Exploring the Frontier: Generative AI Applications in Online Consumer Behavior Analytic

• Takuma Kimura ^[1]
  1. [1] Hosei University

     Hosei University

     Japón

     
• Localización: Management Letters / Cuadernos de Gestión, ISSN 1131-6837, Vol. 25, Nº 1, 2025, págs. 57-70
• Idioma: inglés
• DOI: 10.5295/cdg.232121tk
• Títulos paralelos:
  • Explorando la frontera: aplicaciones generativas de la IA en el análisis del comportamiento de los consumidores en línea
• Enlaces
  • Texto completo
• Resumen
  • español

    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.

  • English

    This paper presents a systematic review of the application of generative artificial intelligence (AI) in online consumer behavior analytics (OCBA). With the advent of e-commerce and social media, consumer behavior increasingly occurs online, generating vast amounts of data. This shift necessitates advanced analytical tools, and generative AI emerges as a pivotal technology. Generative AI, distinct from traditional AI, can autonomously generate new content based on learned data patterns, offering innovative approaches to OCBA. Based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and data synthesis method proposed by Webster and Watson (2002), this study analyzes 28 peer-reviewed papers, focusing on how generative AI is applied in OCBA and how it can enhance OCBA performance. The findings show that generative adversarial networks (GANs) are the most used, followed by variational autoencoders (VAEs) and autoregressive models. This review categorizes the application areas of generative AI in OCBA and examines how these technologies enhance OCBA’s effectiveness and efficiency. Furthermore, the paper discusses the challenges associated with generative AI, emphasizing the need to consider ethical issues, such as bias and data privacy. This comprehensive review contributes to a deeper understanding of generative AI’s role in OCBA, outlining its applications and functionalities from a technical perspective. It guides future research and practice, highlighting areas for further exploration and improvement in leveraging generative AI for consumer behavior analytics.

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