Knowledge that Transforms

The author describes his career in marketing research, which involves a series of nonacademic positions. He argues that the commercial market research environment provides challenges that motivate methodological innovation, and he discusses the development of adaptive conjoint analysis in response to client problems. He suggests that young people should keep their options open, avoid early specialization, and follow their interests.

This article is a response to Richard Johnson's (2005) memoir. It notes some of the important contributions that Johnson has made to marketing research and reflects on his unique personal and professional characteristics.

The author believes that marketing would be strengthened by closer relationships between academics and practitioners. He argues that the commercial market research environment can be stimulating for methodological development, and he suggests that everyone would benefit if academics and practitioners had experience in both environments.

In the usual multinomial choice model, consumers choose to use “actual value” information; that is, utilities are continuous functions of product attributes (e.g., choices depend on actual magnitudes of price differences). The authors propose an alternative model in which consumers use only “ordered value” information; that is, utilities are functions only of the relative orderings of the attributes' values across alternatives (e.g., choices depend only on the price ordering and not on actual prices). The ordered value model is attractive because it fits well with psychological evidence that consumers often favor decision mechanisms that are cognitively less demanding. Using a supermarket shopper panel data set, the authors evaluate four models in which (1) all consumers use actual values; (2) all consumers use ordered values; (3) some consumers use actual values all the time, and some consumers use ordered values all the time; and (4) all consumers use both actual values and ordered values but with different propensities. In the analysis, the ordered value model finds stronger support than the actual value model: Model 1 outperforms Model 2; in the two hybrid choice models (Models 3 and 4), ordered value processing is more prevalent than actual value processing. These results suggest that consumers in some product categories engage more heavily in ordered value processing than in actual value processing.

Richard Johnson has had a remarkable career in an era that integrated the use of computers and mathematical models into marketing research. This article summarizes his contributions in terms of theoretical advances, practical solutions, and the development of a culture that links the academic and practitioner worlds. The author speculates on the personality traits that made Johnson so successful and ways that his critical roles can be fulfilled by others in the future.

This research examines the dynamic process of inference updating. The authors present a framework that delineates two mechanisms that guide the updating of personality trait inferences about brands. The results of three experiments show that chronics (those for whom the trait is accessible) update their initial inferences on the basis of the trait implications of new information. Notably, nonchronics (those for whom the trait is not accessible) also update their initial inferences, but they do so on the basis of the evaluative implications of new information. The framework adds to the inference-making literature by uncovering two distinct paths of inference updating and by emphasizing the moderating role of trait accessibility. The findings have direct implications for marketers attempting to understand the construction of brand personality, and they emphasize the constantly evolving nature of brand perceptions and the notion that both the consumer and the marketer have important roles to play in this process.

Marketing models are often intended to guide managers to make decisions. In this rejoinder, the author reviews three questions that must be addressed before a model can be established as useful for policy analysis.

ance of aggregation biases, and so on. Franses (2005) provides an inventory of commonly used diagnostic tests. Franses (see Table 2 for 1998–2000 and Table 3 for 2001– 2003) also shows that there is a paucity of actual use of diagnostic tests in articles published in JMR. Such paucity may suggest that researchers have a disincentive to conduct all relevant tests. For example, it might be imagined that though researchers prefer to publish a valid model rather than one that is invalid, publishing an invalid model is still preferred to not publishing one, especially if it is difficult for readers to detect model deficiencies. Given data constraints, it is virtually impossible for researchers to accommodate all possible nuances. Thus, researchers rely on theories and experience to decide which aspects are the most critical to include in a model. With accumulating empirical evidence in the literature, the expectation is that future modeling efforts will be more informed and thus likely to provide increasingly useful (i.e., valid and reliable) results. However, I urge researchers to consult the checklist that Franses provides and to conduct all diagnostic tests when appropriate. In applied econometrics, there are three possible reasons for specific tests not to be used. First, researchers may argue convincingly that a test does not apply in the model’s context. For example, testing the null hypothesis of zero autocorrelation in the error term in a model of purely cross-sectional data is irrelevant. (Separately, I note that the use of generalized least squares rather than ordinary least squares to accommodate serial correlation in time series data is a technical correction that is not convincing unless the researcher can justify how serial correction logically arises for an otherwise correctly specified model.) Second, some tests may not yet be available for cases other than linear models and normally distributed errors. Third, researchers may argue that the violation of a particular assumption does not invalidate the substantive results. For example, consistency of ordinary least squares does not require normality of the error term. In all other cases, it is the researchers’ responsibility to conduct and show appropriate diagnostic tests. The model cannot be assumed to be valid unless proper diagnostic tests fail to reject the assumptions. Because all models are incomplete representations of reality, a central question is: How can a model that is superior to a meaningful alternative (e.g., judgment or a simpler representation than the proposed model) be obtained? If the interest of the researcher is to discover how marketing activities affect purchases or other responses (as in a “causal” model), any comparison to a model without marketing variables seems useless. Still, it might be argued that Beginning with the August 2003 issue, Journal of Marketing Research (JMR) has published one or more comments on the lead article, followed by a rejoinder. I have asked experts to provide commentary on one article in each issue that I believe has especially relevant content for researchers and managers. In the current issue, the lead article is an invited paper for which I also asked several experts to provide comments. In all cases, I provide an opportunity for the author(s) of the original article to prepare a rejoinder in the same issue. My hope is that such related reflections and commentaries on a current topic will enhance the value of JMR to readers. Although I see no reason to entice authors to express strong disagreements about specific issues, I expect that such collections of articles will enable readers to become more informed about differences in perspectives that researchers with substantial expertise and experience have on important issues.

Models of distribution channels have defined retailer and manufacturer pricing decision variables in different ways, such as absolute retail price or absolute retail margin and absolute manufacturer price or absolute manufacturer margin. This article examines whether this choice of definition affects the equilibrium outcomes from such models. It shows that the equilibrium outcomes do not change with these definitions if manufacturers are modeled as Stackelberg pricing leaders to their retailer. However, if manufacturers are modeled as Bertrand-Nash competitors to their retailer or as Stackelberg pricing followers to their retailer, the equilibrium outcomes change depending on how the retailer's pricing decision variables are defined. Moreover, if in these two cases manufacturers and retailer are allowed to define their own pricing decision variables, then (1) manufacturers are indifferent about choosing among absolute prices, absolute margins, and percentage margins, but (2) the retailer chooses percentage margins. These results have implications for both theoretical and empirical models of price competition in distribution channels.