Reputation and feedback systems are commonly integrated as a part of online marketplaces. However, the majority of the literature focuses on the static impact of online reviews (Reimers and Waldfogel, 2019; Tadelis, 2016). There is a growing body of research showing that firms respond to online reviews by taking certain actions, including adjusting advertising strat- egy accordingly (Hollenbeck et al., 2019), manipulating seller reputation with fake reviews (Mayzlin et al., 2014; Luca and Zervas, 2016), adopting costly short-run action to improve ratings (Hunter, 2020) and replying to reviews and improving product quality based on reviews (Proserpio and Zervas, 2016; Ananthakrishnan et al., 2019). Given the economic significance of two-sided platforms, each platform policy change can have large impacts on consumers, sellers and the platforms themselves. Across two essays, I aim to show two types of firm responses to their ratings and shed light on their corresponding platform rating policy implications.In Chapter 1, we study the market of fake product reviews on Amazon.com. Reviews are purchased in large private groups on Facebook and other sites. We hand collected on these markets and then collected a panel of data on these products’ ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. Using detailed data on product outcomes before and after they buy fake reviews, we can directly determine if these are low-quality products using fake reviews to deceive and harm consumers or if they are high-quality products that solicit reviews to establish reputation. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. The theoretical literature on review fraud shows conditions when they are a deceptive form of fraud and conditions where they function as simply another form of advertising. Finally, we examine whether rating manipulation harms consumers or whether it is mainly used by high-quality product producers as an alternative to advertising or by new products trying to solve the cold-start problem. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for younger products, indicating rating manipulation is mostly used by low-quality product producers. Finally, we observe that Amazon deletes large numbers of reviews, and we document their deletion policy.
In Chapter 2, we study how rating system design affects innovation incentives. In settings where product quality cannot be observed prior to purchase, online ratings serve as a signal of product quality for consumers and affect demand. Owing to their impact on sales, ratings also motivate firms to innovate. If firms use displayed ratings to guide their investments in improving product quality, then platform rating aggregation policies can play a key role in increasing or decreasing firms’ innovation incentives. We study in depth the impact of online rating systems on innovation incentives and, more importantly, the corresponding implications of the design of the rating aggregation policy. After collecting a unique firm- level dataset from a mobile game app platform, we combined reduced-form analysis and the structural model to show how rating systems can be optimized for innovation. We show that innovation has a positive impact on all key rating system metrics. Building on empirical evidence, we developed a dynamic structural model to represent firms’ forward- looking behavior and estimate innovation cost. We then evaluate the impact of alternative rating aggregation policies on innovation incentives. The counterfactual analysis shows that placing greater weight on recent ratings can increase the innovation rate substantially.
Across two chapters, this dissertation contributes substantively and theoretically to our comprehension of how firms respond to their online ratings and how two-sided platforms can design better policies to combat fake reviews and encourage firm innovation. As rating systems are increasingly adopted by platforms and consumers rely on ratings to make decisions, it is important to design better platform rating policies to help consumers, honest firms, and the platforms themselves.
