Exploring the Frontier: Generative AI Applications in Online Consumer Behavior Analytics
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Published
11-09-2024
Takuma Kimura
https://orcid.org/0000-0001-7126-188X
Abstract
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. Further
more, 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.
How to Cite
Kimura, . T. (2024). Exploring the Frontier: Generative AI Applications in Online Consumer Behavior Analytics. Cuadernos De Gestión, 1–14. https://doi.org/10.5295/cdg.232121tk
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Keywords
Generative artificial intelligence, Generative adversarial network, Variational autoencoders, Autore gressive model, Generative pre-trained transformer, Online consumer behavior analytics
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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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