The increased Internet usage has driven a rapid growth of e-commerce transactions. One of the key determinants of the increased online transactions is the influence of electronic word-of-mouth (eWOM) in the form of online reviews. In particular, comparative reviews that compare similar products provide valuable information for consumers to evaluate multiple products and play a pivotal role in driving consumer purchase decisions. By constructing a product network based on products connected by comparative reviews, we develop several new network centrality measures and empirically examine the impact of eWOM through these new centrality measures and the semantic similarity of the comparative reviews. We find that the comparative reviews are key eWOM measures that influence the product’s sales within a product network. Our findings also demonstrate that the text semantic similarity is a better measure of the strength of tie in a comparative product network than the review sentiment. Our study contributes to the eWOM literature by utilizing text review semantic similarity to capturing review strength based on the latent product features, and to the network graph theory through the new centrality measures we have developed. Overall, our findings provide important insights for e-commerce platform operators and vendors to leverage the impact of eWOM and help consumers compare products in a more effective manner.
Vemprala, Naga; Liu, Charles Zhechao; and Choo, Kim-Kwang Raymond, "HOW HELPFUL ARE COMPARATIVE REVIEWS FOR PREDICTING PRODUCT DEMAND?" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
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