质量披露与失望：来自奥斯卡提名的证据

Abstract

This study examines the unintended consequences of quality disclosures, focusing on how Academy Award nominations impact consumer satisfaction in the movie industry. Awards and certifications typically signal high quality and increase consumer expectations. Yet, if the experience falls short of the expectation, they may also lead to disappointment. Using a novel data set from MovieLens, we analyze user ratings for movies surrounding Academy Award nominations from 1995 to 2019. We first implement a difference-in-differences strategy comparing nominated and non-nominated films and then introduce a novel recommendation-based matching approach that leverages vector representations of user preferences trained prior to the nominations. Our analysis removes taste-based selection and isolates changes in user experience: users who rate a movie after its nomination assign significantly lower ratings than similar users who rated the same film earlier. This effect accounts for more than 7% of the prenomination rating gap between nominated and non-nominated films and is most pronounced among less experienced users. Our findings are validated with data from IMDb, where the effect is even more pronounced, likely reflecting differences in the composition of the user base across platforms. Additional textual analysis of user-generated content on both platforms provides further evidence that the postnomination decline in ratings is driven by disappointment, rather than disinterest, deteriorating viewing conditions, or snob effects associated with mainstream popularity.