Emerging Scalability and Extreme Outcomes in New Ventures: Power Law Distribution Analyses of Three Studies

Abstract

Reviews of the management, entrepreneurship, and marketing literature suggest that most performance-based outcomes are not distributed according to Gaussian assumptions within the normal, bell-shaped curve. Instead, Paretian (i.e., power-law) distributions are the new norm, where extreme outliers occur far more frequently and, more importantly, have a disproportionate influence on the larger system than normal statistics would lead us to believe. The unique statistical properties of power-law distributions require a scale-free theory, where a single explanation at one level applies to multiple units at the preceding level. As such, I develop a hypothesis to suggest that a founder’s expectations for future growth in the nascent organizing stage can influence a venture’s potential ability to scale up into an extreme outcome at later stages. I use MATLAB to construct semi-parametric bootstrap estimates for maximum likelihood fit with a power-law model on representative sample datasets from three levels of self-organized venture emergence: nascent, active start-up, and hyper-growth. I find substantial support for the scale-free hypothesis – a universal scaling exponent of ~ 1.75 – at multiple units and levels of analysis. I use the results to suggest various implications for theory, practice, pedagogy, and policy.

Keywords: Entrepreneurship, New Venture Growth, Power Law Distribution, Extreme Outcomes

JEL Classification: L22, L26