Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms

This study extends emerging theories of star performers to digital platforms, an increasingly prevalent entrepreneurial context. It hypothesizes that the unique characteristics of many digital platforms (e.g., low marginal costs, feedback loops, and network effects) produce heavy-tailed performance distributions, indicating the existence of star entrepreneurs. Using longitudinal data from an online learning platform, proportional differentiation is identified as the most likely generative mechanism and lognormal distribution as the most likely shape for distributions of entrepreneurial performance in digital contexts. This study contributes theory and empirical evidence for non-normal entrepreneurial performance with implications for scholars and practitioners of digital entrepreneurship.

Automated summary

This study applies theories of star performers to digital platforms and suggests that the unique characteristics of digital platforms lead to heavy-tailed performance distributions, with proportional differentiation being the most likely mechanism for generating entrepreneurial performance in digital contexts. This provides theoretical and empirical evidence for non-normal entrepreneurial performance and has implications for scholars and practitioners of digital entrepreneurship.