Characterisation of youth entrepreneurship in Medellín-Colombia using machine learning

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 w...

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Autores:
Ojeda Beltran, Adelaida
Solano-Barliza, Andrés D.
ARRUBLA HOYOS, WILSON DE JESÚS
Ortega, Danny Daniel
Cama-Pinto, Dora
Holgado-Terriza, Juan Antonio
Damas, Miguel
Toscano-Vanegas, Gilberto
Cama-Pinto, Alejandro
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10297
Acceso en línea:
https://hdl.handle.net/11323/10297
https://repositorio.cuc.edu.co/
Palabra clave:
Artificial intelligence
Machine learning
Data mining
K-mean
Youth entrepreneurship
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
Description
Summary: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.