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Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches
Profiling (Non-)Nascent Entrepreneurs in Hungary Based on
Machine Learning Approaches
In our study, we examined the characteristics of nascent entrepreneurs using the 2021
Global Entrepreneurship Monitor national representative data in Hungary. We examined our topic
based on Arenius and Minitti’s four-category theory framework. In our research, we examined
system-level feature sets with four machine learning modeling algorithms: multivariate adaptive
regression spline (MARS), support vector machine (SVM), random forest (RF), and AdaBoost. Our
results show that each machine algorithm can predict nascent entrepreneurs with over 90% adaptive cruise control (ACC) accuracy. Furthermore, the adaptation of the categories of variables based on the theory of Arenius and Minitti provides an appropriate framework for obtaining reliable predictions. Based on our results, it can be concluded that perceptual factors have different importance and weight along the optimal models, and if we include further reliability measures in the model validation, we cannot pinpoint only one algorithm that can adequately identify nascent entrepreneurs. Accurate forecasting requires a careful and predictor-level analysis of the algorithms’ models, which also includes the systemic relationship between the affecting factors. An important but unexpected result of our study is that we identified that Hungarian NEs have very specific previous entrepreneurial and business ownership experience; thus, they can be defined not as a beginner but as a novice enterprise.
Keywords: nascent entrepreneurs; machine learning; Global Entrepreneurship Monitor
Csákné Filep Judit
Sustainability 2022, 14, 3571.
Aportado por: José Carlos Sánchez García