The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment
Open accessAbstract
We argue that reputation mechanisms used by platform markets suffer from two problems.First, buyers may draw conclusions about the quality of the platform from single transactions, causing a reputational externality across sellers.Second, for a variety of reasons we discuss, reputations will be biased.We document these problems using eBay data and claim that platforms can benefit from identifying and promoting higher quality sellers.We create an unobservable measure of seller quality and demonstrate the benefits of our approach through a controlled experiment that prioritizes better quality sellers.We highlight the importance of reputational externalities and chart an agenda that aims to create more realistic models of platform markets.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
It is more expensive to leave a negative review than a positive review because of seller retaliation and harassment.
“it is more “expensive” to leave a negative review than it is to leave a positive review because of seller retaliation and harassment.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Section 3, “Reputation and Transaction Quality at eBay” (page unavailable), says sellers historically retaliated against negative feedback and cites anecdotal cases of harassment. But it describes the higher cost of negative feedback conditionally, rather than establishing that it is generally more expensive to leave. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNosko & Tadelis show that a buyer who has a better experience on eBay (indicated by buying from a seller with a higher EPP score) is more likely to continue to transact on eBay again in the future.
“Nosko & Tadelis (2015) show that a buyer who has a better experience on eBay (indicated by buying from a seller with a higher EPP score) is more likely to continue to transact on eBay again in the future.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
In a controlled experiment on eBay, treated buyers exposed to higher EPP sellers were significantly more likely to return and purchase again on eBay compared to the control group.
“The results show that treated buyers who were exposed to higher EPP sellers were significantly more likely to return and purchase again on eBay compared to the control group of buyers.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Marketplace platforms can benefit from using data in ways that uncover better measures of seller quality.
“Hence, Nosko & Tadelis argue that marketplace platforms can benefit from using data in ways that uncover better measures of seller quality.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Using internal eBay data, Nosko & Tadelis establish that the percent positive measure has a mean of 99.3% and a median of 100%.
“Using internal eBay data, Nosko & Tadelis establish that the percent positive measure has a mean of 99.3% and a median of 100%.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
The median seller has a percent positive score of 100%.
“The median seller has a score of 100%.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Nosko & Tadelis's EPP measure has a mean of 64% and a median of 67%, and exhibits significantly more variability than the percent positive measure because there is significant variation in the choice of buyers to be silent across sellers.
“EPP has a mean of 64% and a median of 67%, and it exhibits significantly more variability than the percent positive measure because there is significant variation in the choice of buyers to be silent across sellers.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
The data show there are three times as many complaints to customer service as there are negative feedback scores, suggesting buyers avoid leaving negative feedback and prefer more costly actions of demonstrating dissatisfaction.
“the data show that there are three times as many complaints to customer service as there are negative feedback scores, suggesting that buyers avoid leaving negative feedback and prefer instead to pursue more costly actions of demonstrating their dissatisfaction.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Nosko & Tadelis (2015) directly show how biased reputation measures really are.
“Nosko & Tadelis (2015) expand on Dellarocas & Wood (2008) and directly show how biased reputation measures really are.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Giving negative feedback is more costly than giving positive feedback due to retaliation.
“Horton & Golden (2015) contend that giving negative feedback is more costly than giving positive feedback due to retaliation.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
As Nosko & Tadelis (2015) show, a score of 98% on eBay places a seller below the tenth percentile of the distribution.
“In reality, as Nosko & Tadelis (2015) show, a score of 98% on eBay places a seller below the tenth percentile of the distribution.”
From Reputation and Feedback Systems in Online Platform Markets (2016) · cites this paper as Nosko & Tadelis (2015)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue
Ghose, A., Ipeirotis, P. G., & Li, B. (2014) · Management Science · doi:10.1287/mnsc.2013.1828 · Cited by 299 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- Bajari, P. and Hortaçsu, A. (2004). Economic insights from internet auctions. Journal of Economic Literature, 42(2):457-486.
- Bar-Isaac, H. and Tadelis, S. (2008). Seller reputation. Foundations and Trends R in Microeconomics, 4(4):273-351.
- Bolton, G., Greiner, B., and Ockenfels, A. (2013). Engineering trust: reciprocity in the production of reputation information. Management Science, 59(2):265-285.
- Cabral, L. and Hortaçsu, A. (2010). The dynamics of seller reputation: Evidence from ebay*. The Journal of Industrial Economics, 58(1):54-78.
- Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management science, 49(10):1407-1424.
- Dellarocas, C. and Wood, C. A. (2008). The sound of silence in online feedback: Estimating trading risks in the presence of reporting bias. Management Science, 54(3):460-476.
- Greif, A. (1989). Reputation and coalitions in medieval trade: Evidence on the maghribi traders. The Journal of Economic History, 4(4):857-882.
- Mayzlin, D., Dover, Y., and Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8):2421-55.
- Rochet, J.-C. and Tirole, J. (2006). Two-sided markets: A progress report. The RAND Journal of Economics, 37(3):645-667.
- Tirole, J. (1996). A theory of collective reputations (with applications to the persistence of corruption and to firm quality). Review of Economic Studies, 63(1):1-22.
Cited by
Sorted by citations
Competing with complementors: An empirical look at Amazon.com
Zhu, F., & Liu, Q. (2018) · Strategic Management Journal · doi:10.1002/smj.2932 · Cited by 546 · see citing works
Reputation and Feedback Systems in Online Platform Markets
Tadelis, S. (2016) · Annual Review of Economics · doi:10.1146/annurev-economics-080315-015325 · Cited by 524 · see citing works
Self-citationEinav, L., Farronato, C., & Levin, J. (2016) · Annual Review of Economics · doi:10.1146/annurev-economics-080315-015334 · Cited by 553 · see citing works
Gig economy platforms: Boon or Bane?
(2019) · OECD Economics Department Working Papers · doi:10.1787/fdb0570b-en · Cited by 137 · see citing works
Whose Voice Do We Hear in the Marketplace? Evidence from Consumer Complaining Behavior
Raval, D. (2020) · Marketing Science · doi:10.1287/mksc.2018.1140 · Cited by 47 · see citing works
- Authors
- Nosko, C., & Tadelis, S.
- Affiliations
- University of Chicago (US)
- ORCID
- Tadelis, Steven
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2015-01
- OpenAlex
- W1557570185
- Cited by
- 229
- References
- 1
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 18 · 2023: 27 · 2022: 22 · 2021: 18 · 2020: 25 · 2019: 29 · 2018: 21 · 2017: 21 · 2016: 11 · 2015: 11 · 2014: 1 · 2012: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Keywords
- reputation mechanism, platform markets, seller quality, online marketplaces, eBay, platform quality
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
