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Top1. Introduction
It is common for consumers to seek information about a product (or service) prior to making their purchasing decisions. With the internet becoming a greater part of personal and professional life, online user reviews are an increasingly important source for discovering product quality (Zhu and Zhang 2010). For example, Cone Trend Tracker find that 64% of their respondents search for consumer reviews prior to purchase in 2011 (up from 55% in 2010) (Cone Trend Tracker 2011). The literature refers to online reviews as online word-of-mouth (WOM). Online WOM may substitute and complement traditional WOM interaction (e.g., between friends or family) and business-to-consumer communication (e.g., advertising) (Chevalier and Mayzlin 2006). For example, consumers may rely on traditional or online WOM, advertising, or a combination of these sources to fulfill their product information needs.
The literature suggests that online user reviews influence sales (Godes and Mayzlin 2004; Liu 2006; Duan, Gu and Whinston 2008; Dellarocas, Zhang, and Awad 2007; Chevalier and Mayzlin 2006; Zhu and Zhang 2010; Clemons, Gao, and Hitt 2006; Moe and Trusov 2011). Yet, empirical findings are mixed on the significance of the two main online WOM metrics, number of reviews (volume) and average rating (valence) (Godes and Silva 2011). When review volume is significant, it implies that online WOM builds awareness (i.e., spreads information) (Godes and Mayzlin 2004; Dellarocas, Zhang, and Awad 2007; Zhu and Zhang 2010). The significance of valence implies that online WOM has a persuasive role (Liu 2006; Chatterjee 2001). Considerable attention is given to experience goods over search goods as dominant attributes of the latter are difficult to assess prior to purchase. Additionally, the analyses focus on single product categories, for example, box office movies (Liu 2006; Duan, Gu and Whinston 2008; Dellarocas, Zhang, and Awad 2007).
Despite empirical support for the efficacy of online reviews in driving sales, there are still reasons to suspect that their role may be limited (Zhu and Zhang 2010; Dellarocas and Narayan 2006). For example, reliance on online reviews may vary based on whether a product is an experience good or search good, and the anonymity of online WOM may make it less credible compared to traditional WOM (Dellarocas 2003). In an attempt to fill a gap in the literature, this study examines the relationship between Amazon’s sales ranks and its online WOM for ten experience and search categories from “Hot New Releases” during the regular and holiday season. The objective of this study is to examine whether online WOM has an impact on consumers’ online purchasing behavior and the nature of such an impact (i.e., awareness vs. persuasive). We incorporate Facebook Likes, which is part of Amazon’s integration with Facebook, as a measure of social media. This is a “thumbs-up” icon consumers can click on in an Amazon product page indicating that they “Like” a product. Amazon shoppers can see if their friends “Liked” a product and they can choose to share their “Likes” via e-mail, Facebook or other social media platform. We find that review volume, star ratings, and Facebook Likes impact Electronics category sales, and that Facebook Likes impact Music category sales. This indicates that online WOM has an awareness and persuasive role in driving sales for these experience categories.
The rest of the article is organized as follows: a literature review on WOM as a predictor of consumer behavior is followed by the research methodology detailing various hypotheses we propose and test as well as the data used for the empirical tests. The ‘Results and Discussions’ section then follows. Finally, we present what we learned from this exercise in the ‘Conclusions’ section.