The Determinants of Online Review Informativeness: Evidence from Field Experiments on Airbnb

Fradkin, Andrey; Grewal, Elena; Holtz, David

doi:10.2139/ssrn.2939064

Cited by 162 publications

(216 citation statements)

References 10 publications

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“…Market design principles have generally focused on increasing the information flow within a platform (Bolton et al 2013, Che and HÖrner 2014, Dai et al 2014, Fradkin et al 2014), but we highlight a situation in which platforms may be providing too much information.…”

Section: A Designing a Discrimination-free Marketplacementioning

confidence: 99%

Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

Edelman¹,

Luca

Svirsky

2017

American Economic Journal: Applied Economics

558

419

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Section: A Designing a Discrimination-free Marketplacementioning

confidence: 99%

Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

Edelman¹,

Luca

Svirsky

2017

American Economic Journal: Applied Economics

558

419

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“…Studies focusing on the motivations behind joining Airbnb to provide hospitality have pointed out that monetary compensation and sociability are two important aspects [19,22]. Fradkin et al experimentally investigated the determinants and bias in the Airbnb review system [14]. Fradkin proposed ranking algorithms for the Airbnb search engine [13].…”

Section: Related Workmentioning

confidence: 99%

“…These results may suggest that many guests had overall positive experiences during their stays, corroborating advocates' argument on traveler experiences. It can also indicate the presence of selection bias in review behaviors [14]-only those who had great experiences chose to give reviews.…”

Section: Introductionmentioning

confidence: 99%

Sharing Means Renting?: An Entire-Marketplace Analysis of Airbnb

2017

SSRN Journal

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Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue-commercial hosts who own multiple listings on Airbnb-repeatedly discussed in the current debate. We find that their existence is prevalent, they are early-movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.

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“…Under a critical lens, scholars have begun to look at questions of power [56] and inequality [1], covering algorithms [17,62], information asymmetries [69], collective action [66], and ratings [30,52,73]. However, limited research to date has looked at the psychological and emotional repercussions of the sharing economy on providers or consumers.…”

Section: Introductionmentioning

confidence: 99%

“…This mechanism works in a form of indirect reciprocity, where information about participants can be shared among a network [12,43,50]. For example, the key trust mechanism on Airbnb is the review feature [30,73], while Uber and other ride-sharing platforms rely on bilateral user ratings [52].…”

Section: Introductionmentioning

confidence: 99%