The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index; 14 of the 82 sources its claims cite were checked for support.
Reputation and Feedback Systems in Online Platform Markets
Open accessAbstract
Online marketplaces have become ubiquitous, as sites such as eBay, Taobao, Uber, and Airbnb are frequented by billions of users. The success of these marketplaces is attributed to not only the ease in which buyers can find sellers, but also the trust that these marketplaces help facilitate through reputation and feedback systems. I begin by briefly describing the basic ideas surrounding the role of reputation in facilitating trust and trade, and offer an overview of how feedback and reputation systems work in online marketplaces. I then describe the literature that explores the effects of reputation and feedback systems on online marketplaces and highlight some of the problems of bias in feedback and reputation systems as they appear today. I discuss ways to address these problems to improve the practical design of online marketplaces and suggest some directions for future research.
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Claims the paper makes
51 No full text of the sources · 30 Own interpretation · 22 No basis given · 10 Sources not found · 8 Supported · 5 Partly supported · 2 Not checked yet · 2 Own result · 1 Supported so far · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Bolton et al. (2004) compare trading with feedback to trading without feedback and find that although feedback induces a substantial improvement in transaction efficiency (a lack of cheating and improved trust)”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
What the sources say
- Partly supportedcites Bolton et al. (2004) sourceExperimental — accuracy not yet measured
The Results (§4, page unavailable) explicitly report that access to reputation information substantially improves transaction efficiency, and §4.2 links feedback to trust and trustworthy behavior. The parenthetical goes further: the paper does not say feedback eliminates cheating, and §4.2 describes sellers who did not ship orders in the feedback market. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The median seller has a score of 100%.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction states that percent positive (PP) has a median of 100% in the authors’ dataset. Section 3 likewise says the median seller’s score is 100% while discussing the distribution of PP, so the cited sentence refers to PP rather than the separate feedback score. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Hence, Nosko & Tadelis argue that marketplace platforms can benefit from using data in ways that uncover better measures of seller quality.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction says the authors use eBay buyer-behavior data to create a new measure of seller quality, then use that measure to prioritize good sellers in search results. It reports that this approach increased transaction quality and buyer retention, supporting the paraphrase. (LLM, conf=0.93)
plainconditional- Asserted: passes — The authors put it forward as true.
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- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“it is more “expensive” to leave a negative review than it is to leave a positive review because of seller retaliation and harassment.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Bolton et al. (2013) source
- Partly supportedcites Nosko & Tadelis (2015) sourceExperimental — accuracy not yet measured
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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“it also exhibits a public goods problem in that participants understand that the benefits of trust and trustworthy behavior are enjoyed by everyone and are therefore not completely internalized.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
What the sources say
- Partly supportedcites Bolton et al. (2004) sourceExperimental — accuracy not yet measured
Section 4.3 explains that, in feedback markets, the benefits of trust and trustworthiness extend to other traders and are not fully internalized, unlike in partner relationships. The Conclusions report that participants responded to strategic incentives, but the paper does not establish that they understood this particular public-goods mechanism. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“They estimate an 8% price premium to having 2,000 positive and 1 negative feedback over a reputation of 10 positive and no negative feedbacks.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
What the sources say
- Partly supportedcites Resnick et al. (2006) sourceExperimental — accuracy not yet measured
The Discussion reports a 7.6% price difference between an established seller with more than 2,000 positive comments and one negative comment and new seller IDs. But the Procedures say the new IDs began with zero feedback and accumulated positives during the experiment; they did not all have a fixed reputation of 10 positives. The roughly 8% premium is supported, but the stated comparator is too specific. (LLM, conf=0.88)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Nosko & Tadelis (2015) expand on Dellarocas & Wood (2008) and directly show how biased reputation measures really are.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction reports that eBay sellers’ conventional percent-positive reputation measure averaged 99.3%, while the authors’ alternative quality measure averaged 64%. It also identifies Dellarocas and Wood (2008) as the closest prior work and says this paper focuses on how biased observable reputation measures are relative to seller quality. The Discussion states that the authors demonstrated bias in eBay reputation feedback. (LLM, conf=0.94)
- Not matchedcites Dellarocas & Wood (2008)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction reports a controlled eBay experiment that promoted higher-EPP sellers in treated buyers’ search results. It explicitly states that treated buyers exposed to higher-EPP sellers were significantly more likely to return and purchase again than control buyers. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Silence is bad news.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction explicitly says that a buyer with a better, higher-EPP experience is more likely to transact on eBay again and says the authors confirm this with data. Section 5.1 reports that buying from a higher-EPP seller increases the probability of another purchase within 180 days. (LLM, conf=0.95)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Silence is bad news.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In reality, as Nosko & Tadelis (2015) show, a score of 98% on eBay places a seller below the tenth percentile of the distribution.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
Section 3, “Reputation and Transaction Quality at eBay,” states directly that a 98% score places a seller below the tenth percentile of the distribution. The same section describes 98% as the tenth-percentile point on its histogram of eBay sellers’ percent-positive feedback scores. (LLM, conf=0.95)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Supported
- It may state two separate claims as one. 45
- Probably not a substantial claim. 22
Every source the paper cites for this claim was checked and supports it.
