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.

Abstract-With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: 'reviewer-related' features, 'review subjectivity' features, and 'review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

The growing pervasiveness of the Internet has changed the way that consumers shop for goods. Increasingly, usergenerated product reviews serve as a valuable source of information for customers making product choices online. While there is a significant body of theory on multi-attribute choice under uncertainty, the literature that examines product reviews has not built on this stream of theory for a variety of reasons. Typically, the impact of product reviews has been incorporated by numeric variables representing the valence and volume of reviews. In this paper we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted and hence, the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. We provide a text mining technique that allows us to incorporate text in choice and panel data models by decomposing textual reviews into segments, evaluating different product features. We test our approach on a unique dataset collected from Amazon, and demonstrate how it can be used to learn consumers' relative preferences for different product features. The dataset used contains three different groups of products (digital cameras, camcorders, PDAs), associated sales data and consumer review data gathered over a 15-month period. Additionally, we present and discuss two experimental techniques that can be used to alleviate the problem of data sparsity and of omitted variables: the first technique models consumer opinions as elements of a tensor product of independent feature and evaluation spaces and the second technique clusters rare opinions based on pointwise mutual information. The paper concludes by discussing the managerial relevance of this work as a tool for extracting actionable business intelligence from user-generated content.

Management, as well as seminar participants at the Fox School of Business at Temple University for useful suggestions. They also thank Ashish Agarwal, Avi Goldfarb, Yili Hong, Pei-yu Chen and Evan Polman for providing helpful comments. Anindya Ghose acknowledges funding from the WCAI (Wharton Customer Analytics Institute) and NSF CAREER Award IIS-0643847.