A Constrained Finite Mixture Structural Equation Methodology for Empirically Deriving Strategic Types

Miles and Snow (1978) originally envisioned strategy as an agglomeration of decisions by which a firm group or strategic business unit (SBU) aligns itself with its environment. Accordingly, these authors conceptualized a classification of “strategic types” based on SBUs’ patterns of decisions into the now familiar Prospector-AnalyzerDefender-Reactor (P-A-D-R) framework. The Miles and Snow framework has been criticized by Hambrick (1983,1984) and others for ignoring industry and environmental factors, for lacking explanatory or predictive power in specific industries, and for being somewhat naively determined methodologically speaking. In this light, we devise a constrained latent structure structural equation based methodology to empirically estimate an “optimal” typology using data obtained from 216 SBUs in the U.S. as an illustration. This procedure allows for more comprehensive modeling and grouping of entities and simultaneously provides a diagnosis of the sources of heterogeneity producing the derived strategic groups. In addition, environmental and industry specific factors can be easily incorporated. Results show that our derived four mixed-type solution dominates the four-group P-A-D-R Miles and Snow classification (as well as a number of other nested model solutions) in terms of objective statistical fit criteria for this dataset, suggesting a more contingency-driven strategic stance adopted by these SBUs.