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Cited by 299Information Retrieval and Search BehaviorDigital Marketing and Social MediaConsumer Market Behavior and Pricing

The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.

Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

Open access
Ghose, Anindya, Ipeirotis, Panagiotis G., Li, Beibei·2014·Management Science
DOI 10.1287/mnsc.2013.1828
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Abstract

In this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. (3) Our randomized experiments also reveal that an “active” personalized ranking system (wherein users can interact with and customize the ranking algorithm) leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized ranking system (wherein users cannot interact with the ranking algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines. This paper was accepted by Lorin Hitt, information systems.

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Research works printed but not linked: 3

  1. 10.2307/3172866 · DOI · Paper page
  2. Greene W, 1999, Econometric Analysis
  3. Koulayev S, 2014, RAND J. Econom.

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Metadata

Authors
Ghose, A., Ipeirotis, P. G., & Li, B.
Affiliations
  • New York University (US)
  • Carnegie Mellon University (US)
ORCID
Venue
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2014-07
Volume
60
Issue
7
Pages
1632-1654
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
DOI
10.1287/mnsc.2013.1828
OpenAlex
W2136522867
Cited by
299
References
42
Citations by year
2026: 24 · 2025: 20 · 2024: 41 · 2023: 36 · 2022: 31 · 2021: 29 · 2020: 22 · 2019: 22 · 2018: 14 · 2017: 17 · 2016: 22 · 2015: 11 · 2014: 7
FWCI
52.36
Type
journal article
Language
en
Open access
Locations
  • Management Science (publishedVersion)
  • SSRN Electronic Journal (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
Keywords
search engine ranking, personalized ranking, search engine revenue, consumer behavior, product ratings, online travel, social media signals, information overload, purchase intention, click-through rate, ranking algorithms

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