Vol. 13 No. 2 (2025)
June 2025

Articles

Exploring entrepreneurial phases with machine learning models: Evidence from Hungary

Published 2025-06-30

  • Áron Szennay
    • ,
  • Judit Csákné Filep
    • ,
  • Melinda Krankovits

Áron Szennay
Budapest University of Economics and Business

Judit Csákné Filep
Budapest University of Economics and Business

Melinda Krankovits
Széchenyi István University

Abstract

Objective: The article aims to explore the potential differences between the two phases of entrepreneurship, i.e., total early-stage entrepreneurial activity and established business, as defined by the Global Entrepreneurship Monitor (GEM). The study aimed to classify entrepreneurs using various machine learning models and to evaluate their classification performance comparatively.

Research Design & Methods: Using the Hungarian GEM datasets from 2021 to 2023, we analysed a subsample of 964 entrepreneurs. Due to inconsistent results from traditional analyses (e.g., correlations, regressions, principal component analyses), we employed machine learning approaches (supervised learning classification methods) to uncover latent relationships between variables.

Findings: The study utilized seven machine learning classification methods to examine the feasibility of grouping companies within the sample using Hungarian GEM data. Findings indicate that machine learning techniques are particularly effective for classifying businesses, although the performance of each method varies significantly.

Implications & Recommendations: These results provide valuable insights for researchers in selecting methodologies to identify various business phases. Moreover, they offer practical benefits for market research professionals, suggesting that machine learning techniques can enhance the classification and understanding of entrepreneurial phases.

Contribution & Value Added: The study adds to the existing body of knowledge by demonstrating the effectiveness of machine learning methods in classifying business phases. It highlights the variability in performance across different machine learning techniques, thereby guiding future research and practical applications in market research and entrepreneurship studies.

Keywords

entrepreneurship, responsibility, Global Entrepreneurship Monitor, GEM, machine learning

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Author Biography

Áron Szennay

Senior Research Fellow at the Budapest LAB Office for Entrepreneurship Development, Budapest University of Economics and Business, PhD in regional sciences, and author of publications on entrepreneurship and its concerns regarding sustainability. His research interests include entrepreneurship, digitalisation, sustainability.

Judit Csákné Filep

Senior Research Fellow at the Budapest LAB Office for Entrepreneurship Development, Budapest University of Economics and Business. She holds a PhD in Management and Business Administration. She is the author of publications on family business and entrepreneurship. As the National Team Leader for Hungary in the Global Entrepreneurship Monitor, she is a major contributor to national and international entrepreneurship research. She also heads the Family Business Research Programme at the Budapest Business University. She is committed to the development of Hungarian family businesses and is a frequent contributor to conferences, podcasts, and other media engagements focused on the sector. Her research and interests include family business and entrepreneurship.

Melinda Krankovits

Assistant professor at Széchenyi István University, Department of Mathematics and Computer Science, PhD in regional sciences. Author of publications on distance learning in higher education and a quality assurance expert. She also contributes to papers on other fields as an expert in AI and machine learning. Her research and interests include machine learning, data processing, and data mining.

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