This project will build a theoretical framework to address how an entrepreneur's initial resource endowments, expectations for novelty, engagement with the market, and the general market environments interact to facilitate the emergence of extremely successful and influential new ventures. Ventures like this are outliers because they are rare and have disproportionate impact at multiple levels. Moreover, these outlier ventures are very different in both their inputs and outcomes than what most would characterize as typical. The project's framework will enhance knowledge of how organizational systems grow, improve performance, and achieve outlier outcomes over time in changing competitive environments. Because this framework is built on data from multiple complex social systems, it is generalizable for and capable of improving the outcomes of individuals, teams, firms, industries, and nations.
The overarching research focus of this project is to identify the primary drivers of outlier outcomes in entrepreneurship. Building a theory about the emergence of outliers is difficult under the assumption of normal, bell-shaped distributions of inputs and outcomes. Under the normal distribution assumption, outliers are usually cleansed from the analysis-either deleted or mathematically transformed-to reduce their influence. This project moves beyond the normal distribution assumption and uses theoretical assumptions and methods from complexity science. This theoretical approach has two advantages. First, a complexity perspective assumes that in relatively unconstrained environments, outcomes will likely be distributed according to a power law. This distribution is vital because traditional theory building and testing methods assume that the probability of an extreme event is effectively zero. A power law assumption can more adequately reflect the empirical distributions found in studies of emerging ventures. Second, a complexity perspective suggests that a system's outcomes are primarily the result of an interconnected combinations of inputs, including 1) an agent's initial endowment of resources, 2) the agent's rules for engagement with other agents based on expectations for future outcomes, and 3) the quantity and quality of environmental resources. Within this perspective, the emergence of a system from non-existence to existence occurs as a nonlinear, threshold-based process. This project uses non-parametric maximum-likelihood bootstrap estimates of power law fit on representative samples of nascent entrepreneurial ventures to derive a meta-construct framework of inputs that apply to all organizational forms: endowments, expectations, engagement, and environments. Then, to test this generalizable 4E framework, these inputs are used to build an agent-based simulation model, the outcomes for which will be validated with empirical data from the fastest growing privately held firms.