In the paper
“Using internal eBay data, Nosko & Tadelis establish that the percent positive measure has a mean of 99.3% and a median of 100%.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction states that, in the authors’ dataset, sellers’ percent positive (PP) measure has a mean of 99.3% and a median of 100%. This directly matches the attributed figures. (LLM, conf=0.95)
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
Section 3, “Reputation and Transaction Quality at eBay” (page not provided), reports negative feedback on 0.39% of transactions versus dispute tickets on over 1%. It says opening a dispute takes substantially more effort and concludes that many bad transactions received no negative feedback. The claim’s threefold comparison is an approximation of those figures. (LLM, conf=0.88)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Supportedcites Nosko & Tadelis (2015) source
The Introduction reports an EPP mean of 64% and median of 67% and says EPP offers much more variability than percent positive. Section 3 describes substantial spread in EPP and illustrates how sellers with identical percent-positive scores can have different EPP scores because their numbers of silent transactions differ. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
- The authors state this as an assumption: Silence is bad news.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“If δ becomes infinitesimally small, then for the buyer to trust the seller it must be that p ≥ 0.6 because it effectively becomes a one-stage game for the buyer.If, however, the buyer is extremely patient so that δ approaches 1, then for the buyer to trust the seller it must be that p ≥foot_3 7 .”
§ Reputation and Feedback Systems in Online Platform Markets > 1 -p p
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Today, approximately 65% of buyers leave feedback on eBay, a very high fraction, and an even higher fraction of more than 80% left feedback during the earlier days of eBay.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.1. Some Basic Features
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is largely understood today that eBay's success was not due to only the relative simplicity and transparency of its auction format, but also a brilliant innovation introduced first by eBay and copied later in one form or another by practically every other marketplace: the use of a feedback and reputation mechanism.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This fear of retaliation was most likely a central reason almost all buyers left positive feedback on eBay, which in turn caused eBay to switch from the two-sided reputation system to a one-sided reputation system.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.2. One-Sided Versus Two-Sided Feedback
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Indeed, feedback and reputation systems are central to the operations of every ecommerce marketplace, and their heritage can be traced, in part, to the ancient ancestor institutions of the physical marketplaces of the Middle Ages.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many online platforms offer at least one side of the market the ability to make choices that depend on the reputation of the other side of the market.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.1. Some Basic Features
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike the simple positive/negative/neutral feedback system in which the identities of buyers are disclosed, the DSRs are anonymous and aggregated so that the seller cannot infer who left them.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.1. Some Basic Features
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For a marketplace to flourish, therefore, it is necessary that both sides of the market trust each other, and for that, they need to have safeguards that alleviate the problems caused by asymmetric information.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Every transaction requires some level of trust between the buyer and seller, usually with the institutional support of law or some other enforcement mechanism.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike a physical transaction in a store, where the buyer can touch and feel the good she is buying, this close contact is absent in ecommerce and the buyer may not be able to verify the seller's identity.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Bolton et al. (2013) used data from eBay during the period when the reputation system was two-sided and convincingly showed that sellers wait to get feedback before giving feedback to buyers.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.2. One-Sided Versus Two-Sided Feedback
What the sources say
- Not checked — no full text of the sourcecites Bolton et al. (2013) source
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“First, sellers with better reputations should attract more potential buyers and obtain higher prices for their goods and services.”
