Intención emprendedora: segmentación de perfiles a través de inteligencia artificial
Palabras clave:
intención emprendedora, inteligencia artificial, clusterización, emprendimiento juvenil
Resumen
El objetivo de este estudio es analizar la intención emprendedora mediante un enfoque cuantitativo que combina técnicas estadísticas clásicas y algoritmos de Inteligencia Artificial. A partir de datos del Global Entrepreneurship Monitor y una encuesta estructurada de 89 ítems, se evaluaron variables clave como la motivación para emprender, los valores personales, el ecosistema emprendedor, la diversidad del conocimiento y los esquemas de inversión. La validez del instrumento se comprobó mediante Análisis Factorial Exploratorio, obteniendo altos índices de confiabilidad (Alfa de Cronbach > .86) y adecuación muestral (KMO > .89). Posteriormente, se aplicó el algoritmo de K-medias técnica de aprendizaje no supervisado ampliamente utilizada por su eficiencia y escalabilidad, con el propósito de segmentar los datos en clústeres homogéneos. Para evaluar la relevancia de cada variable, se empleó la varianza intra-clúster como métrica de dispersión interna. Variables con menor varianza presentaron mayor peso en la formación de perfiles, lo cual optimiza la interpretación y precisión del modelo. Los hallazgos demuestran que esta metodología permite identificar factores determinantes en la intención emprendedora favoreciendo la reducción de dimensionalidad y aportando información clave para la toma de decisiones en entornos complejos y de alto volumen de datos. Se concluye intención emprendedora responde a una estructura multidimensional en la que convergen motivaciones económicas, sociales y personales.
Citas
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Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
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Wang, X.-H., You, X., Wang, H.-P., & Wang, B. (2023). The Effect of Entrepreneurship Education on Entrepreneurial Intention: Mediation of Entrepreneurial Self-Efficacy and Moderating Model of Psychological Capital. Sustainability, 15(3), 2562. https://doi.org/10.3390/su15032562
Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. Journal of Black Psychology, 44(3), 219-246. https://doi.org/10.1177/0095798418771807
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678. https://doi.org/10.1109/TNN.2005.845141
Zeynalov, S., & Doğantan, E. (2025). The Effect of Digital Literacy and Entrepreneurship Education on Digital Entrepreneurship Intention: The Mediating Role of Personal Innovativeness. Technology, Knowledge and Learning, 1-18. https://doi.org/10.1007/s10758-025-09821-1
Aristizábal, J. M., Tarapuez, E., & Astudillo, C. A. (2024). Predicting entrepreneurial intention in Colombian academics: a machine learning approach. Journal of Entrepreneurship in Emerging Economies, 17(2), 260-288. https://doi.org/10.1108/JEEE-04-2023-0141
Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. En S. SIAM Activity Group on Discrete Mathematics, Proceeding (págs. 1027 - 1035). Society for Industrial and Applied Mathematics. https://doi.org/10.5555/1283383
Bagirov, A. M., Taheri, S., & Ugon, J. (2016). Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems. Pattern Recognition, 53, 12-24. https://doi.org/10.1016/j.patcog.2015.11.011
Ding, C., & He, X. (2004). K-means clustering via principal component analysis. ICML ‘04: Proceedings of the twenty-first international conference on Machine learning, 29. https://doi.org/10.1145/1015330.1015408
Fränti, P., & Sieranoja, S. (2019). How much can k-means be improved by using better initialization and repeats? Pattern Recognition, 93, 95-112. https://doi.org/10.1016/j.patcog.2019.04.014
Gribel, D., & Vidal, T. (2019). HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recognition, 88, 569-583. https://doi.org/10.1016/j.patcog.2018.12.022
Guatemala, M. A., Martínez, P. G., y Martínez de Escobar, F. A. (2023). Tendencias de Marketing Digital para Emprendedores Sociales Basados en Plataformas Digitales. Revista de Educación y Comunicación en la Sociedad del Conocimiento, 23(1), 154-178. https://doi.org/10.30827/eticanet.v23i1.27709
Han, J., Kamber, M., & Pei, J. (2022). Data mining: concepts and techniques. Morgan Kaufmann.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and adcances in the era of big data. Information Sciences, 622, 178-210. https://doi.org/10.1016/j.ins.2022.11.139
Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys (CSUR), 31(3), 264 - 323. https://doi.org/10.1145/331499.331504
Lloret, S., Ferreres, A., Hernández, A., & Tómas, I. (2017). The exploratory factor analysis of items: guided analysis based on empirical data and software. Anales de Psicología, 33(2), 417-432. https://doi.org/10.6018/analesps.33.2.270211
López-Aguado, M., & Gutiérrez-Provecho, L. (2019). Cómo realizar e interpretar un análisis factorial exploratorio utilizando SPSS. REIRE Revista d´Innovació y Recerca en Educació, 1-14. https://doi.org/10.1344/reire2019.12.227057
MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281-297.
