We describe when and how to use simulation methods in theory development. We develop a roadmap that describes theory development using simulation and position simulation in the "sweet spot" between theory-creating methods, such as multiple case inductive studies and formal modeling, and theory-testing methods. Simulation strengths include internal validity and facility with longitudinal, nonlinear, and process phenomena. Simulation's primary value occurs in creative experimentation to produce novel theory. We conclude with evaluation guidelines.Simulation is an increasingly significant methodological approach to theory development in the literature focused on strategy and organizations (e.g., Adner, 2002;Lant & Mezias, 1990;Repenning, 2002;Rivkin & Siggelkow, 2003;Zott, 2003), Indeed, several influential research efforts (e.g., Cohen, March, & Olsen, 1972;March, 1991) have used simulation as their primary method. Yet while simulation has become an important methodology, its value for theory development remains clouded and even controversial.On the one hand, some argue that simulation methods contribute effectively to theory development. For example, simulation can provide superior insight into complex theoretical relationships among constructs, especially when challenging empirical data limitations exist (Zott, 2003). It can also provide an analytically precise means of specifying the assumptions and theoretical logic that lie at the heart of verbal theories (Carroll & Harrison, 1998;Kreps, 1990). In addition, simulation can clearly reveal the outcomes of the interactions among multiple underlying organizational and strategic processes, especially as they unfold over time (Repenning, 2002). From these perspectives, simulation can be a powerful method for sharply specifying and extending extant theory in useful ways.On the other hand, some researchers maintain that simulation methods often yield very little in terms of actual theory development. They suggest that simulations are simply "toy models" of actual phenomena, in that they either replicate the obvious or strip away so much realism that they are simply too inaccurate to yield valid theoretical insights (Chattoe, 1998;Fine & Elsbach, 2000). For example, simulation research is usually based on at least some clearly unrealistic assumptions, such as zero search costs (Rivkin, 2000) and all strategic rules are effective (Davis, Eisenhardt, & Bingham, 2007). In addition, simulation constructs are often "measured" by empirically distant approaches, such as "0" and "1" bit strings as representations of organizations (Bruderer & Singh, 1996) and strategies (Rivkin, 2001). The results of research using simulation methods can also be dynamically indeterminate and overly complex (Fichman, 1999). From these perspectives, the value of simulation methods for theoretical development is tenuous.The controversy surrounding the value of simulation methods for theory development partially arises, in our view, from a lack of clarity about the method and its related link to th...

While much research indicates that organizational processes are learned from experiences, surprisingly little is known about what is actually learned. Using a novel method to measure explicit learning, we track the learned content of six technology‐based ventures from three diverse countries as they internationalize. The emergent theoretical framework indicates that firms learn heuristics. These heuristics have a common structure centered on opportunity capture and are learned in a specific developmental order. This results in a deliberately small, yet increasingly strategic, portfolio of heuristics. Broadly, we contribute to the psychological foundations of strategy by highlighting the rationality of heuristics as strategy, capability creation as the cognitive transition from novice to expert heuristics, and simplification cycling as a critical dynamic capability for sustaining competitive advantage. Copyright © 2011 John Wiley & Sons, Ltd.

Using computational and mathematical modeling, this study explores the tension between too little and too much structure that is shaped by the core tradeoff between efficiency and flexibility in dynamic environments. Our aim is to develop a more precise theory of the fundamental relationships among structure, performance, and environment. We find that the structure-performance relationship is unexpectedly asymmetric, in that it is better to err on the side of too much structure, and that different environmental dynamism dimensions (i.e., velocity, complexity, ambiguity, and unpredictability) have unique effects on performance. Increasing unpredictability decreases optimal structure and narrows its range from a wide to a narrow set of effective strategies. We also find that a strategy of simple rules, which combines improvisation with low-to-moderately structured rules to execute a variety of opportunities, is viable in many environments but essential in some. This sharpens the boundary condition between the strategic logics of positioning and opportunity. And juxtaposing the structural challenges of adaptation for entrepreneurial vs. established organizations, we find that entrepreneurial organizations should quickly add structure in all environments, while established organizations are better off seeking predictable environments unless they can devote sufficient attention to managing a dissipative equilibrium of structure (i.e., edge of chaos) in unpredictable environments.

Our purpose is to clarify the microfoundations of performance in dynamic environments. A key premise is that the microfoundational link from organization, strategy, and dynamic capabilities to performance centers on how leaders manage the fundamental tension between efficiency and flexibility. We develop several insights. First, regarding structure, we highlight that organizations often drift toward efficiency, and so balancing efficiency and flexibility comes, counterintuitively, through unbalancing to favor flexibility. Second, we argue that environmental dynamism, rather than being simply stable or dynamic, is a multidimensional construct with dimensions that uniquely influence the importance and ease of balancing efficiency and flexibility. Third, we outline how executives balance efficiency and flexibility through cognitively sophisticated, single solutions rather than by simply holding contradictions. Overall, we go beyond the caricature of new organizational forms as obsessed with fluidity and the simplistic view of routines as the microfoundation of performance. Rather, we contribute a more accurate view of how leaders effectively balance between efficiency and flexibility by emphasizing heuristics-based “strategies of simple rules,” multiple environmental realities, and higher-order “expert” cognition. Together, these insights seek to add needed precision to the microfoundations of performance in dynamic environments.

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