https://doi.org/10.1177/1938965520902012 Cornell Hospitality Quarterly 1–12 © The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1938965520902012 journals.sagepub.com/home/cqx Article Introduction Search goods with prepurchase product descriptions allow the potential customers to evaluate a product’s quality with- out actually purchasing it (Nelson, 1970). However, service in the hospitality industry is considered an experience good, meaning that prepurchase product descriptions of hotels are not particularly informative and potential customers must actually visit a hotel to evaluate its quality (Huang et al., 2009). Therefore, previous studies show that customers typically spend more time and energy learning from others’ experiences when reading prepurchase information on experience goods than when reading prepurchase informa- tion on search goods (Huang et al., 2009). Because the experiences of others have a strong impact on potential cus- tomers, online reviews are particularly important in the hos- pitality industry. C. Anderson (2012) demonstrated the importance of online reviews to hospitality firms as he indi- cated an increasing frequency of visitation to review sites like TripAdvisor prior to hotel purchase as well as was indi- cating that a 1% increase in online reputation resulted in a 0.96% in hotel performance (RevPAR). However, although numerous recent papers discuss online reviews, little work has been done in hospitality research on to answering the fundamental question: Are online reviews biased? The goal of this study is to investigate how customers’ intrinsic motivation to post reviews induces bias in the online review system and how this bias is reduced when individuals become familiar with the platform. In particular, we suggest that online review platforms are more likely to suffer from negative and extreme underreporting biases when reviews are generated by customers who are unfamil- iar with the platform. In contrast, customers who are famil- iar with a given review platform tend to equally post reviews across a variety of ratings, which reduces the underreport- ing bias. This finding is explained using the benefit-cost theory (Thibaut & Kelley, 1959). Several researchers have empirically demonstrated the existence of systematic biases in online consumer product ratings. For example, studies show that only con- sumers with positive expected net utility will purchase a product and have the opportunity to review it, and thus the submitted ratings are likely to be positively skewed (Hu et al., 2017). Therefore, product evaluations may not account for the net utility to those who have not yet pur- chased the product. As a result, the distribution of online product reviews is likely to be positively skewed (Gao et al., 2015; Hu et al., 2006, 2017). Other studies focus on the temporal and sequential bias components in a plat- form. For instance, Li and Hitt (2008), Godes and Silva (2012), and Moe and Trusov (2011) show that posted product ratings become increasingly negative as rating environments mature. 902012CQX XX X 10.1177/1938965520902012Cornell Hospitality QuarterlyHan and Anderson research-article 2020 1 Cornell SC Johnson College of Business, Ithaca, NY, USA Corresponding Author: Saram Han, School of Hotel Administration, Cornell SC Johnson College of Business, Ithaca, NY 14853, USA. Email: sh2322@cornell.edu Customer Motivation and Response Bias in Online Reviews Saram Han 1 and Chris K. Anderson 1 Abstract The voluntary nature of online customer review platforms self-selects customers with strong opinions, resulting in an underreporting bias. However, little research has been conducted on the relationship between postpurchase satisfaction and the propensity to share one’s opinion. The goal of this study is to empirically examine the relationship between customer satisfaction and reporting motivation in online review platforms. The results of this study demonstrate that customer intention to post an online hotel review varies depending on the level of customer satisfaction. Online reviewers are more motivated to post extreme and negative ratings. However, this underreporting bias is mitigated when ratings are generated by reviewers who are familiar with the online review posting process. The relationship between individual familiarity with the review platform and the underreporting bias can be explained using the benefit-cost theory. Keywords underreporting; online review; benefit-cost theory