The Impact of AI-Driven Hyper-Personalization on Sustainable Consumer Buying Behaviour: Opportunities, Ethical Challenges, and Pathways for Green Marketing in Emerging Markets

Abstract

Artificial Intelligence (AI)-driven hyper-personalization is revolutionizing green marketing by tailoring sustainability recommendations to individual consumer preferences, potentially bridging the attitude-behavior gap in sustainable consumption. This paper examines how AI algorithms analyze real-time data—such as purchase history, browsing patterns, and environmental values—to nudge consumers toward eco-friendly choices in emerging markets like India. Drawing on a systematic literature review of 2024-2026 studies and conceptual framework development, key opportunities include boosted green purchase intent (up to 35% in personalized nudges) and reduced waste via demand forecasting. However, ethical challenges like algorithmic bias, data privacy erosion, and manipulative over-nudging risk undermining trust, particularly among privacy-sensitive Gen Z in Rajasthan.

We propose an Ethical AI-Hyper-Personalization Framework (EAHPF) for green marketing, integrating transparency audits, cultural localization, and regulatory alignment with India's Digital Personal Data Protection Act (2023). Empirical insights from PLS-SEM models in recent studies show hyper-personalized "green" content enhances attitudes but triggers skepticism if perceived as excessive. Implications for managers emphasize hybrid human-AI oversight for sustainable B2C strategies. Future research directions include longitudinal studies on AI's long-term impact on hyper-personalization in B2B sustainability transitions. This interdisciplinary work contributes to management theory by extending socio-technical systems to ethical AI in consumer behavior, aiding faculty recruitment in sustainability-focused Indian academia.