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

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
Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1287/mnsc.2013.1828

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Buyers are more likely to select an item higher up on the search results page, implying some sort of search costs.

    “Indeed, buyers are more likely to select an item higher up on the search results page, implying some sort of search costs.”

    From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Ghose et al. 2014

  • The literature on search costs has demonstrated a correlation between ranking and purchase or click-through behavior.

    “The literature on search costs has demonstrated a correlation between ranking and purchase (or click-through) behavior (Ghose et al. 2014).”

    From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Ghose et al. 2014

  • Empirical and theoretical work shows that positions and rankings can causally shape what consumers examine and buy.

    “Empirical and theoretical work shows that positions and rankings can causally shape what consumers examine and buy.”

    From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [14]

  • Several patterns we document mirror directionally human behavior: ranking/position materially shape choices in digital markets, and platform endorsements/badges shift demand.

    “That said, several patterns we document mirror directionally human behavior-ranking/position materially shape choices in digital markets [14, 41], and platform endorsements/badges shift demand [29].”

    From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [14]

Cites

Flagged first, then by citations

2 of the 42 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

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 · 2013: 1 · 2012: 2
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 › Sociology and Political Science › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review