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

Aug 5, 2026

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The article applies selected supervised machine learning methods to classify family businesses during cluster formation. The study’s purpose is to evaluate alternative learning algorithms and build an online classification tool for entrepreneurs. Using 448 survey responses, the work develops a classifier based on understanding of cluster concepts, managers’ perceptions, company experience, cluster operational status, and business-network experience. A comparison of decision trees and neural networks identifies the best-performing ensemble bagged tree model with strong effectiveness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Abstract

Purpose

Design/Methodology/Approach

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Practical Implications

Introduction

European Research Studies Journal Volume XXVII, Issue 4, 2024 pp. 248-272 The Application of Selected Supervised Machine Learning Methods in the Classification of Family Businesses in the Context of Cluster Formation Submitted 13/09/24, 1st revision 23/09/24, 2nd revision 14/10/24, accepted 30/10/24 Daria Wotzka1, Paweł Frącz2, Jolanta Staszewska3, Joachim Foltys4, Małgorzata Smolarek5, Krzysztof Orzechowski6 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 1Opole University of Technology, Faculty of Electrical Engineering Automatic Control and Informatics, ORCID 0000-0002-8861-7974, [email:](email: d.wotzka@po.edu.pl)[ ](email: d.wotzka@po.edu.pl)[d.wotzka@po.edu.pl](email: d.wotzka@po.edu.pl); 2Opole University, Faculty of Economics, ORCID 0000-0003-1677-6084, email: [p](pawel.fracz@uni.opole.pl)[awel.fracz@uni.opole.pl](pawel.fracz@uni.opole.pl); 3Humanitas University, Institute of Management and Quality Sciences, ORCID 0000-0001- 914-212, [email:](email: jolanta.staszewska@humanitas.edu.pl)[ jo](email: jolanta.staszewska@humanitas.edu.pl)[lanta.staszewska@humanitas.edu.pl](email: jolanta.staszewska@humanitas.edu.pl); 4Humanitas University, Institute of Management and Quality Sciences, ORCID 0000-0003- 4836-3161, [email:](email: joachim.foltys@humanitas.edu.pl)[ j](email: joachim.foltys@humanitas.edu.pl)[oachim.foltys@humanitas.edu.pl](email: joachim.foltys@humanitas.edu.pl); 5Humanitas University, Institute of Management and Quality Sciences, ORCID 0000-0002- 3766-8843, [email:](email: malgorzata.smolarek@humanitas.edu.pl)[ ](email: malgorzata.smolarek@humanitas.edu.pl)[malgorzata.smolarek@humanitas.edu.pl](email: malgorzata.smolarek@humanitas.edu.pl); 6Humanitas University, Institute of Management and Quality Sciences, email: [krzysztof.orzechowski@humanitas.edu.pl](krzysztof.orzechowski@humanitas.edu.pl); 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. Keywords: Family busines

FAQs

What is the main research purpose of the article?

To apply supervised machine learning methods to classify family businesses in the context of cluster formation, and to evaluate learning algorithms for an online classification tool.

How was the classification model developed and validated?

The study uses 448 survey responses and constructs a classifier using factors such as understanding of cluster concepts, managers’ perceptions, business experience with clusters, cluster operational status, and experience in business networks, then compares decision trees and neural networks with evaluation metrics.

Which algorithm performed best and what effectiveness was reported?

An ensemble bagged tree model was selected, achieving an average effectiveness of about 82% (as the mean of multiple metrics), with a median value of 96%.

Aug 5, 2026

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