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The Application of Selected Supervised Machine Learning Methods in the Classification of Family Businesses in the Context of Cluster Formation

Author

Listed:
  • Daria Wotzka
  • Pawel Fracz
  • Jolanta Staszewska
  • Joachim Foltys
  • Malgorzata Smolarek
  • Krzysztof Orzechowski

Abstract

Purpose: The article focuses on the application of selected supervised machine learning methods for the classification of family businesses in the context of cluster formation. The research aim was to evaluate various learning algorithms to develop a tool for classifying entrepreneurs, intended for use in an online application. Design/Methodology/Approach: Through a comprehensive survey, 448 responses were gathered, addressing various aspects of clusters and related experiences. Based on the collected data, classification methods for respondents were developed in the context of cluster formation. The classifier categorizes entrepreneurs based on their under-standing of cluster concepts, managers' perceptions of clusters, companies' experiences with clusters, the operational status of clusters, and experience in business networks. The article conducts a comparative analysis of the classification outcomes derived from the application of decision trees and neural networks across diverse configurations. This analysis, based on distinct evaluation metrics, culminates in the identification of the most optimal algorithm suited for the task at hand. Findings: As a result of the conducted research, a supervised machine learning algorithm in the form of an ensemble bagged tree was selected. This algorithm achieves an average effectiveness of 82%, measured as the arithmetic mean of accuracy, specificity, precision, sensitivity, F1 score, and the Matthews correlation coefficient. The median value was 96%. Practical Implications: The presented results have been implemented in the form of a computer application that allows for the simulation and classification of entrepreneurs based on their business experiences. The developed tool is being deployed as a web-based application, serving as a platform to showcase the numerous possibilities and benefits of cluster formation. Originality/Value: This study represents a novel approach, as there are no available articles specifically applying machine learning techniques to classify entrepreneurs, particularly family-owned businesses, in the context of cluster formation.

Suggested Citation

  • Daria Wotzka & Pawel Fracz & Jolanta Staszewska & Joachim Foltys & Malgorzata Smolarek & Krzysztof Orzechowski, 2024. "The Application of Selected Supervised Machine Learning Methods in the Classification of Family Businesses in the Context of Cluster Formation," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 248-272.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:4:p:248-272
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    References listed on IDEAS

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    1. Xiaofei Chen & Enru Wang & Changhong Miao & Lili Ji & Shaoqi Pan, 2020. "Industrial Clusters as Drivers of Sustainable Regional Economic Development? An Analysis of an Automotive Cluster from the Perspective of Firms’ Role," Sustainability, MDPI, vol. 12(7), pages 1-22, April.
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    3. Jolanta Staszewska & Malgorzata Smolarek & Joachim Foltys & Daria Wotzka & Pawel Fracz, 2024. "The Possibilities of Cooperation among Family Firms within a Cluster Environment," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 132-154.
    4. Konstantinos Liapis & Antonios Rovolis & Christos Galanos & Eleftherios Thalassinos, 2013. "The Clusters of Economic Similarities between EU Countries: A View Under Recent Financial and Debt Crisis," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 41-66.
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    More about this item

    Keywords

    Family businesses classification; machine learning; cluster formation.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis

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