Using Power-Law Science to Enhance Knowledge for Practical Relevance

Abstract

The debates about knowledge relevance have been around for years and several calls have been issued to change the methods used by researchers to improve the translation of academic research to management. We think a change in perspective about what phenomenon we study and how we study them is key to enhance knowledge for practical relevance. Specifically, most research produced by academics focuses on Gaussian science - the science of normal distributions, stable means, finite variance, and statistical significance. These help produce knowledge about normal phenomena but fails to provide solutions for organizations as truly dynamic systems. In contrast, we argue that research ontology and epistemology need to shift in some significant measure to the study of Paretian rank frequencies; what we call power-law science. This paper introduces Pareto rank frequency distributions and how they differ from normal methods of conducting research and suggests methods to use at various points on Pareto distributions to offer practical knowledge about phenomena faced by managers.

Keywords: Knowledge relevance, Normal science, Power Laws, Extremes, Limits of Averages