The Dynamic AI Personalization Nexus (DAPN): Redefining Marketing Theory and Consumer Behaviour in the Age of Algorithmic Personalization

Abstract The rise of AI-driven personalization makes a major turning point in marketing, reshaping how companies understand and engage with customers. This conceptual paper introduces the Dynamic AI personalization Nexus (DAPN) - a framework that explains how firms and consumers interact in a deeply individualized, data-driven environment that goes beyond traditional segmentation. At the heart framework is the idea that personalization relevance drives value creation. However, it also highlights a key tension: while stronger personalization can boost firm performance it may also reduce consumer trust if it feels intrusive – what we term the personalization – autonomy paradox. The paper proposes algorithmic transparency as a crucial moderating factor that can ease these tensions and support more sustainable, trust-based digital relationships. DAPN draws from marketing theory, behavioural science and AI ethics to present eight conceptual propositions that aim to balance personalization efficiency with respect for consumer autonomy. This study adds a human-centred perspective to algorithmic personalization and offers practical guidance for developing Ethical AI Marketing Frameworks that prioritize fairness, transparency and consumer choice. The DAPN provides a foundation for future research and strategic use within evolving intelligent marketing systems. Keywords AI-driven personalization; Dynamic AI Personalization Nexus (DAPN); Consumer behaviour; Personalization relevance; Algorithmic transparency; Ethical AI marketing; Personalization–autonomy paradox; Marketing theory; Data-driven marketing; Consumer trust