The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions
Open accessAbstract
Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third-party sellers’ products to consumers. These rankings decrease consumer search costs and increase the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging because rankings are endogenous: consumers pay more attention to highly ranked products, and intermediaries rank the most relevant products at the top. In this paper, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to make three contributions. First, I identify the causal effect of rankings and show that they affect what consumers search, but conditional on search, do not affect purchases. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $1.92, which is lower than literature estimates obtained without experimental variation. I also use model predictions, data patterns, and a feature of the data set (opaque offers) to show rankings lower search costs, instead of affecting consumer expectations or utility. Finally, I show a utility-based ranking built on this model’s estimates benefits consumers and the search intermediary. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1072 .
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Claims the paper makes
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?
In the citing paper
“For example, previous research has found that online search engines lower the cost of locating information without affecting expectations or utility for consumers [25,26].”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [26]
In the citing paper
“The position coefficient on inspection is negative and significant for all LLMs, ranging from -0.0002 to -0.0005 (all ps<0.001;Table 2, left panel), whereas the human benchmark is -0.0019 (p<0.001).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“AI agents search more deeply than humans and never decline to buy.”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“For Flash and Pro, it is indistinguishable from zero (-0.000014, p=0.338 and -0.000008, p=0.606, respectively).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“Search depth is marginally lower ( M Alternative = 2.92 vs. M Original = 3.12 ) but remains far above the human benchmark ( M Human = 1.12 ).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“whereas the human benchmark is -0.0019 (p<0.001).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“The designs differ in list length: our page shows 100 listings, while impressions in the human benchmark average roughly seven, so the per-rank slope is estimated over a much longer span in our data.”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“Research shows that with hotel search, humans make on average 1.12 clicks and 93% of sessions contain only one click (Ursu 2018).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“The canonical explanation is that ranking determines which alternatives enter the consideration set while leaving the consumer's valuation of an inspected alternative untouched (Ursu 2018).”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“For human consumers, an effect on the consideration set is an effect on demand, because the consideration set mostly holds one alternative.”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
In the citing paper
“human consumers who click at least once book 66% of the time and take the outside option 34% of the time”
From Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf (2026) · cites this paper as Ursu 2018
Citations
Cites
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An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets
Ghose, A., & Yang, S. (2009) · Management Science · doi:10.1287/mnsc.1090.1054 · Cited by 684
Ghose, A., Ipeirotis, P. G., & Li, B. (2012) · Marketing Science · doi:10.1287/mksc.1110.0700 · Cited by 649
Ansari, A., & Mela, C. F. (2003) · Journal of Marketing Research · doi:10.1509/jmkr.40.2.131.19224 · Cited by 639
Hortaçsu, A., & Syverson, C. (2004) · The Quarterly Journal of Economics · doi:10.1162/0033553041382184 · Cited by 593
Research works printed but not linked: 3
- Athey S, Imbens G (2015) Machine learning methods for estimating heterogeneous causal effects. Working paper, Stanford University, Palo Alto, CA, http://arxiv.org/pdf/1504.01132v1.pdf. · arXiv
- Chen Y, 2016, Management Sci., 34, 606
- Hausman J, 1996, The Economics of New Goods, Studies in Income and Wealth, 207
Cited by
Sorted by citations
Chen, Y., & Yao, S. (2012) · Management Science · doi:10.1287/mnsc.2016.2557 · Cited by 179
Search Personalization Using Machine Learning
Yoganarasimhan, H. (2019) · Management Science · doi:10.1287/mnsc.2018.3255 · Cited by 171
Zhang, J., Adomavičius, G., Gupta, A., & Ketter, W. (2020) · Information Systems Research · doi:10.1287/isre.2019.0876 · Cited by 132
Armstrong, M. (2017) · Journal of the European Economic Association · doi:10.1093/jeea/jvx017 · Cited by 117
Online Reviews: Star Ratings, Position Effects and Purchase Likelihood
Vana, P., & Lambrecht, A. (2018) · Marketing Science · doi:10.1287/mksc.2020.1278 · Cited by 115
Metadata
- Authors
- Ursu, R. M.
- Affiliations
- New York University (US)
- Corresponding author
- Ursu, Raluca M.
- ORCID
- Ursu, Raluca M.
- Venue
- Marketing Science
- Published
- 2018-08
- Volume
- 37
- Issue
- 4
- Pages
- 530-552
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W3124312273
- Cited by
- 328
- References
- 39
- Citations by year
- 2026: 47 · 2025: 53 · 2024: 57 · 2023: 35 · 2022: 44 · 2021: 35 · 2020: 26 · 2019: 11 · 2018: 12
- FWCI
- 37.64
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- Locations
- Marketing Science (publishedVersion)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- University of Chicago
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- search engine ranking, consumer search behavior, purchase decision, position effect, search costs, consumer welfare, Expedia, third-party sellers
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