Add to Calendar 10/09/2012 10:00:00 10/09/2012 11:30:00 15 Marketing & Policy Studies-Faculty Candidate Seminar Measurement of Interactions in Non-linear Marketing Models: Some Observations and Precautions in the Context of Interaction Effect of Critics Rating and Economic Environment on Movie Attendance ABSTRACT: In nonlinear models, a typical way to determine the interaction effect between variables is to linearize the model for estimation purpose, add an interaction term, and then use the estimate of the parameter of the interaction term to determine the presence (or absence) and the extent of the interaction effect. In this paper, we show that in many cases such an approach is problematic. By design, non?linear models inherently include interactions, and as a result the interaction coefficient does not capture the full extent and complexity of the interaction effect. After exploring the complexities of interaction effects in non?linear models, we outline methods to estimate and understand the interaction effects in two widely used marketing models. To illustrate our method, we use 26 years of weekly US movie market data to test the interactions between critics’ ratings and consumer sentiment about economic conditions on box office attendance. In addition to find Peter B. Lewis Building 218, 11119 Bellflower Road, Cleveland, OH, 44106-7235, United States Weatherhead School of Management rxe73@case.edu MM/DD/YYYY

Marketing & Policy Studies-Faculty Candidate Seminar

Speaker(s): Tirtha Dhar - Assistant Professor of Marketing, Sauder School of Business, Univ of British Columbia

Date & Time: Tuesday, Oct. 9, 2012 from 10 a.m. to 11:30 a.m. (Eastern)

Measurement of Interactions in Non-linear Marketing Models: Some Observations and Precautions in the Context of  Interaction Effect of Critics Rating and Economic Environment on Movie Attendance

 

ABSTRACT:  In nonlinear models, a typical way to determine the interaction effect between

variables is to linearize the model for estimation purpose, add an interaction term, and

then use the estimate of the parameter of the interaction term to determine the presence

(or absence) and the extent of the interaction effect. In this paper, we show that in many

cases such an approach is problematic. By design, non?linear models inherently include

interactions, and as a result the interaction coefficient does not capture the full extent and

complexity of the interaction effect. After exploring the complexities of interaction effects

in non?linear models, we outline methods to estimate and understand the interaction

effects in two widely used marketing models. To illustrate our method, we use 26 years of

weekly US movie market data to test the interactions between critics’ ratings and consumer

sentiment about economic conditions on box office attendance. In addition to finding that

movie attendance is counter?cyclical, an expected but not previously documented result,

we also show, contrary to popular belief, that critics’ ratings have larger impact during

economic downturns than during periods of economic expansion. 

Location
Peter B. Lewis Building 218
11119 Bellflower Road
Cleveland, OH 44106-7235
United States

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