A probit model of choice dynamics

A probit model of choice dynamics

0.00 Avg rating0 Votes
Article ID: iaor1993490
Country: United States
Volume: 11
Issue: 2
Start Page Number: 189
End Page Number: 206
Publication Date: Mar 1992
Journal: Marketing Science
Authors: ,
Keywords: differential equations, behaviour, demand, decision
Abstract:

There are many products which are repeatedly purchased by consumers. In such cases it is likely that choice history, that is the sequence of choices made in the past, as well as marketing variables affect subsequent choice decisions. Attempts to model the effects of choice history have been generally based on the inclusion of variables that represent brand loyalty and/or variety seeking behavior. In this paper the authors present a model of dynamic choice behavior which is more general and incorporates four characteristics. The first characteristic labeled preference reinforcement and preference reduction represents loyalty and variety seeking. The second is the short-term reluctance of a consumer to move from the current brand (inertia) or the willingness to move to another brand (mobility). The third characteristic captures the effect of repetitive consumption (the long term effect) on inertia and mobility. The fourth characteristic incorporates the similarity or dissimilarity of choice alternatives. This is important in a dynamic model because choice on the current purchase occasion can be affected by whether a similar or dissimilar alternative was chosen on the previous occasion. Similarities of alternatives are represented in terms of distances. The effect of price on choice behavior is also modeled. Individual-level purchase data from a consumer panel are used to estimate a covariance probit and an independent probit specification of the model. From a substantive perspective the model gives interesting insights into the dynamics of choice behavior. The model predicts switches better than a benchmark model which incorporates only loyalty. In addition, it is superior to three benchmark models in overall predictive ability.

Reviews

Required fields are marked *. Your email address will not be published.