The Impact of Shopbot Use on Prices and Price Dispersion: Evidence from Online Book Retailing

Zhulei Tang, Purdue University
Michael D. Smith, Carnegie Mellon University
Alan Montgomery, Carnegie Mellon University


Abstract or Description

The growth of Internet price search tools, notably shopbots, has reduced consumers’ search costs for price and some product characteristics. While a variety of analytic models predict that increased consumer search through shopbots will lower price levels among competing retailers, there is no consensus in the empirical literature as to whether price dispersion will increase or decrease in response to increased consumer search through shopbots. Moreover, there are no papers that have empirically tested these predictions using direct observation of variation in shopbot use over time. This paper examines the impact of changes in shopbot use over time on pricing behavior in the Internet book market. Using price data obtained from a leading shopbot, combined with clickstream data on shopbot usage from August 1999 to July 2001, we show that an increase of 1% in shopbot use is correlated with a $0.41 decrease in price levels, after controlling for product and market characteristics. We also show that price dispersion decreases with shopbot use in a non-linear fashion. These findings are robust to controlling for potential simultaneity bias and the possible influence of prominent retailers, bestsellers, seasonality, and structural changes in the online book industry