Logo
Munich Personal RePEc Archive

Consumer Behavior Theory in the Era of Generative AI

Zhang, Jincheng (2026): Consumer Behavior Theory in the Era of Generative AI.

[thumbnail of MPRA_paper_129360.docx] PDF
MPRA_paper_129360.docx

Download (34kB)

Abstract

With the rapid development of generative AI, traditional consumer behavior theories centered on "information search-rational decision-making" are undergoing structural changes. Consumers no longer rely solely on static information platforms but engage in interactive dialogue with generative AI, completing needs identification, solution generation, and decision optimization with the support of dynamically generated content. This paper proposes a "Generative AI-Driven Interactive Consumer Behavior Model" (GAIBB) based on the integration of classic consumer behavior theories (such as the Theory of Rational Behavior, the Theory of Planned Behavior, and the Theory of Experiential Consumption). This model emphasizes three mechanisms: "co-creation decision-making," "generative recommendation," and "instant feedback loop," explaining how AI reshapes consumers' cognitive paths and purchasing behavior. The research further indicates that generative AI is driving a shift in consumer behavior from "information acquisition" to "co-creation of cognition," and reconstructing the power structure between platforms, brands, and users.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.