§ Reputation and Feedback Systems in Online Platform Markets > 1 -p p
What the sources say
- Not checked — no full text of the sourcecites Bar-Isaac & Tadelis 2008 source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In fact, Airbnb even verifies the identity of all parties given the high stakes involved.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.2. One-Sided Versus Two-Sided Feedback
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“That the median reputation score of the sample is 21 points means that moving from no reputation at all to the median increases price by less than $1, a negligible impact when considering a median price of $275.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites McDonald & Slawson (2002) source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Second, as sellers' reputations get better (or worse), their economic returns and growth will also get better (or worse).”
§ Reputation and Feedback Systems in Online Platform Markets > 1 -p p
What the sources say
- Not checked — no full text of the sourcecites Bar-Isaac & Tadelis 2008 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, sellers' negative feedback scores were primarily retaliatory, which in turn made it risky for buyers to leave negative feedback (a point to which I return in Section 5 below).”
§ Reputation and Feedback Systems in Online Platform Markets > 3.2. One-Sided Versus Two-Sided Feedback
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A buyer can of course choose to leave no feedback at all, which is what a standard model of selfish behavior would predict because leaving feedback supplies a public good, for which the buyer receives no payoff except for some internally motivated moral satisfaction.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.1. Some Basic Features
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Their results suggest that eBay's reputation score is positively correlated with both the closing price and the number of bids.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites McDonald & Slawson (2002) source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Second, quality uncertainty may be a result of hidden actions that determine the quality of the good or service, what is often referred to as moral hazard.”
§ Reputation and Feedback Systems in Online Platform Markets > 1. INTRODUCTION
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Multiple review targets may create an inference problem that confuses the seller's quality of executing the sale and the quality of the product.”
§ Reputation and Feedback Systems in Online Platform Markets > 3.1. Some Basic Features
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Naturally, it is all but impossible to verify the quality of ungraded cards online, implying that buyers are exposed to potential moral hazard by sellers who may exaggerate card quality to receive higher payments.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“one shortcoming of observational studies that every applied economist is aware of is the possible endogeneity concern that results from potential selection or omitted variable biases.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“In comparison, moving from any two subsequent quartiles has no significant effect on sale price, consistent with the article's theoretical prediction.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Livingston (2005) source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Surprisingly, interactions between rating and claims are insignificant, suggesting that buyers prefer higher-rated sellers but are willing still to trade with lower-rated sellers and pay more if the claimed quality is high enough.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Although prices appear to be lower for reputable sellers, they do enjoy more listings and higher success rates, in line with Cabral & Hortac ¸su's (2010) results.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not matchedcites Cai et al. (2014)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Cabral & Hortac ¸su (2010) source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“A second result shows that centralized feedback leads sellers with higher cumulative success rates to sell more products in more regions, suggesting that centralization facilitates the expansion of reputable sellers into new markets.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not matchedcites Cai et al. (2014)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“On average, a card with a claimed quality of 10 is sold for 28.6% more than a card with a claimed quality of 9 and 75.5% more than a card with a claimed quality below 9.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The lab results suggest that higher reporting costs decrease buyers' willingness to provide reviews on sellers, leading to a decrease in buyers' trust and sellers' trustworthiness, but these results are not statistically significant.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Not checked — no full text of the sourcecites Li & Xiao (2014) source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The results therefore suggest that the effect is mainly one of reducing moral hazard and not causing a change in the composition of sellers due to reducing adverse selection.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
What the sources say
- Not matchedcites Klein et al. (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a weakness of this approach is that the laboratory may simulate a situation that is far from the workings of a real-world marketplace.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.2. Exploiting Exogenous Variation
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“he finds a positive effect of reputation on the probabilities of receiving a bid and of a sale happening, as well as on the sale price, especially for the first positive reviews.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Livingston (2005) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It will be in the strategic interest of these hotels to inflate their own ratings with fake positive reviews and simultaneously impact negatively the ratings of their competitors by submitting fake negative reviews of them.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Mayzlin et al. (2014) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The data show that claims of quality for ungraded cards seem suspiciously high, both when observing their distribution and when considering sellers' expected payoffs.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“however, ratings do not affect the price paid for the card, consistent with Livingston (2005) regarding the reputation premium.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“but the previous literature suggests that the effect on prices is small.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A growing literature has shown that user-generated feedback mechanisms are often biased and can be prone to influence by sellers.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“One result demonstrates that an increase in the cumulative success rate of a seller is correlated with a larger fraction of repeat buyers, but this effect weakens after feedback centralization.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not matchedcites Cai et al. (2014)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Additionally, given the research design lacks a fear of retaliation, reports are negatively biased.”
