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Product reviews on retailer’s websites and online review platforms (ORPs) have become ubiquitous. Helpful reviews influence purchase decisions and prospective consumers use these reviews as information source as well as to gain an insight into the user experience of the product by reading about other people’s experiences (Hu, Liu, & Zhang, 2008; Park, Lee, & Han, 2007; Shen, Dai, Wang, & Gou, 2015). However, only a small fraction of the reviews is helpful; some review can be biased, provide incomplete information, or even be fake (Cheung, Luo, Sia, & Chen, 2009). Consequently, the research on online consumer reviews and the management of the review platforms has been steadily increasing in the past decade. Researchers have now established several characteristics of the products, reviews, and the reviewers that make the reviews helpful (Ghose & Ipeirotis, 2010), sorting rules that make the ORPs more convenient to navigate (Geekwire, 2016), and review summarization techniques that reduce cognitive burden and improve the prospective consumer’s experience (Geekwire, 2015).
Online retailers and ORPs are also proactive in working towards improving the user experience. They have implemented multiple ways in which a prospective consumer could navigate (search, sort, and filter) the reviews. They are trying to authenticate the reviews and obtaining reviews from experts (Geekwire, 2016). Other than this, they are also attempting to provide a richer experience to the prospective consumers by introducing additional content such as details about the product features, comparisons of the products with others based on the features, and recommendations of similar products. This additional retailer generated content targets the prospective consumer’s need for objective information (Bates, 1989; Payne, 1976) and also makes the websites stickier (Holsing & Olbrich, 2012; Lin, 2007). Research in information sciences has established the value of reviews and recommendations (Pazzani & Billsus, 2007; Adomavicius & Tuzhilin, 2005; Ghose & Ipeirotis, 2010). However, the contributions of retailer generated content other than product recommendations is not well understood. On one hand, content such as technical details and product comparisons increases the cognitive burden on the prospective consumer while on the other it satisfies the consumer’s need for objective information (Chen, Shang, & Kao 2009; Gao, Zhang, Wang, & Ba, 2012; Kim, Galliers, Shin, Ryoo, & Kim 2012). This paper aims at estimating the impact that enriching the prospective consumer’s experience through retailer generated content on the product landing pages has on prospective consumers.