Analyzing the Spillover Roles of User-Generated Online Product Reviews on Purchases: Evidence from Clickstream Data

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1 Analyzing the Spillover Roles of User-Generated Online Product Reviews on Purchases: Evidence from Clickstream Data Young Kwark*, Gene Moo Lee**, Paul A. Pavlou***, and Liangfei Qiu* *University of Florida, **University of Texas at Arlington, ***Temple University Abstract: Online product reviews are increasingly important for shaping consumer purchasing decisions. In this study, using clickstream data, we examine the spillover roles of online product reviews on consumer purchasing across substitute versus complementary products by leveraging text to quantify the similarity of pairwise products. We also investigate how product characteristics (e.g., niche product) and the channel (whether the product review is viewed on a mobile device or a PC) moderate the proposed spillover roles. Our study has managerial implications on how to leverage the spillover roles of online product reviews on substitute/complementary products. Key words: Spillover roles, Online product reviews, Substitute products, Complementary products Introduction Online product reviews have received much interest from academics and practitioners alike. This is because consumers do rely on the reviews delivered by other consumers to reduce product uncertainty (e.g., Dimoka, Hong, Pavlou 2012; Hong and Pavlou 2014). Previous studies largely focused on the effect of online product reviews on aggregate product demand (e.g., Archak et al., 2011; Forman et al., 2008; Goes et al., 2014), and, in general, they showed the influential effect of online product reviews on consumer purchasing decisions (e.g., Chevalier and Mayzlin 2006; Hu, Pavlou, and Zhang 2009; Zhu and Zhang 2010). However, the literature exclusively focused on the effect of online product reviews on a single product, ignoring their spillover role on other products. Extending the literature, we use

2 clickstream data to examine the spillover role of online product reviews in consumer purchasing decisions for substitute/complementary products. We also examine how product characteristics and channel media moderate the spillover role of online product reviews on other related products. Clickstream data from consumers who shop on a retailer s website allow us to observe complete records of online product reviews consumers viewed, enabling us to clearly define a consumer s consideration set and examine whether the online reviews for a focal product the consumer viewed affect her likelihood of purchasing other products of the same brand, and/or purchasing competing products of a different brand. We aim to answer two research questions: (1) How do the online reviews of a focal product affect the probability of a consumer purchasing competing products? (2) How do product characteristics (e.g., niche products), competition intensity (e.g., complementary versus competing products), and channel media (e.g., mobile or PC) moderate the role of the online reviews of a focal product on competing products? At the core of our study is the notion of competing products. And defining competing products in practice is not trivial. We leverage consumer co-visits with a text mining algorithm. If consumers covisited a pair of products in a session, we can infer those products to be related. We use a text mining algorithm of topic modeling on product descriptions to quantify the similarity of pairwise products. Once we build various similarity measures among products, we can estimate the competition or halo effect with respect to different similarity levels. For example, if two products are almost identical in text similarity, we may observe stronger spillover effects. In other words, we can quantify the intensity of product competition.

3 Data Data are collected from a UK-based big box retailer, and they contain individual level clickstream data for 250,000 consumers, including website visits, product page views, reviews read, and purchases made. Consumers views and transactions on the retailer s website were tracked over the two months in two product categories of Technology and Home & Garden. Analysis To examine the spillover role of the online product reviews on the focal product, we consider a pair-wise relationship of the focal product and the other products in a consumer s choice set. The key variables of four types of the other products and focal products are described as follows. Table 1. Key Variables in Research Model We present our preliminary results below: (1) Baseline estimation: Substitutes Vs. Complements In the first set of regression models, we do not differentiate between substitutes produced by the same brand or different brands. The dependent variable is purchase, and the independent variables are

4 rate_focal, vol_focal, rate_subs, and rate_comp. Among the pairs of products consumers co-visited in a session, we measure the similarity measures generated by topic modeling. If similarity is greater than 0.8, we call them substitutes; if the value is smaller than 0.2, we call them complements. The results are robust to different thresholds of similarity. We use fixed effects models to control for unobserved individual heterogeneity and all standard errors are clustered in consumers. The baseline result is that the coefficient on rate_subs is negative and significant, while the coefficient on rate_comp is positive and significant, consistent with our intuition on substitutes and complements (Column 1 in Table 2). Table 2. The Spillover Roles of Online Product Reviews ( Home Category) (2) Substitutes vs. Complements of Same vs. Different Brands In the next estimation, the dependent variable is purchase, and the independent variables are rate_focal, vol_focal, price_focal, rate_subs_samebrand, rate_comp_samebrand, rate_subs_diffbrand, and rate_comp_diffbrand. Our basic findings are as follows (Column 2 in Table 2): (1) the focal product

5 rating and the focal product review volume have a positive impact on purchases of the focal product. Price has a negative impact. (2) rate_subs_diffbrand has a larger negative impact than rate_subs_samebrand. The implication is that the negative spillover role of the online reviews of substitute products from different brands is greater than that of the same brand. (3) rate_comp_diffbrand has a larger positive impact than rate_comp_samebrand. The implication is that the positive spillover role of online reviews of substitute products from different brands is greater than that of the same brand. Summarizing (2) and (3), we show that the magnitude of the spillover role of online product reviews critically depends on (a) whether the product is a substitute or a complement; (b) whether the product is produced by the same brand or a different brand. The results are qualitatively consistent in both categories (home & technology). (3) Moderating Effects We examine how the spillover role (different brands) or halo role (same brands) is moderated by other factors, such as product characteristics, specifically the product being a niche one (measured by the volume of reviews or purchases) and channel media (measured by the device used to view products--- mobile or PC). We find that the negative impact of the online reviews for substitute products is stronger. Concluding Remark Using clickstream data, we investigate the "spillover role of online product reviews on competing products at the individual consumer level. This study will provide managerial implications for practitioners to better leverage online product reviews by shedding light on spillover role of product reviews and helping the design of online product review systems. REFERENCES AVAILABLE UPON REQUEST