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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Vydané v:Journal of business venturing Ročník 39; číslo 1; s. 106347
Hlavní autori: Gala, Kaushik, Schwab, Andreas, Mueller, Brandon A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.01.2024
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ISSN:0883-9026, 1873-2003
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Abstract 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.
AbstractList 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.
ArticleNumber 106347
Author Gala, Kaushik
Schwab, Andreas
Mueller, Brandon A.
Author_xml – sequence: 1
  givenname: Kaushik
  orcidid: 0000-0001-7849-2349
  surname: Gala
  fullname: Gala, Kaushik
  email: kgala@iastate.edu
– sequence: 2
  givenname: Andreas
  orcidid: 0000-0002-7968-1907
  surname: Schwab
  fullname: Schwab, Andreas
– sequence: 3
  givenname: Brandon A.
  surname: Mueller
  fullname: Mueller, Brandon A.
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Keywords Entrepreneurial performance
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Digital platforms
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Star entrepreneurs
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Lognormal performance distributions
Proportional differentiation
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Snippet This study extends emerging theories of star performers to digital platforms, an increasingly prevalent entrepreneurial context. It hypothesizes that the...
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StartPage 106347
SubjectTerms Digital platforms
Entrepreneurial performance
Lognormal performance distributions
Proportional differentiation
Star entrepreneurs
Title Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms
URI https://dx.doi.org/10.1016/j.jbusvent.2023.106347
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