Promotional Reviews: An Empirical Investigation of Online Review Manipulation

By Dina Mayzlin, Yaniv Dover, Judith A. Chevalier. 2014

908 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0dce6407a2fa77c800538513399691d756fe1076

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In Management Science and Operations Research

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Citations in the corpus

Reputation and Feedback Systems in Online Platform Markets (2016, background): “Mayzlin et al. (2014) exploit different policies about who can leave feedback across several travel sites and show biases in ratings for hotels from the online travel sites that are consistent with strategic feedback manipulation by sellers.”
The manager's dilemma: a conceptualization of online review manipulation strategies (2018, background): “In their study of the two websites, Mayzlin, Dover, and Chevalier (2014) found that the distribution of reviews on TripAdvisor, therefore, contains more weight in both extreme tails.”
When and how Managers' Responses to Online Reviews Affect Subsequent Reviews (2018, methodology): “Third, we contribute to the causal identification literature in the eWOM context (Chevalier and Mayzlin 2006; Mayzlin, Dover, and Chevalier 2014; Proserpio and Zervas 2017) by using treatment observability as amethod of estimating the effect of the treatment on the treated.”
Scoping the Sharing Economy: Origins, Definitions, Impact and Regulatory Issues (2016, background): “More generally, on TripAdvisor 31% of reviews are five star, on Expedia 44% (Mayzlin et al., 2014) and on Airbnb 75%.”
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud (2016, neutral): “For example, Mayzlin et al. (2014) ﬁnd that the management structure of hotels in their study is associated with review fraud.”
Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews (2015, methodology): “†We thank Frederic Brunel, John Byers, Sharon Goldberg, Michael Luca, Tim Simcoe, and Greg Stoddard for helpful comments and discussion.”
How credibility affects eWOM reading: The influences of expertise, trustworthiness, and similarity on utilitarian and social functions (2014, background): “Typically, companies aim to minimize the occurrence of negative eWOM and amplify positive eWOM (Mayzlin, Dover, and Chevalier 2012).”
Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings (2016, background): “Firms (or their agents) sometimes post fictitious favorable reviews for their own products and services and/or post fictitious negative reviews for the products and services of their competitors (Mayzlin, Dover, and Chevalier 2014).”
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction (2016, background): “…have an incentive to collude and cheat as in the setting of peer grading, as well as reviews on sites like Amazon and Yelp, where artists and ﬁrms are incentivized to manufacture positive reviews for their own products and negative reviews for their rivals (Harmon, 2004; Mayzlin et al., 2012).”
The Passions and the Interests: Unpacking the 'Sharing Economy' (2016, background): “More generally, the rate of five star reviews is 31% on TripAdvisor and 44% on Expedia (Mayzlin et al., 2014) compared to 75% on Airbnb.”