Abstract
The aim of this paper is to study the empirical phenomenon of rating bubbles, i.e. clustering on extremely positive values in e-commerce platforms and rating web sites. By means of a field experiment that exogenously manipulates prior ratings for a hotel in an important Italian tourism destination, we investigate whether consumers are influenced by prior ratings when evaluating their stay (i.e., social influence bias). Results show that positive social influence exists, and that herd behavior is asymmetric: information on prior positive ratings has a stronger influence on consumers’ rating attitude than information on prior mediocre ratings. Furthermore, we are able to exclude any brag-or-moan effect: the behavior of frequent reviewers, on average, is not statistically different from the behavior of consumers who have never posted ratings online. Yet, non-reviewers exhibit a higher influence to excellent prior ratings, thus lending support to the social influence bias interpretation. Finally, also repeat customers are affected by prior ratings, although to a lesser extent with respect to new customers.
Highlights
Nowadays user-generated ratings are an inseparable part of the Web - an essential component of what was called the Web 2.0 by many
The literature provides many recommendations to mitigate or eliminate the problems encountered in online rating systems and UGC platforms; these management implications would enhance the effectiveness of marketing strategies and better regulate the market
While Aral (2014) proposes systems where the average score is hidden while users rate a product, Krishnan et al (2014) suggest to introduce the 3-step rating system applied by their case-study, the California Report Card, and use machine learning to estimate the social influence bias and to correct it before the review is posted onto the platform
Summary
Nowadays user-generated ratings are an inseparable part of the Web - an essential component of what was called the Web 2.0 by many. 2“World Tourism Organization UNWTO: Annual Report 2014” (2015); available at http://www2.unwto.org/annualreport2014 cannot deliver efficient and trustworthy information; iv) the impact of online ratings on behavioral intentions and product sales. Each of these issues has positive and normative contents, suggesting reforms for: improving the reliability of user-generated online ratings; the organization and functioning of online rating platforms; enhancing the effectiveness of firms’ marketing policy; improving the regulation of markets in order to enhance efficiency. The paper is structured as follows: Section 2 provides a literature review on online rating systems; Section 3 motivates the paper and unfolds the main research questions and the novelty of our approach; Section 4 describes the experimental design; Section 5 presents the main results; Section 6 discusses the findings and relates them to previous and future research
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