Please join the Marketing Group for its third Research Seminar of the Spring 2020 semester, featuring Megan Hunter, a PhD candidate in quantitative marketing at Stanford Graduate School of Business.
In this paper, I show that the manner in which ratings are displayed incentivizes firms to strategically take costly short-run actions that improve their ratings. In my context, ratings are displayed to the nearest half star, as is common practice on many ratings platforms. Since the true average rating is not shown, firms have an incentive to remain just above the rounding threshold in order to have a higher displayed rating. However, once a firm's rating passes the threshold, the incentive to improve their ratings drops. I study this phenomena in the context of auto repair.
Consumers face significant uncertainty in the auto repair market, which makes it a prime context in which to study reviews. I first show that consumers value ratings in this market. Firms' revenue and number of consumers increases with their displayed rating. To overcome the endogenous relationship between ratings and quality, I utilize a regression-discontinuity strategy. Since ratings have a significant effect on demand, firms have an incentive to pay attention to their ratings. Consistent with the manipulation of ratings by firms, I find that there is an excessive amount of bunching around ratings thresholds. These actions are typically unobserved, but due to my novel data and the discontinuity of displayed ratings, I can model firm behavior. Specifically, I provide evidence that firms change the services they provide and potentially exert extra effort in key rating states. Finally, I provide a theoretical framework in order to quantify the actions and provide optimal policies for firm actions depending on their rating and number of reviews.