Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?

Authors:
Nan Hu;Hasan Cavusoglu;Ling Liu;Chenkai Ni
Affiliations:
University of Wisconsin Eau Claire;University of British Columbia;University of Wisconsin Eau Claire;National University of Singapore
Venue:
ACM Transactions on Management Information Systems (TMIS)
Year:
2012

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Abstract

By using online retail data collected from Amazon, Barnes & Nobel, and Pricegrabber, this paper investigates whether online vendors’ pricing decisions fully reflect the information contained in various components of customers’ online reviews. The findings suggest that there is inefficiency in vendors’ pricing decisions. Specifically, vendors do not appear to fully understand the incremental predictive power of online reviews in forecasting future sales when they adjust their prices. However, they do understand demand persistence. Interestingly, vendors reduce price if the actual demand is higher than the expected demand (positive demand shock). This phenomenon is attributed to the advertising effect suggested in previous literature and the intense competitiveness of e-Commerce. Finally, we document that vendors do not change their prices directly in response to online reviews; their response to online reviews is through forecasting consumer’s future demand.