Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Adelaida Ojeda-Beltrán, Andrés Solano-Barliza, Wilson Arrubla-Hoyos, Danny Daniel Ortega, Dora Cama-Pinto (), Juan Antonio Holgado-Terriza, Miguel Damas, Gilberto Toscano-Vanegas and Alejandro Cama-Pinto ()
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Adelaida Ojeda-Beltrán: Faculty of Economy, Universidad del Atlántico, Puerto Colombia 081007, Colombia
Andrés Solano-Barliza: Faculty of Engineering, Universidad de La Guajira, Riohacha 440002, Colombia
Wilson Arrubla-Hoyos: Faculty of Engineering, Universidad Nacional Abierta y a Distancia, Sincelejo 700002, Colombia
Danny Daniel Ortega: Faculty of Economy, Universidad del Atlántico, Puerto Colombia 081007, Colombia
Dora Cama-Pinto: Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain
Juan Antonio Holgado-Terriza: Software Engineering Department, University of Granada, 18014 Granada, Spain
Miguel Damas: Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain
Gilberto Toscano-Vanegas: Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia
Alejandro Cama-Pinto: Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia

Sustainability, 2023, vol. 15, issue 13, 1-19

Abstract: The aim of this paper is to identify profiles of young Colombian entrepreneurs based on data from the “Youth Entrepreneurship” survey developed by the Colombian Youth Secretariat. Our research results show five profiles of entrepreneurs, mainly differentiated by age and entrepreneurial motives, as well as the identification of relevant skills, capacities, and capabilities for entrepreneurship, such as creativity, learning, and leadership. The sample consists of 633 young people aged between 14 and 28 years in Medellín. The data treatment was approached through cluster analysis using the K-means algorithm to obtain information about the underlying nature and structure of the data. These data analysis techniques provide valuable information that can help to better understand the behaviour of Colombian entrepreneurs. They also reveal hidden information in the data. Therefore, one of the advantages of using statistical and artificial intelligence techniques in this type of study is to extract valuable information that might otherwise go unnoticed. The clusters generated show correlations with profiles that can support the design of policies in Colombia to promote an entrepreneurial ecosystem and the creation and development of new businesses through business regulation.

Keywords: artificial intelligence; machine learning; data mining; K-mean; youth entrepreneurship (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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