Morrison, Donald G.; Silva-risso, Jorge
June 1995
Marketing Science;1995 Part 2 of 2, Vol. 14 Issue 3, pG61
Academic Journal
If marketing science is to advance, then some generalizations on the effects of marketing mix variables across brands, product categories, and geographical regions are vital. Useful generalizations can only come from empirical studies. Whenever researchers are looking for empirical generalizations in marketing, they should explicitly consider their data as coming from the model "Observed Value = True Score + Error." The empirical generalization in question should then be based on the latent true scores. This necessary condition is especially important when the unit of analysis is the individual consumer. Error estimates based on asymptotic properties come from a likelihood function that assumes an explicit functional form, which is obviously not exactly correct. Therefore, it would not be surprising that error estimates are biased downwards due to model misspecification. Furthermore, while comparing geographical areas it is very likely that researchers are omitting key variables that differ over those areas. The substantial difference in couponing activity across cities is one such example. Researchers have no way of quantifying these distortions, but it is their opinion that a literal reading of the Wittink et al. results will vastly overstate the true variability of price elasticities across the ten cities.


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