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E-commerce and Web 2.0 had a radical impact on the way consumers interact with retailers. Through review mechanisms, consumers are now able to tell retailers – and everyone else – exactly what they think about an offered product or service. Online product reviews provide a new level of insight and have been used in new product development (Lee & Yang, 2015) and marketing initiatives (Lee & Bradlow, 2011). They also play a key role in the purchase decision of other consumers. Researchers have reported that more than 90% of online shoppers read reviews before making a purchase and 83% report that their purchase decision is based on reviews (Zhang, Cheung & Lee, 2014). Reviews have been found to influence purchase decision (Lee & Ma, 2012) and have a positive impact on sales (Floyd, Freling, Alhoqail, Cho & Freling, 2014; Hu, Liu & Zhang, 2008; Yao, Fang, Dineen & Yao, 2009). The assumption is that the reviews are written by other consumers and are unbiased, which leads individuals to trust the comments (Cheung, Sia & Kuan, 2012; Huang, Tan, Ke & Wei, 2018; Robinson, Goh & Zhang, 2012). In fact, the opinions of peers are deemed to be more trustworthy than company sponsored advertising (Dellarocas, 2003; Sher & Lee, 2009). Trust in online transactions and data quality measures have been studied extensively in the information systems literature. This research synthesizes these streams of research to better understand how online reviews are perceived as useful to the consumer.
Given the breadth and depth of this new consumer communication channel, it is not surprising that online reviews have generated a substantial amount of marketing research. Whether examined as Electronic Word of Mouth (eWOM), online product reviews (OPR) or voice of the customer (VOC), researchers have sought to understand what makes a review useful to another consumer. Recent meta-analytic studies have found there is general agreement that review helpfulness is impacted positively by review length, age, reviewer information disclosure and indicators of reviewer expertise (Hong, Xu, Wang & Fan, 2017; Wang, Wang & Yao, 2018). The research is mixed on the impact of rating (linear and quadratic) and readability on the usefulness of reviews. Moderators, such as product type (Mudambi & Schuff, 2010) and review length (Chua & Banerjee, 2015b) have been found to impact review usefulness. Wang et al. (2018) call for a more theoretically grounded explanation of what constitutes a helpful review. The information systems research, in the areas of trust in online transactions and data quality is a potential source of theoretical underpinning. This research considers the usefulness of online product reviews in the context of Wang & Strong’s (1996) Data Quality Framework. Incorporating both heuristic and systematic elements, the proposed model seeks to understand how consumers judge the usefulness of an online review.