Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Author:

Ojeda-Beltrán Adelaida1,Solano-Barliza Andrés2ORCID,Arrubla-Hoyos Wilson3ORCID,Ortega Danny Daniel1,Cama-Pinto Dora45ORCID,Holgado-Terriza Juan Antonio6ORCID,Damas Miguel4ORCID,Toscano-Vanegas Gilberto7,Cama-Pinto Alejandro7ORCID

Affiliation:

1. Faculty of Economy, Universidad del Atlántico, Puerto Colombia 081007, Colombia

2. Faculty of Engineering, Universidad de La Guajira, Riohacha 440002, Colombia

3. Faculty of Engineering, Universidad Nacional Abierta y a Distancia, Sincelejo 700002, Colombia

4. Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain

5. Faculty of Industrial Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru

6. Software Engineering Department, University of Granada, 18014 Granada, Spain

7. Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia

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.

Funder

AUIP

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference89 articles.

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2. (2023, April 15). Innpulsa Colombia. (s.f.). Boletín Analítica. Available online: https://www.innpulsacolombia.com/sites/default/files/documentos-recursos-pdf/Boletin%20Analitica.pdf.

3. Pereira Laverde, F., Osorio-Tinoco, F., and Pinzón, N. (2022). Nuestro Reto: Impactar la DINAMICA Emprendedora Colombiana: GEM Colombia 2021–2022, Ediciones.

4. Velasco Chávez, R., Ordoñez Arias, C., Marion, R.S., and Juan, C.E. (2023, April 15). Ley de Emprendimiento en Colombia. In Innpulsa, Colombia. Publications. Available online: https://www.innpulsacolombia.com/sites/default/files/documentos-recursos-pdf/Boletin%20Analitica.pdf.

5. Predicting entrepreneurial activity using machine learning;Schade;J. Bus. Ventur. Insights,2023

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