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
What the sources say
- Not checked — no full text of the sourcecites Li & Xiao (2014) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This may explain why buyers are more willing to trade with reputable sellers but are not willing to pay more.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Moreover, reputable sellers are less likely to default or provide counterfeit cards.”
§ Reputation and Feedback Systems in Online Platform Markets > 4.1. Observational Studies
What the sources say
- Not checked — no full text of the sourcecites Jin & Kato (2006) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Indeed, buyers are more likely to select an item higher up on the search results page, implying some sort of search costs.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
What the sources say
- Not checked — no full text of the sourcecites Ghose et al. 2014 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On one hand, the huge growth of online marketplaces suggests that feedback and reputation systems do a good job of policing bad actors and minimizing fraudulent behavior, possibly eliminating the need for onerous rules and regulations.”
§ Reputation and Feedback Systems in Online Platform Markets > 7. CONCLUDING REMARKS
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The rise of online marketplaces and their penetration into practically every household is, to a large extent, attributable to the success of these reputation and feedback systems.”
§ Reputation and Feedback Systems in Online Platform Markets > 7. CONCLUDING REMARKS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This implies that platforms can influence a buyer's consideration set of sellers by manipulating the set of sellers that the buyer sees, as well as the order in which they are listed.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In one experiment they offer users a coupon to leave feedback and show that the users who did reported more negative experiences than reviewers in the control group, suggesting that otherwise they would have probably been silent.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Fradkin et al. (2015) source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Zervas et al. (2015) demonstrate that grade inflation is also severe on Airbnb (where ratings are overwhelmingly positive), averaged at 4.7 out of 5 stars with 94% of property ratings with 4.5 or 5 stars.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Zervas et al. (2015) source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Moreover, in 2007, 28% of contracts ended with a feedback score of less than four stars, whereas in 2014 only 9% of transactions had these lower ratings.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not matchedcites Horton & Golden (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The role of reputation and feedback systems is to promote trust and trustworthiness in online marketplaces so as to reduce frictions caused by asymmetric information, and in turn increase the efficiency of these markets.”
§ Reputation and Feedback Systems in Online Platform Markets > 7. CONCLUDING REMARKS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“on Airbnb (where ratings are overwhelmingly positive), averaged at 4.7 out of 5 stars with 94% of property ratings with 4.5 or 5 stars.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This information could also be used to rank sellers by quality and manipulate the consideration sets of buyers.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In a second experiment they disable retaliation in reviews, similar to what eBay did in 2008, and find that retaliation (or rewards for positive feedback) cause a bias but that the magnitude of this bias is smaller than that caused by a lack of incentives to leave truthful feedback.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Fradkin et al. (2015) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“They further argue that what constitutes bad feedback depends on the market penalty associated with that bad feedback.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not matchedcites Horton & Golden (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They show that TripAdvisor ratings are also relatively positive, but to a lesser degree, with an average of 3.8 out of 5 stars.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The results in Mayzlin et al. (2014) indeed show greater bunching at the extreme ratings for hotels on the sites where posting reviews is cheaper, and this is exacerbated by local competition (more local hotels).”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not checked — no full text of the sourcecites Mayzlin et al. (2014) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Still, user-generated feedback will continue to be an important input into the myriad of metrics that marketplaces can and will use to match their buyers with high-quality sellers in order to generate good experiences.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Xu et al. (2015) document and explain the rise of a centralized marketplace for fake reputations used by sellers on the Alibaba marketplace in China.”
§ Reputation and Feedback Systems in Online Platform Markets > Weighted by transaction volume
What the sources say
- Not matchedcites Xu et al. (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The authors state this as an assumption.
In the trade game, the buyer must send payment to the seller before the seller ships.
“Assume that to transact the buyer needs to send payment to the seller first”
The authors state this as an assumption.
The seller is either an honest seller who always ships or an opportunistic seller who maximizes expected payoff.
“Now imagine that the seller can be one of two types: an honest seller or an opportunistic seller.An honest seller will always ship the item, whereas an opportunistic seller will maximize his expected payoff.”
The authors state this as an assumption.
The buyer does not know the seller type but knows the probability p that the seller is honest.
“The buyer does not know the seller type but does know that a seller is honest with probability p ∈ (0, 1).”
The authors state this as an assumption.
The seller discounts future payoffs at a discount factor δ ∈ (0,1).
