Explorando la frontera: Aplicaciones generativas de la IA en el análisis del comportamiento de los consumidores en línea
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Publicado
21-03-2025
Takuma Kimura
https://orcid.org/0000-0001-7126-188X
Resumen
Este artículo presenta una revisión sistemática de la aplicación de la IA generativa en el Análisis del Comportamiento del Consumidor Online (OCBA). Con la llegada del comercio electrónico y las redes sociales, el comportamiento de los consumidores se produce cada vez más en línea, lo que genera enormes cantidades de datos. Este cambio requiere herramientas analíticas avanzadas, y la IA generativa emerge como una tecnología fundamental. La IA generativa, distinta de la IA tradicional, puede generar de forma autónoma nuevos contenidos basados en patrones de datos aprendidos, ofreciendo enfoques innovadores a la OCBA. Basado en la metodología PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) y el método de síntesis de datos propuesto por Webster y Watson (2002), este estudio analiza 28 artículos revisados por pares, centrándose en cómo se aplica la IA generativa en OCBA y cómo puede mejorar el rendimiento de OCBA. Los resultados muestran que las redes generativas adversariales (GAN) son las más utilizadas, seguidas de los autocodificadores variacionales (VAE) y los modelos autorregresivos. La revisión clasifica las áreas de aplicación de la IA generativa en OCBA y examina cómo estas tecnologías mejoran la eficacia y la eficiencia de OCBA. Además, el artículo analiza los retos asociados a la IA generativa, haciendo hincapié en la necesidad de tener en cuenta cuestiones éticas como la parcialidad y la privacidad de los datos. Esta revisión contribuye a una comprensión más profunda del papel de la IA generativa en la OCBA, esbozando sus aplicaciones y funcionalidades desde una perspectiva técnica.
Cómo citar
Kimura, . T. (2025). Explorando la frontera: Aplicaciones generativas de la IA en el análisis del comportamiento de los consumidores en línea. Cuadernos De Gestión, 25(1), 57–70. https://doi.org/10.5295/cdg.232121tk
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Keywords
Inteligencia artificial generativa, Redes generativas adversariales, Autocodificadores variacionales, Modelo autorregresivo, Transformador generativo preentrenado, Análisis del comportamiento del consumidor online
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Kimura, T. (2023). Assessment of personal values for data-driven human resource management. Data Science Journal, 22. https://doi.org/10.5334/dsj-2023-022
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