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...

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Bibliographic Details
Published in:Journal of business venturing Vol. 39; no. 1; p. 106347
Main Authors: Gala, Kaushik, Schwab, Andreas, Mueller, Brandon A.
Format: Journal Article
Language:English
Published: Elsevier Inc 01.01.2024
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ISSN:0883-9026, 1873-2003
Online Access:Get full text
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Summary: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. •Heavy-tailed performance distributions in digital contexts signify star entrepreneurs.•Low marginal costs, positive feedback loops, and network effects drive heavy tails.•Lognormal distributions characterize entrepreneurial performance on digital platforms.•Proportional differentiation is a key generative mechanism of star entrepreneurs.•Platform characteristics such as knowledge intensity can influence tail extremity.
ISSN:0883-9026
1873-2003
DOI:10.1016/j.jbusvent.2023.106347