On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data
Information Systems Research, Forthcoming
Posted: 14 Sep 2016 Last revised: 15 May 2021
Date Written: February 5, 2021
We study the spillover effects of the online reviews of other co-visited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by: (a) whether the related (co-visited) products are complementary or substitutive, (b) the choice of media channel (mobile or PC) used, (c) whether the related products are from the same or a different brand, and (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product-pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of co-visited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less-experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.
Keywords: Online Product Reviews, Substitutive Products, Complementary Products, Brand Spillover, WOM Spillover, Topic Modeling, Machine Learning
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