten D. Schultz, 2020. "The impact of ad positioning in search engine advertising: a multifaceted decision problem," Electronic Commerce Research, Springer, vol. 20(4), pages 945-968, December.

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    More about this item

    Keywords

    Internet search; Page rank; Click-through rates; Scarce attention; D03; D12; D83;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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