Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets

Abstract

C onsumer-generated product reviews have proliferated online, driven by the notion that consumers' decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Online community members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members' judgment of products and reviews. Implications for research on the relationship between online word-of-mouth (WOM) and sales, peer recognition and reputation systems, and conformity to online community norms are discussed.

Herd behavior in consumers’ adoption of online reviews

“…First, we enrich the understanding of the influence of online reviews. At the market level, product sales are found to be influenced by the level of review ratings (Zhu & Zhang, 2010), the valence and volume of online reviews (Berger, Sorensen, & Rasmussen, 2010;Chevalier & Mayzlin, 2006;Duan et al, 2008), and the characteristics of review contributors (Forman, Ghose, & Wiesenfeld, 2008). However, this line of studies provides limited insight into individuals' cognitive processes with regard to how informational factors (e.g., factors related to information itself and information sources) may affect ones' decisionmaking process.…”

The role of mobile‐computing self‐efficacy in consumer information disclosure

“…Consequently, if the new Web vendors did not already have social or brand capital (Lowry et al, 2008), they had to find alternative ways to convince potential consumers of their trustworthiness. Such persuasion attempts have been, for example, through the use of privacy and security seals (Bélanger et al, 2002;Kim et al, 2008;Xu et al, 2010;Lowry et al, 2012) and high-quality and intuitive websites with attractive interfaces (McKnight et al, 2002;Gefen et al, 2003;Lowry et al, 2008) including logos , mobile websites and convincing consumer reviews (Forman et al, 2008).…”

“Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews

“…This pioneering work shows that insight into the composition of reviews is imperative in understanding the effects of reviews on consumer judgment, as consumers seem to react differently to different types of reviews. For example, by linking useful votes to the product rating of a review, several studies found that clearly negative or positive product ratings (i.e., 1-and 5-star ratings) are perceived as more useful than moderate ratings (i.e., 3-star ratings, see Danescu-Niculescu-Mizil, Kossinets, Kleinberg, & Lee, 2009;Forman, Ghose, & Wiesenfeld, 2008). Others found that the polarity of product ratings contribute to the perceived usefulness of reviews, such that negative reviews have more impact on consumer judgment than positive reviews (Basuroy, Chatterjee, & Ravid, 2003;Chevelier & Mayzlin, 2006;Sen & Lerman, 2007).…”

“…We collected the useful votes and total votes given above posted reviews in the form: ''[number of useful votes] out of [number of members who voted] found the following review useful' (see Figure 1). By calculating the fraction of useful votes among the total votes, useful votes were translated into percentages ranging from 1 to 100 that indicate the 'perceived usefulness of a review' (Forman et al, 2008;Mudambi & Schuff, 2010).…”