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Predicting entrepreneurial activity using machine learning

Author

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  • Schade, Philipp
  • Schuhmacher, Monika C.
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.

Suggested Citation

  • Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    • Handle: RePEc:eee:jobuve:v:19:y:2023:i:c:s2352673422000555
    DOI: 10.1016/j.jbvi.2022.e00357
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    Supervised machine learning; Classification; Prediction; Entrepreneurial activity;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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