دانلود رایگان مقاله درک توزیع طولانی مدت و فرایند مولد در کارآفرینی

abstract

Crawford et al.’s (2014, 2015) research on empirical distributions in entrepreneurship has shown that almost all input and outcome variables in entrepreneurship follow highly skewed long-tail distributions. They refer to these as power-law (PL) distributions based on a quantitative PL fitting procedure. However, the generative process of these distributions is still unclear. Building on their research, I cultivate a more nuanced understanding of the long-tail distributions and their plausible generative process in entrepreneurship. In this study, the fitting procedure is applied to new ventures' initial expectations and temporal outcome variables on employment and revenue, including comparisons of fitting results from alternative long-tail models. In conclusion, I find that ventures' less skewed early-stage outcome distributions change into more skewed PL distributions over time, while most expectation distributions do not fit a specific long-tail model. Using a simple simulation, I suggest that a multiplicative process may be a plausible generative mechanism for the transformation.

6. Conclusion

I showed that ventures' less-skewed outcome distributions change into more skewed over time. The simulation results suggest that the random multiplicative process may be a plausible generative mechanism for the transformation. However, in the simulation results, the LN model has better fit than the PL model at every stages (1025 activities), unlike the empirical findings. This result implies that a more complicated mechanism, in addition to the random multiplicative process, may exist behind the transformation. More sophisticated agent-based modeling and simulations with plausible assumptions will be useful to discern the generative process.