Inertial Disruption:The Impact of a New Competitive Entrant on Online Consumer Search
Published 1/1/2009
Author: Wendy W. Moe & Sha Yang
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Executive Summary
Consumer search has always been considered an important stage in the decision-making process and has been the focus of extant research both in the online and the offline environments. This article examines how search is affected by a disruptive market change—namely, that of a new competitive entry. Specifically, the authors examine shifts in consumer tendencies to search incumbent sites after a new competitor enters the market. The context of this study is the online bookstore market pioneered by Amazon in July 1995. Several competitors soon followed, including Barnesandnoble.com in May 1997. This study focuses on the entry of Borders.com, a notable new competitor that launched its site in May 1998. The entry of Borders, a major competitor in the offline environment, marked a dramatic change in the online market, thus providing a rich context to study the effects of competitive entry on consumer search.
The specific research objective of this article is to examine the impact of a new competitive entry on the online consumer search behavior observed through Internet clickstream data. The authors use store visitation as a measure of online search and decompose a shopper’s tendency to search a given Web site into a baseline search preference for that site and an inertial effect of visiting that site. In other words, consumers visit specific store sites partly because they have a preference for that site and partly out of habit (inertia). The authors develop a Bayesian model that measures the preference versus inertial components of visitation and then captures and describes the change in these behavioral components resulting from a new competitive entry. They find that inertia is an important driver in search behavior and is easily disrupted by a new competitive entry. For incumbent competitors in similar industries, the identification of highly inertial customers may serve as a first step in predicting customer attrition and limiting the negative effect of an anticipated competitive entry.
Biography
Wendy W. Moe is Associate Professor of Marketing in the Robert H. Smith School of Business at the University of Maryland. Her research interests lie in modeling online consumer shopping behavior and early sales forecasting. Some of her previous work has focused on developing statistical methods and models for Internet clickstream data. Professor Moe has also developed several early forecasting models that can predict the sales of entertainment products early in their life cycles and, in some cases, even before the actual launch of the product. Her current research focuses on technology-enabled measures of early product success. Her research has appeared in Marketing Science, Journal of Marketing Research, Management Science, Journal of Interactive Marketing, Journal of Consumer Psychology, California Management Review, and Journal of Public Policy & Marketing.
Sha Yang is Associate Professor of Marketing in the Stern School of Business at New York University. She received her PhD in Marketing from Ohio State University. Her primary research focuses on understanding consumer behavior and market competition and developing econometric methods for the analysis of customer- and firm-level data, with a specialized area in hierarchical Bayesian modeling and estimation. Her research has appeared in various journals, including Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, Quantitative Marketing and Economics, Journal of Retailing, Marketing Letters, International Journal of Forecasting, and Journal of Economic Psychology.
Journal of Marketing, Volume 73, Number 1, January 2009
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