“Imagine that the seller discounts future payoffs at a discount factor of δ ∈ (0, 1).”
The authors state this as an assumption.
The buyer uses the same discount factor δ as the seller.
“Assuming that the buyer too uses δ as her discount factor”
The authors state this as an assumption.
In the two-stage game, the buyer believes the game will be played twice and an opportunistic seller will abuse trust always.
“Imagine, for example, that the buyer believes that this trading game will be played twice and that an opportunistic seller will abuse trust always”
The authors state this as an assumption.
The frequency of a seller's feedback is a good proxy for the frequency of actual transactions.
“First, they assume that the frequency of a seller's feedback is a good proxy for the frequency of actual transactions”
The authors state this as an assumption.
The nature of feedback correlates with buyer satisfaction.
“second, they assume that the nature of feedback correlates with buyer satisfaction.”
The authors state this as an assumption.
The mapping between seller behavior and DSRs was not affected by the change to one-sided feedback.
“their key assumption that the mapping between seller behavior and DSRs was not affected by the change to one-sided feedback.”
The authors state this as an assumption.
Reputation is two-sided.
“based on several assumptions, most notably that reputation is two-sided”
The authors state this as an assumption.
Silence is bad news.
“The tenet of their approach is that silence is bad news.”
The paper relies on this without support.
The economic literature assumes that market participants understand the equilibrium they are playing and correctly infer information from signals and actions.
“The economic literature takes the view that market participants understand the equilibrium they are playing and correctly infer information from signals and actions.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This is highlighted by the puzzlement of economists that find evaluative infrastructures to be capable of sustaining markets despite being plagued by factual inaccuracies, misleading claims, and unrepresentative assessments (Tadelis, 2016;Resnick, et al., 2006).”
From Evaluative infrastructures: Accounting for platform organization (2017) · cites this paper as Tadelis, 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Indeed, as previous research has shown, ''good'' on-site reputation is an effective trust signal in online transactions (Dellarocas et al. 2009; Qiu et al. 2018; Tadelis 2016).”
From In Stars We Trust – A Note on Reputation Portability Between Digital Platforms (2022) · cites this paper as Tadelis 2016
The cited source says what the claim attributes to it.
In the citing paper
“Even for established complementors (i.e., those who already have an on-site rating at their disposal), higher trustworthiness enables them to attract more demand and/or realize price premiums (Tadelis 2016).”
From In Stars We Trust – A Note on Reputation Portability Between Digital Platforms (2022) · cites this paper as Tadelis 2016
The cited source says what the claim attributes to it.
In the citing paper
“For example, the introduction of unique agent identifiers (Chan et al., 2024b) would enable the construction of reputation systems, which are critically important in otherwise (pseudo-)anonymous interactions such as online marketplaces (Tadelis, 2016).”
From Multi-Agent Risks from Advanced AI (2025) · cites this paper as Tadelis, 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Reputation and Feedback Systems in Online Platform Markets > 5. BIASES IN ONLINE FEEDBACK SYSTEMS
“As shown, the x-axis starts at 98%, which is the tenth percentile.”
§ Reputation and Feedback Systems in Online Platform Markets > 6. ONLINE FEEDBACK AND MARKET DESIGN
“a score of 98% on eBay places a seller below the tenth percentile of the distribution.”
Citations
Cites
The Market for "Lemons": Quality Uncertainty and the Market Mechanism
Akerlof, G. A. (1970) · The Quarterly Journal of Economics · doi:10.2307/1879431 · Cited by 23,016
The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms
Dellarocas, C. N. (2003) · Management Science · doi:10.1287/mnsc.49.10.1407.17308 · Cited by 3,583
Institutions and the Path to the Modern Economy
Greif, A. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511791307 · Cited by 2,877
Rational cooperation in the finitely repeated prisoners' dilemma
Kreps, D. M., Milgrom, P., Roberts, J., & Wilson, R. (1982) · Journal of Economic Theory · doi:10.1016/0022-0531(82)90029-1 · Cited by 2,494
Corporate culture and economic theory
Kreps, D. M. (1990) · Cambridge University Press eBooks · doi:10.1017/cbo9780511571657.006 · Cited by 2,225
Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000) · Communications of the ACM · doi:10.1145/355112.355122 · Cited by 2,190 · cited 1×
“As mentioned earlier, many have attributed the success of ebay to the advent of its reputation and feedback mechanism (see, e.g., Resnick et al. (2000)).”