Morales-Morales, J., Valdez-Acosta, N., Morales-Morales, J. R., y Arturo, Y.-R. (2023). Calidad en el servicio hotelero: Propuesta de metodología experimental. Revista Venezolana De Gerencia, 28(104), 1717-1734. https://doi.org/10.52080/rvgluz.28.104.21
Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 55(3), 529-539. https://doi.org/10.1007/s11187-019-00202-4
Oti, E. U., Unyeagu, S., Nwankwo, C. H., Alvan, W. K., & Osuji, G. A. (2020). New K-means clsutering methods that minimize the total intra-cluster variance. African Journal of Mathematics and Statistics Studies, 3(5), 42-54.
Pizarro, R. K., y Martínez, M. O. (2020). Análisis factorial exploratorio mediante el uso de las medidas de adecuación muestral kmo y esfericidad de bartlett para determinar factores principales. Journal of Science and Research, V(1), 903-924. https://doi.org/10.5281/zenodo.4453224
Rafiq, M., Yang, J., & Bashar, S. (2024). Impact of personality traits and sustainability education on green enterpreneurship behavior of university students: Mediating role of green entrepreneurial intention. Journal of Global Entrepreneurship Research, 14(14). https://doi.org/10.1007/s40497-024-00384-6
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
Schubert, E. (2023). Stop using the elbow criterion for k-means and how to choose the number of clusters instead. ACM SIGKDD Exploration Newsletter, 25(1), 36-42. https://doi.org/10.1145/3606274.3606278
Sólorzano, S. S., Pizarro, R. J., Díaz, C. J., Arias, M. J., Zamora, C. M., Lozzelli, V. M., & Montes, N. (2024). Acceptance of artificial intelligence and its effect on entrepreneurial intention in foreign trade students: a mirror analysis. Journal of Innovation and Entrepreneurship, 13(59). https://doi.org/10.1186/s13731-024-00412-5
Taktak-Kallel, I. (2025). Advancing Entrepreneurship Knowledge with Artificial Intelligence (AI) Methods: Insights from Scoping Review of Emerging AI-Powered Research on Student Entrepreneurial Intentions. Journal of Innovation Economics & Management , 175-XXXIX. https://doi.org/10.3917/jie.pr1.0175
Wang, X.-H., You, X., Wang, H.-P., & Wang, B. (2023). The Effect of Entrepreneurship Education on Entrepreneurial Intention: Mediation of Entrepreneurial Self-Efficacy and Moderating Model of Psychological Capital. Sustainability, 15(3), 2562. https://doi.org/10.3390/su15032562
Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. Journal of Black Psychology, 44(3), 219-246. https://doi.org/10.1177/0095798418771807
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678. https://doi.org/10.1109/TNN.2005.845141
Zeynalov, S., & Doğantan, E. (2025). The Effect of Digital Literacy and Entrepreneurship Education on Digital Entrepreneurship Intention: The Mediating Role of Personal Innovativeness. Technology, Knowledge and Learning, 1-18. https://doi.org/10.1007/s10758-025-09821-1
Publicado
2025-07-28
Cómo citar
Lara-Salazar, J. R., Morales-Morales, J., & Yee-Rendón, A. (2025). Intención emprendedora: segmentación de perfiles a través de inteligencia artificial. Revista Venezolana De Gerencia, 30(13), 660-677. https://doi.org/10.52080/rvgluz.30.especial13.42
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