Generative Artificial Intelligence Powered Hyper Personalization and Consumer Decision Making in Digital Marketing Ecosystems

Abstract

Generative Artificial Intelligence (GenAI) is transforming digital marketing personalization from conventional retrieval- and recommendation-based approaches toward hyper-personalization characterized by real-time adaptivity, generative content synthesis, multimodality, and contextual relevance. However, the mechanisms linking GenAI-powered hyper-personalization to Consumer Decision Making (CDM) remain theoretically fragmented, particularly regarding perceived personalization, trust in AI, and privacy concern. This study develops a conceptual framework explaining how GenAI-powered hyper-personalization influences CDM through sequential psychological mechanisms and under privacy-related boundary conditions. Grounded primarily in the Stimulus-Organism-Response (S-O-R) paradigm and complemented by the Technology Acceptance Model and Privacy Calculus Theory, the study synthesizes literature from 2021 to 2026 and develops six research propositions. Empirical validation is proposed using PLS-SEM and a between-subjects scenario experiment comparing GenAI-generated and conventional algorithmic personalization. The framework conceptualizes GenAI-powered hyper-personalization as the stimulus, perceived personalization and trust in AI as sequential organismic mechanisms, privacy concern as a boundary condition, and CDM as the response. The study contributes by distinguishing generative hyper-personalization from conventional personalization, specifying a sequential perceived-personalization and trust mechanism, and incorporating privacy concern as a dual boundary condition. The framework also provides implications for technology innovation management, digital transformation, responsible AI governance, transparency, privacy protection, and consumer relationships.

Keywords

• GenAI
• Hyper-personalization
• CDM
• Perceived Personalization
• Privacy Concern