Journalartikel

Jahr der Veröffentlichung: 2023

Zeitschrift: Journal of Business Venturing Insights

Bandnummer: 19

DOI Link: https://doi.org/10.1016/j.jbvi.2022.e00357

Verlag: Elsevier

Abstract: 

This study evaluates the predictability of entrepreneurial activity using machine learning. We compare different supervised machine learning techniques: decision tree, random forest, artificial neural network, k-nearest neighbor, extreme gradient boosting tree ensemble, and naïve Bayes, as well as run the traditional multiple logistic regression for obtaining a baseline and estimating their relative model prediction performance on a Global Entrepreneurship Monitor dataset of 1,192,818 individuals from 99 countries. By comparing different machine learning techniques, we predict out-of-sample opportunity-motivated entrepreneurial activity with an overall accuracy ranging from 70.1% to 91.2%. The results demonstrate that the extreme gradient boosting tree ensemble is superior in predicting opportunity-motivated entrepreneurial activity. Finally, a global surrogate model reveals that knowing an entrepreneur, entrepreneurial self-efficacy, and opportunity recognition are the three most important features for predicting opportunity-motivated entrepreneurial activity. For comparison purposes, we perform the same analyses for necessity-motivated entrepreneurial activity. The results reveal that the extreme gradient boosting tree ensemble is also the best-performing technique in predicting this form of entrepreneurial activity with a 96.5% accuracy.

Harvard-Zitierstil: Schade, P. and Schuhmacher, M. (2023) Predicting entrepreneurial activity using machine learning, Journal of Business Venturing Insights, 19, Article e00357. https://doi.org/10.1016/j.jbvi.2022.e00357

APA-Zitierstil: Schade, P., & Schuhmacher, M. (2023). Predicting entrepreneurial activity using machine learning. Journal of Business Venturing Insights. 19, Article e00357. https://doi.org/10.1016/j.jbvi.2022.e00357