Academic journal article Journal of Electronic Commerce Research

Expert Reviewers Beware! the Effects of Review Set Balance, Review Source and Review Content on Consumer Responses to Online Reviews

Academic journal article Journal of Electronic Commerce Research

Expert Reviewers Beware! the Effects of Review Set Balance, Review Source and Review Content on Consumer Responses to Online Reviews

Article excerpt

1. Introduction

Web 2.0 has brought about dramatic changes in the way users interact with the Internet and has led to a rapid increase in the production of user generated content (UGC) [Kim et al. 2012]. One such form of UGC is online reviews. A growing number of online sellers are encouraging users or buyers of their products to post their personal evaluation on the sellers' website or provide (potential) customers with information from third-party review websites (such as,, [Godes & Mayzlin 2004]. In the US, 61% of Americans have ever left a review on a website, the most frequent location being (42%) [Diaz 2014]. Recent consumer surveys reveal that 79% at least sometimes check reviews before purchasing a product or service [Diaz 2014], and that 72% trust online reviews as much as personal recommendations [Anderson 2012]. Academic research also demonstrates the powerful role of online reviews in influencing consumers attitudes [Chakravarty et al. 2010; Sen & Lerman 2007], behavioral intentions [Sparks & Browning 2011; Tsang & Prendergast 2009], and sales [Chevalier & Mayzlin 2006; Zhu & Zhang 2010]. For example, Chevalier and Mayzlin [2006] showed that online book reviews impact product sales on and, while Zhu and Zhang [2010] concluded that online reviews play an influential role on video game sales.

At the same time, however, there is no standard in writing reviews and therefore, all reviews are not created equal. Reviewers usually discuss attributes that are relevant to them and different reviewers may have different experiences with the same product. As a result, it is very likely that readers are confronted with diverse online reviews that are in conflict with each other (some reviews are positive, some negative), while coming from different sources (both fellow consumers and experts) and discussing different product attributes. For instance, while some reviewers would discuss a certain attribute of the reviewed object (specifically to this study: the food in a restaurant), others would mention a different attribute (in this study: the service).

Past studies have identified that the influence of online reviews depends on multiple factors, such as the valence of the review set [e.g., Lee & Youn 2009], reviewer characteristics [e.g., Willemsen et al. 2012], number of reviews [e.g., Huang & Chen 2006] and review length [e.g., Mudambi & Schuff 2010]. Despite existing knowledge regarding the powerful impact of online reviews, there are still gaps that need to be addressed on how consumers process online reviews and come to a certain purchase decision. For instance, scholars have largely presumed that on the Internet, people evaluate information in a cognitively effortful fashion to arrive at a certain judgment [Metzger 2007]. However, recent research has shown that consumers also routinely invoke different heuristics and are, therefore, prone to cognitive biases such as social influences, review order and review source [e.g., Baek et al. 2012; Purnawirawan et al. 2012; Sparks et al. 2013; Sridhar & Srinivasan 2012].

The present study attempts to provide a better understanding of how people process online reviews in terms of review impression and purchase intention. More precisely, we investigate how review set balance (the ratio of positive and negative reviews about the same object), review source (peer or expert) and review content consistency (does a conflicting review discuss a product attribute consistent with the majority, or a different attribute?) interact to affect review impression and buying intention.

Although review set balance [e.g., Park et al. 2007; Purnawirawan et al. 2012] has not been studied as frequently as review valence, there seems to be a consensus that readers' attitudes toward a product are affected by the majority opinion [Burnkrant & Cousineau 1975; Sundar 2008]. …

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