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Prototypes and strategy: assigning causal credit using fuzzy sets

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Strategies often are stylized on the basis of particular prototypes (e.g. differentiate or low cost) whose efficacy is uncertain often due to uncertainty of complex interactions among its elements. Because of the difficulty in assigning causal credit to a given element for an outcome, the adoption of better practices that constitute strategies is frequently characterized as lacking in causal validity. The authors apply Ragin's (2000) fuzzy logic methodology to identify high performance configurations in the 1989 data set of MacDuffie (1995). The results indicate that discrete prototypes of practices are associated with higher performance, but that the variety of outcomes points to experimentation and search. These results reflect the fundamental challenge of complex causality when there is limited diversity in observed experiments given the large number of choice variables. Fuzzy set methodology provides an approach to reduce this complexity by logical rules that permit an exploration of the simplifying assumptions. It is this interaction between prototypical understandings of strategy and exploration in the absence of data that is the most important contribution of this methodology.

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