Date of this Version
This paper examines search across competing e-commerce sites. By analyzing panel data from over 10,000 Internet households and three commodity-like products (books, compact discs (CDs), and air travel services), we show that the amount of online search is actually quite limited. On average, households visit only 1.2 book sites, 1.3 CD sites, and 1.8 travel sites during a typical active month in each category. Using probabilistic models, we characterize search behavior at the individual level in terms of (1) depth of search, (2) dynamics of search, and (3) activity of search. We model an individual's tendency to search as a logarithmic process, finding that shoppers search across very few sites in a given shopping month. We extend the logarithmic model of search to allow for time-varying dynamics that may cause the consumer to evolve and, perhaps, learn to search over time. We find that for two of the three product categories studied, search propensity does not change from month to month. However, in the third product category we find mild evidence of time-varying dynamics, where search decreases over time from already low levels. Finally, we model the level of a household's shopping activity and integrate it into our model of search. The results suggest that more-active online shoppers tend also to search across more sites. This consumer characteristic largely drives the dynamics of search that can easily be mistaken as increases from experience at the individual level.
Originally published in Management Science © 2004 INFORMS
This is a pre-publication version. The final version is available at http://dx.doi.org/10.1287/mnsc.1040.0194
electronic commerce, dynamic consumer search, stochastic models, consumer behavior
Johnson, E. J., Moe, W. W., Fader, P. S., Bellman, S., & Lohse, G. L. (2004). On the Depth and Dynamics of Online Search Behavior. Management Science, 50 (3), 299-308. http://dx.doi.org/10.1287/mnsc.1040.0194
Behavioral Economics Commons, Business Administration, Management, and Operations Commons, Business Analytics Commons, E-Commerce Commons, Marketing Commons, Statistics and Probability Commons, Technology and Innovation Commons
Date Posted: 15 June 2018
This document has been peer reviewed.