Search results

This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1982

Can firms and coevolutionary groups suffer from too much interdependent complexity? Is complexity theory an alternative explanation to competitive selection for the emergent order apparent in coevolutionary industry groups? The biologist Stewart Kauffman suggests a theory of complexity catastrophe offering universal principles explaining phenomena normally attributed to Darwinian natural selection theory. Kauffman's complexity theory seems to apply equally well to firms in coevolutionary pockets. Based on complexity theory, four kinds of complexity are identified. Kauffman's "NK[C] model" is positioned "at the edge of chaos" between complexity driven by "Newtonian" simple rules and rule-driven deterministic chaos. Kauffman's insight, which is the basis of the findings in this paper, is that complexity is both a consequence and a cause. Multicoevolutionary complexity in firms is defined by moving natural selection processes inside firms and down to a "parts" level of analysis, in this instance Porter's value chain level, to focus on microstate activities by agents. The assumptions of stochastically idiosyncratic microstates and coevolution in firms are analyzed. Competitive advantage, as a dependent variable, is defined in terms of Nash equilibrium fitness levels. This allows a translation of Kauffman's theory to firms, paying particular attention to (1) how value chain landscapes might be modeled, (2) assumptions underlying Kauffman's models making them amenable to firms, and (3) a delineation of seven of Kauffman's computational experiments. As part of the translation, possible parallels between the application of complexity catastrophe theory to coevolutionary pockets and studies by institutional theorists and social network analysts are discussed. The models derive from spin-glass microstate models resulting in Boolean games. Kauffman's Boolean statistical mechanics is introduced in developing the logic underlying the somewhat simplified NK[C] model. The model allows the use of computational experiments to better understand how the dependent variable—value chain fitness—is affected by changes in the number of internal interdependencies K, the number of coevolutionary links with opponents C, the size of the coevolutionary pocket S, and the number of simultaneous adaptive changes, among other things. Various computational experiments are presented that suggest strategic organizing approaches most likely to foster competitive advantage. High or low Nash equilibrium fitness levels are shown to result from internal and external coevolutionary densities as a function of links among value chain competencies within a firm and between a firm and an opponent. Complexity phenomena appear to suggest a number of expected (and thus validating) and surprising strategies with respect to complex organizational interdependencies. For example, moderate complexity fares best and external coevolutionary complexity sets an upper bound to advantages likely to be gained from internal complexity. Various complexity "lessons" are discussed. Models such as the NK[C] could offer insights into strategic organizing.

Positing that organizational phenomena result from both individual human intentionality and natural causes independent of individuals' intended behavior, the need for a quasi-natural organization science is identified. The paradigm war is defined in terms of positivism and postpositivism, with the suggestion that a more relevant epistemology might be scientific realism. The current unconstructive paradigm proliferation is seen as resulting from an underlying cause, idiosyncratic organizational microstates, phenomena identified by postmodernists. The article develops quasi-natural organization science as an antidote to multiparadigmaticism by recognizing that mathematically, computationally, and experimentally intense twentieth century natural sciences all have microstate idiosyncrasy assumptions similar to those postmodernists suggest are true of organizational phenomena. By framing a quasi-natural organization science focusing on microstates, my intent is not to deny the relevance of either intentionality and subjectivity or natural science and objectivity. The article attacks the microstate idiosyncrasy problem on four frontiers: micro- and macroevolutionary theory, semantic conception epistemology, analytical mechanics, and complexity theory. The first frontier develops the natural side of quasi-natural organization science to explain natural pattern or order. This "order" arguably results from multilevel coevolutionary behavior in a selectionist competitive context in the form of multi-level selectionist effects. The second frontier reviews the historic role of idealized models, as understood by historical realists and the "semantic conception of theories"—idealized constructs such as point masses or the rational actor assumption—that currently successful sciences, such as physics and economics, drew upon early in their life-cycles to sidestep the idiosyncrasy problem. Organization scientists are encouraged to develop theories in terms of idealized models. The third frontier attends to the role of 'instrumental conveniences' as essential constructs in the early life-cycle stages of sciences and the importance of studying rates. For example, a construct such as a pressure vessel acts as a container translating idiosyncratic gas particle movements into a directed pressure stream where particles emerge at some rate. Drawing on Sommerhoff's "directive correlation" concept as an analogous "container" in firms, this section argues that such containers can be used in organizational analysis to translate idiosyncratic microstates into probabilistic rates of occurrence, thereby allowing the use of intrafirm rate models and Hempel's deductive-statistical model of explanation. An example is given showing how human resource variables can be translated into rate concepts and then used in the context of the directive correlation and the deductive statistical model. The fourth frontier draws on complexity theory as a computational/analytical approach that directly incorporates idiosyncrasy by use of dynamical (nonlinear) methods. Complex adaptive systems, kinds of complexity, the causal role of complexity, and levels of adaptive tension likely to foster self-organization are discussed. An example shows how a complexity theory approach differs from a conventional explanation of why participative management decision making styles have failed to proliferate. The combined effect of rate dynamics, statistical mechanics, and dynamical analysis lays the platform for a realist, predictive, and generalizable quasi-natural organization science, thereby offering a possible resolution of the paradigm war. The mitigation of idiosyncrasy effects allows a reemphasis of background laws in organization science, as opposed to the further emphasis of contingent details advocated by post-modernists.

Social entrepreneurship (SE) is increasingly popular in academia and practice, but unified theoretical explanations about the performance of social entrepreneurship firms (SEFs) is missing (Santos, 2012). This deficiency motivates us to theorize about SE from a complexity science perspective. We draw from complexity science to analyze and explain how SEFs emerge, achieve performance, and grow. We link complexity science with SE so as to add explanatory value as well as offering guidelines for better SEF performance toward achieving social objectives while avoiding the chasm of chaos. Our theoretical framework offers complexity insights for building effective networks, and accountability, as well as for improving trust, legitimacy, and sound governance. Drawing on complexity theory to better explain the key elements necessary for improving SEFs' performance and growth, enhances the probability of meeting the challenge of the so-called 'double bottom-line': achieving continuous positive social impacts while attaining financial health.

Although normal distributions and related current quantitative methods are still relevant for some organizational research, the growing ubiquity of power laws signifies that Pareto rank/frequency distributions, fractals, and underlying scale-free theories are increasingly pervasive and valid characterizations of organizational dynamics. When they apply, researchers ignoring power-law effects risk drawing false conclusions and promulgating useless advice to practitioners. This is because what is important to most managers are the extremes they face, not the averages. We show that power laws are pervasive in the organizational world and present 15 scale-free theories that apply to organizations. Next we discuss research implications embedded in Pareto rank/frequency distributions and draw statistical and methodological implications.