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Information Processing Pattern and Propensity to Buy: An Investigation of Online Point-of-Purchase Behavior

Ofer Mintz (), Imran S. Currim () and Ivan Jeliazkov ()
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Ofer Mintz: E. J. Ourso College of Business, Louisiana State University, Baton Rouge, Louisiana 70803
Imran S. Currim: Paul Merage School of Business, University of California, Irvine, Irvine, California 92697

Marketing Science, 2013, vol. 32, issue 5, 716-732

Abstract: The information processing literature provides a wealth of laboratory evidence on the effects that the choice task and individual characteristics have on the extent to which consumers engage in alternative-based versus attribute-based information processing. Less attention has been paid to studying how the processing pattern at the point of purchase is associated with a consumer's propensity to buy in shopping settings. To understand this relationship, we formulate a discrete choice model and perform formal model comparisons to distinguish among several possible dependence structures. We consider models involving an existing measure of information processing, PATTERN; a latent variable version of this measure; and several new refinements and generalizations. Analysis of a unique data set of 895 shoppers on a popular electronics website supports the latent variable specification and provides validation for several hypotheses and modeling components. We find a positive relationship between alternative-based processing and purchase, as well as a tendency of shoppers in the lower price category to engage in alternative-based processing. The results also support the case for joint modeling and estimation. These findings can be useful for future work in information processing and suggest that likely buyers can be identified while engaged in information processing prior to purchase commitment, an important first step in targeting decisions.

Keywords: information processing; discrete choice; Markov chain Monte Carlo (MCMC); digital strategy (search for similar items in EconPapers)
Date: 2013
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