Abstract

Reviews drive online sales. In a perfect world, reviews represent a way to make the market contain less friction. That is, they accurately estimate the value of products without bias from the person writing the review. However, some suggest that factors that do not represent the quality of the product alter the relationship between the review and the sale. We use Tajfel and Turner (1986)’s social categorization theory to develop a model that predicts the sentiment of the review left by a guest. We test our hypotheses using archival data about interactions on AirBnB. We find that when the host and the guest share the same ethnicity with positive stereotypes, the review is more positive. However, if the host and the guest share an ethnicity with negative stereotypes, the review is more negative. The discussion section reflects on these results with respect to theory and offers practical implications.

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Birds of a Feather Lodge Together?: Predicting Review Sentiment Using Social Categorization Theory

Reviews drive online sales. In a perfect world, reviews represent a way to make the market contain less friction. That is, they accurately estimate the value of products without bias from the person writing the review. However, some suggest that factors that do not represent the quality of the product alter the relationship between the review and the sale. We use Tajfel and Turner (1986)’s social categorization theory to develop a model that predicts the sentiment of the review left by a guest. We test our hypotheses using archival data about interactions on AirBnB. We find that when the host and the guest share the same ethnicity with positive stereotypes, the review is more positive. However, if the host and the guest share an ethnicity with negative stereotypes, the review is more negative. The discussion section reflects on these results with respect to theory and offers practical implications.