EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Promotional Chat on the Internet
Marketing Science
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects
Information Technology and Management
Communicative practices in an online financial forum during abnormal stock market behavior
Information and Management
Systematic analysis of centralized online reputation systems
Decision Support Systems
Estimating sequential bias in online reviews: A Kalman filtering approach
Knowledge-Based Systems
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Decision Support Systems
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the mean consumer rating of that product. Hence, manipulation decreases the informativeness of online reviews. Furthermore though consumers understand the existence of manipulation, they can only partially correct it based on their expectation of the overall level of manipulation. Hence, vendors are able to change the final outcomes by manipulating online reviewers. In addition, we demonstrate that at the early stages, after an item is released to the Amazon market, both price and reviews serve as quality indicators. Thus, at this stage, a higher price leads to an increase in sales instead of a decrease in sales. At the late stages, price assumes its normal role, meaning a higher price leads to a decrease in sales. Finally, on average, there is a higher level of manipulation on Barnes & Noble than on Amazon.