Consumers' ratings for restaurants are lower when they went to the restaurants on special occasions, which can be explained by one theory of attribution bias (disappointment of high expectations)

Huang, Ying-Kai, Hope Hurts: Attribution Bias in Yelp Reviews (July 22, 2021). SSRN: https://ssrn.com/abstract=3891195

Abstract: This paper incorporates applied econometrics, causal machine learning and theories of reference-dependent preferences to test whether consuming in a restaurant on special occasions, such as one's birthday, anniversary, commencement, etc., would increase people's expectations and would make consumers rate their consumption experiences lower. Furthermore, our study is closely linked to the emerging literature of attribution bias in economics and psychology and provides a scenario where we can test two leading theories of attribution bias empirically. In our paper, we analyzed reviews from Yelp and combined the text analyses with regressions, matching techniques and causal machine learning. Through a series of models, we found evidence that consumers' ratings for restaurants are lower when they went to the restaurants on special occasions. This result can be explained by one theory of attribution bias where people have higher expectations about restaurants on special occasions and then misattribute their disappointment to the quality of the restaurants. From the connection between our empirical analysis and theories of attribution bias, this paper provides another piece of evidence of how attribution bias influences people's perceptions and behaviors.

Keywords: Attribution Bias, Reference Dependence, Online Reviews, Causal Machine Learning

JEL Classification: D91, D83, D12