Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs

Clariandys Rivera-Kempis, Leobardo Valera and Miguel A. Sastre-Castillo
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Clariandys Rivera-Kempis: Centro de Emprendedores, Instituto de Estudios Superiores de Administración (IESA), Caracas 1011, Venezuela
Leobardo Valera: Department of Electrical Engineering and Computer and Science, University of Tennessee Knoxville, Knoxville, TN 37996, USA
Miguel A. Sastre-Castillo: Faculty of Commerce and Tourism, Universidad Complutense de Madrid, 28003 Madrid, Spain

Sustainability, 2021, vol. 13, issue 15, 1-24

Abstract: Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component analysis, ranking and classification of data using the Ward method, linear discriminant analysis, and Gaussian regression among others.

Keywords: entrepreneurship; entrepreneur; entrepreneurial competence; machine learning; logistic regression; linear discriminant; hierarchical agglomerative clustering algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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