Knowledge that Transforms

In their article, Ho, Lim, and Camerer (2006) lead by example. They identify principles from behavioral economics, and rather than simply exhort their readers to pay attention, they actually produce impressive demonstrations of how these principles can be applied to substantive marketing problems. It is easy to argue that these ideas are important, but it is more difficult to demonstrate that importance. Implicitly, Ho, Lim, and Camerer send a message of encouragement: Behaviorally realistic assumptions are not problems to be ignored; they are opportunities. Formal models can capture psychologically realistic concerns. Together, they should be the grist for the next generation of marketing mills.

In Spring 2003, just before Dick Wittink became editor of Journal of Marketing Research (JMR) and I became executive director of the Marketing Science Institute (MSI), Dick visited the University of Texas at Austin to participate in our annual research camp. During his visit, Dick and I decided to act on our shared interest in academicpractitioner collaborative research as soon as we took up our responsibilities at JMR and MSI. The result of that decision was a research competition, a conference, and this special section of JMR. Because Dick passed away before we could complete work on this special section, I would like to include his voice in motivating this special section by quoting from the research competition announcement that he helped craft:

Several studies show that information used to screen alternatives becomes less important than information acquired later in the search process simply because it was used to screen. Experiment 1 shows that the tendency to deemphasize prescreening information leads to systematically different choices for decision makers who screen alternatives compared with decision makers who do not screen alternatives. Additional studies show that screening encourages decision makers to shift their emphasis from prescreening information to postscreening information (Experiment 2). Prescreening information is deemphasized because of the categorization that occurs when people create a consideration set of retained alternatives (Experiments 3 and 4). Together, the results show that a brand's strength of consideration (i.e., how highly an option ranks on screening criteria) may have little influence on the likelihood of it being chosen in a postscreening choice process.

The author comments on the article by Ho, Lim, and Camerer (2006) , which showcases the potential of the new behavioral economics approach to marketing with six theoretical “case studies.” Each case study describes a new model of consumer behavior or competitive marketing interactions. The author raises two general questions about such models. The first question is whether behavioral economics is, in reality, a new form of theoretical psychology, competing with existing psychological models and theories and subject to the same vulnerability to evidence as applies to psychological theory. The author argues that behavioral economists are ambivalent about this point, sometimes claiming psychological realism or plausibility as a necessary modeling requirement and other times introducing assumptions for modeling convenience. The second question is whether the impact of behavioral economics on marketing will be experienced more strongly through the development of new quantitative models or through the exploration of qualitative lessons stimulated by behavioral violations of rationality. It is argued that behavioral economics not only contributes new modeling instruments but, through its documentation of rationality violations or “anomalies,” can provide a rich source of intuitions and insights into consumer psychology as well.

In this article, the authors examine the costs and benefits of action-based learning (i.e., learning that occurs as a by-product of making repeated decisions with outcome feedback). The authors report the results of three experiments that investigate the effects of different decision goals on what is learned and how transferable that learning is across related decision tasks. Contrary to popular wisdom, compared with traditional learning, experiential learning is likely to be a risky proposition because it can be either accurate and efficient or errorful and biased.

This research presents a feature-based statistical model and subsequently explores the degree to which similarity perceptions between two advertisements can be decomposed and explained by a “weighted-and-summed” distance measure, computed on the advertisements' executional elements, after controlling for familiarity and viewers' attitudinal responses toward the advertisements. Furthermore, the authors obtain empirical findings in two major areas: First, variation in similarity ratings can be explained by the advertisements' features, a finding of potential importance for advertisement construction. Second, some, but not all, executional elements that have been shown (in the literature) to drive recall and persuasion are effective at driving perceptions of similarity. This is of practical importance because managers want their advertisements not only to be liked and remembered but also (possibly) to be perceived as similar (or dissimilar) to those for other products. In particular, an understanding of which items drive which constructs (recall and persuasion, or similarity) can contribute to a more effective overall marketing strategy.

Household life cycle has been widely used as a determinant of consumer behavior and a basis for market segmentation. However, there is considerable disagreement about how life stages should be defined and how households progress through these stages. Existing studies use a priori definitions, which are tested on a cross-sectional survey of households collected at a single point in time and thus cannot reveal the real dynamics of the household life cycle. The Panel Study of Income Dynamics provides longitudinal data on household composition in the United States for a period of 34 years; the authors use this to identify empirically the most typical stages and paths that U.S. households have followed since 1968. They develop a hidden Markov model in which the stages of the household life cycle are taken as latent, unobservable states that are uncovered from the manifest household demographic profiles over the 34 years, assuming that households evolve through these latent stages following a first-order Markov process. The authors apply their results to classify members of another panel (Consumer Expenditure Survey) into life stages, which enables them to study the impact of the household life cycle on households' budgetary allocations, providing a comprehensive analysis of lifestyles (through expenditure patterns) over the household life cycle.