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This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.

The focus of this study is on analyzing influencing factors of antisocial travel-related behaviors – in particular road rage. Building on the concept of redirection, the current chapter develops a theory of natural and planned redirection to derive starting points for demarketing antisocial behaviors. A fuzzy set qualitative comparative analysis (fsQCA) with survey data from 6,811 consumers from the DDB Life Style Study is used to gain insights into the individuals behind road rage. Results show that specific kinds of anti- and prosocial behavior associate with high and low levels of road rage, respectively. The study finds that these prosocial behaviors may function as natural redirection mechanisms and prevent or reduce road rage. Thereby, the findings extend previous analyses of road rage and allow for deriving theoretical and policy implications.