BackgroundMilgrom, P. R., North, D. C., & Weingast*, B. R. (1990) · Economics & Politics · doi:10.1111/j.1468-0343.1990.tb00020.x · Cited by 1,793
The value of reputation on eBay: A controlled experiment
Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006) · Experimental Economics · doi:10.1007/s10683-006-4309-2 · Cited by 1,118
Roth, A. E. (2002) · Econometrica · doi:10.1111/1468-0262.00335 · Cited by 1,017
Promotional Reviews: An Empirical Investigation of Online Review Manipulation
Mayzlin, D., Dover, Y., & Chevalier, J. (2014) · American Economic Review · doi:10.1257/aer.104.8.2421 · Cited by 937
Research works printed but not linked: 3
- Bond E, 1982, Am. Econ. Rev., 72, 836
- Hendricks K, 1988, Am. Econ. Rev., 78, 865
- Klein T, 2016, J. Pol. Econ.
Cited by
Sorted by citations
Items 201–300 of 530
Reputation effects in peer-to-peer online markets: meta-analyses and laboratory experiments
Jiao, R. (2023) · Utrecht University Repository (Utrecht University) · doi:10.33540/1987 · Cited by 5
Transparency and Control in Platforms for Networked Markets
Pang, J. Z. F., Lin, W., Fu, H. D., Kleeman, J., Bitar, E. Y., & Wierman, A. (2022) · Operations Research · doi:10.1287/opre.2021.2244 · Cited by 9
Selling the self: Neo-normative control and the platform paradox
Cameron, L. D., Conzon, V. M., & Lam, L. (2025) · Research in Organizational Behavior · doi:10.1016/j.riob.2025.100230 · Cited by 11
Electronic Word-of-Mouth: A Survey from an Economics Perspective
Doi, N., & Hayakawa, H. (2020) · International Journal of the Economics of Business · doi:10.1080/13571516.2020.1747853 · Cited by 6
Mixed Signals: The Effects of Online Rating Discrepancy on User Trust
Schneider, F., Dykmann, P., & Teubner, T. (2025) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2520178 · Cited by 8
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Dahbi, H., Benabdellah, A. C., Belhadi, A., Kamble, S., & Zouadi, T. (2025) · Journal of the Operational Research Society · doi:10.1080/01605682.2025.2573824 · Cited by 5
Entrepreneurs on the Darknet: Reaction to Negative FeedbackPreprint
Batikas, M., & Kretschmer, T. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3238141 · Cited by 5
Nhat, D. T., Thäter, L., & Teubner, T. (2024) · Electronic Markets · doi:10.1007/s12525-024-00706-2 · Cited by 5
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Quantifying the relationship between specialisation and reputation in an online platform
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Chen, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4081483 · Cited by 5
The role of reputation systems in digital discrimination
(2019) · doi:10.31235/osf.io/85cze · Cited by 5
The Sharing Economy as an Equalizing Economy
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The End of Tourist Traps: The Impact of Review Platforms on Quality Upgrading
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Leveraging Social Media to Buy Fake Reviews
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The Value of Bad Ratings: An Experiment on the Impact of Distortions in Reputation Systems
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Applications of Signaling Theory in Sociological Scholarship
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Social dilemmas in the sharing economy
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Zou, W., Wang, J., & Yan, J. (2020) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.06.038 · Cited by 6
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Resolving selfish and spiteful interdependent conflict
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Share and benefit: incentives for cyber threat intelligence sharing
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Metadata
- Authors
- Tadelis, S.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Tadelis, Steven
- ORCID
- Tadelis, Steven
- Venue
- Annual Review of Economics
- Publisher
- Annual Reviews
- Published
- 2016-10-31
- Volume
- 8
- Issue
- 1
- Pages
- 321-340
- ISSN
- 1941-1383 (ISSN-L)
- 1941-1391
- OpenAlex
- W2517839752
- Cited by
- 524
- References
- 42
- Citations by year
- 2026: 80 · 2025: 67 · 2024: 66 · 2023: 66 · 2022: 57 · 2021: 53 · 2020: 49 · 2019: 42 · 2018: 24 · 2017: 13 · 2016: 6
- FWCI
- 10.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- 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
- Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
- Keywords
- online marketplaces, reputation systems, feedback systems, trust, online trade, algorithmic bias, peer review
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.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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 ›
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