Explaining and predicting online review helpfulness: The role of content and reviewer-related signals

Online reviews provide information about products and services valuable for consumers in the context of purchase decision making. Online reviews also provide additional value to online retailers, as they attract consumers. Therefore, identifying the most-helpful reviews is an important task for online retailers. This research addresses the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling theory. Thereby, our research model posits that the reviewer of a product sends signals to potential buyers. Using a sample of Amazon.com product reviews, we test our model and observe that review content-related signals (i.e., specific review content and writing styles) and reviewer-related signals (i.e., reviewer expertise and non-anonymity) both influence review helpfulness. Furthermore, we find that the signaling environment affects the signal impact and that incentives provided to reviewers influence the signals sent. To demonstrate the practical relevance of our results, we illustrate by means of a problem-specific evaluation scenario that our model provides superior predictions of review helpfulness compared to earlier approaches. Furthermore, we provide evidence that the proposed evaluation scenario provides deeper insights than classical performance metrics. Our findings are highly relevant for online retailers seeking to reduce information overload and consumers' search costs as well as for reviewers contributing online product reviews. (C) 2018 Elsevier B.V. All rights reserved.