Demystifying Variance in Performance: Toward a Multilevel Longitudinal Perspective of Strategic Management

Drawing upon complexity theory, this dissertation contributes both to inquiry on relative importance of business–unit, corporation, industry, and year effects on firm performance and to development of a multilevel longitudinal perspective of strategy. Empirically, this dissertation generates important new insights about variation in performance. This dissertation is the first to (1) capture substantial stable corporation–industry interaction effects that were confounded with stable effects of business unit, corporation, and industry in results of previous studies, (2) demonstrate that stable effects of corporation, industry, corporation–industry interaction, taken together, are of similar relative magnitude to that of stable BU effects, (3) reveal that random and nonlinear year effects are important and significant, and (4) locate all categorical sources of performance variability. Additionally, utilizing Markov Chain Monte Carlo methods in Bayesian framework, this dissertation provides inference statistics for the estimated relative effects of these components. Theoretically, this research provides a broad framework to accommodate existing theories of strategy, leading to a multilevel longitudinal perspective of strategic management. Additionally, findings of this research add support for complexity theory