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...

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.

We argue for a broadened approach to brokerage by distinguishing between brokerage emphasizing a particular structural pattern in which two otherwise disconnected alters are connected through a third party ("brokerage structure") and the social behavior of third parties ("brokerage process"). We explore a processual view of brokerage by examining three fundamental strategic orientations toward brokerage: conduit, tertius gaudens, and tertius iungens that occur in many different forms and combinations. This processual view is especially relevant in increasingly complex and dynamic environments where brokerage behavior is highly varied, intense, and purposeful, and has theoretical implications for studying multiplexity, heterogeneity, and brokerage intensity AU:2 .

Much is known about the importance of learning and some of the distinct learning processes that organizations use (e.g., trial-and-error learning, vicarious learning, experimental learning and improvisational learning). Yet surprisingly little is known about whether these processes combine over time in ordered ways since most research on learning explores one particular process. Using theory elaboration and theory building methods and data on the accumulated country entries of entrepreneurial firms, we address this gap. Our core contribution is an emergent theoretical framework that develops the concept of learning sequences. We find that learning sequences exist and are influenced by initial conditions. We also find that learning sequences evolve in fundamentally distinct ways over time and with repeated use. Finally, data show how different learning sequences differentially effect performance, both in the shorter-term as well as in the longer-term, suggesting that it matters which learning processes are used and when. Overall, our findings on learning sequences have important implications for learning theory, international entrepreneurship, and the growing literature on process management.

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Using a multiple-case, inductive study of eight technology collaborations between ten organizations in the global computing and communications industries between 2001 and 2006 this paper examines why some interorganizational relationships produce technological innovations while others do not. Comparisons of more and less innovative collaborations show that highperforming collaborative innovation involves more than possessing the appropriate structural antecedents (e.g., R&D capabilities, social embeddedness) suggested by prior alliance studies. Rather, it also involves dynamic organizational processes associated with collaboration partners' leadership roles that solve critical innovation problems related to recombination across boundaries. While dominating and consensus leadership processes are associated with less innovation, a rotating leadership process is associated with more innovation. It involves alternating decision control that accesses the complementary capabilities of both partner organizations, zigzagging objectives that engender deep and broad technological search for potential innovations, and fluctuating network cascades that mobilize different participants who bring variable inputs to recombination. The paper also discusses recombination mechanisms in the organization of collaborative innovation, variations in the performance of dynamic interorganizational ties, and how organizations develop symbiotic relationships that overcome the tendency of long-lived relationships toward inertia.

This paper examines how organizations collaborate with multiple partners, such as when they develop innovative and complex product platforms like smartphones, servers, and MRI machines that rely on technologies developed by organizations in three or more sectors. Research on multipartner alliances often treats them as a collection of independent dyads, neglecting the possibility of third-party influence and interference in dyads that can inhibit innovation. Using a multiple-case, inductive study of six groups, each composed of three organizations engaged in technology and product development in the computer industry, I examine the collaborative forms and processes that organizations use to innovate with multiple partners in groups. Groups that used the collaborative forms of independent parallel dyads or single unified triads generated mistrust and conflict that stemmed from expectations about third-party participation and overlapping roles and thus had low innovation performance and weaker ties. Other groups avoided these problems by using a dynamic collaboration process that I call ''group cycling,'' in which managers viewed their triad as a small group, decomposed innovative activities into a series of interlinked dyads between different pairs of partners, and managed third-party interests across time. By temporarily restricting participation to pairs, managers chose which ideas, technologies, and resources to incorporate from third parties into single dyads and ensured that the outputs of multiple dyads were combined into a broader innovative whole.