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Enhancing simulation-based theory development in entrepreneurship through statistical validation

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  • Breig, Roman
  • Coblenz, Maximilian
  • Pelz, Michael

Abstract

Recent research has called for new theory development because variables in entrepreneurship have been shown to follow power laws. Usually, simulation is used to validate these new theories. However, validation has been insufficient because it fails to provide a quantitative comparison of distribution parameters. This neglect can cause misleading conclusions. To address this insufficiency, we contribute a four-step method: the possible simulation parameter range (PSPR). The fundamental advantage of the method is to compare distribution parameters of both empirical data and simulation results. We demonstrate the method's usefulness with an illustrative example.

Suggested Citation

  • Breig, Roman & Coblenz, Maximilian & Pelz, Michael, 2018. "Enhancing simulation-based theory development in entrepreneurship through statistical validation," Journal of Business Venturing Insights, Elsevier, vol. 9(C), pages 53-59.
    • Handle: RePEc:eee:jobuve:v:9:y:2018:i:c:p:53-59
    DOI: 10.1016/j.jbvi.2018.02.003
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    References listed on IDEAS

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    1. Keiki Takadama & Tetsuro Kawai & Yuhsuke Koyama, 2008. "Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-9.
    2. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    3. Crawford, G. Christopher & Aguinis, Herman & Lichtenstein, Benyamin & Davidsson, Per & McKelvey, Bill, 2015. "Power law distributions in entrepreneurship: Implications for theory and research," Journal of Business Venturing, Elsevier, vol. 30(5), pages 696-713.
    4. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    5. Christopher Crawford, G. & McKelvey, Bill & Lichtenstein, Benyamin B., 2014. "The empirical reality of entrepreneurship: How power law distributed outcomes call for new theory and method," Journal of Business Venturing Insights, Elsevier, vol. 1, pages 3-7.
    6. Shim, Jaehu, 2016. "Toward a more nuanced understanding of long-tail distributions and their generative process in entrepreneurship," Journal of Business Venturing Insights, Elsevier, vol. 6(C), pages 21-27.
    7. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    8. Pierpaolo Andriani & Bill McKelvey, 2009. "Perspective ---From Gaussian to Paretian Thinking: Causes and Implications of Power Laws in Organizations," Organization Science, INFORMS, vol. 20(6), pages 1053-1071, December.
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    Cited by:

    1. Dean A. Shepherd & Johan Wiklund, 2020. "Simple Rules, Templates, and Heuristics! An Attempt to Deconstruct the Craft of Writing an Entrepreneurship Paper," Entrepreneurship Theory and Practice, , vol. 44(3), pages 371-390, May.

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