Elena Grewal’s research while affiliated with Yale-New Haven Hospital and other places

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Publications (3)


Figure 1. (Color online) Review Flow on the Website
Figure 6. (Color online) Rating Distributions
Figure 7. (Color online) Effects of Experiment on Reviews
Figure 8. (Color online) Always Reviewer Causal Effects
Figure 9. (Color online) Distribution of Average Ratings at a Listing Level

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Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb
  • Article
  • Full-text available

October 2021

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50 Reads

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82 Citations

Marketing Science

Andrey Fradkin

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Elena Grewal

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David Holtz

Reputation systems are used by nearly every digital marketplace, but designs vary and the effects of these designs are not well understood. We use a large-scale experiment on Airbnb to study the causal effects of one particular design choice—the timing with which feedback by one user about another is revealed on the platform. Feedback was hidden until both parties submitted a review in the treatment group and was revealed immediately after submission in the control group. The treatment stimulated more reviewing in total. This is due to users’ curiosity about what their counterparty wrote and/or the desire to have feedback visible to other users. We also show that the treatment reduced retaliation and reciprocation in feedback and led to lower ratings as a result. The effects of the policy on feedback did not translate into reduced adverse selection on the platform.

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Bias and Reciprocity in Online Reviews

June 2015

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615 Reads

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114 Citations

Reviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems.

Citations (3)


... Finally, it is important to consider that individuals who leave reviews are likely different from those who do not, and individuals who visit memorials are likely different from those who choose not to visit. For example, studies of AirBnB reviews have shown that those who do not leave reviews tend to have worse experiences than those who do (Fradkin et al., 2021). While this study controls for review ratings, which helps account for some of the bias in emotional expression, it is important to recognize that the dataset may not fully capture the range of experiences at memorial sites. ...

Reference:

Memorials and collective memory: A text analysis of online reviews
Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb

Marketing Science

... However, transferring such systems into B2B scenarios is difficult since current systems applied in B2C scenarios are still subject to various challenges (Jøsang & Goldbeck, 2009). Remarkably, there is a lack of incentives for participants to submit ratings; they are often biased with unfair ratings, while fake ratings remain a huge issue (Ansari & Gupta, 2021;Dellarocas, 2003;Fradkin et al., 2018;He et al., 2022;Neumann & Gutt, 2019a;Resnick & Zeckhauser, 2002). On top of that, B2B environments are even more complex, showing various peculiarities (Chen et al., 2022;Dellarocas, 2003;Zhu, 2002). ...

The Determinants of Online Review Informativeness: Evidence from Field Experiments on Airbnb
  • Citing Article
  • January 2018

SSRN Electronic Journal

... About 72% of guests left reviews for their hosts on Airbnb (Chesky, 2012). A field experiment conducted on Airbnb has corroborated this data and demonstrated a similar finding that 68% of guests have left a review (Fradkin et al., 2015). Considering Airbnb's review policy and the vast proportion of reviews the guests provide, we used the number of reviews as a proxy for our dependent variable, NumberOfBookings. ...

Bias and Reciprocity in Online Reviews
  • Citing Conference Paper
  • June 2015