HYPER-PERSONALIZATION, AI-DRIVEN RECOMMENDATION ENGINES, CONSUMER ENGAGEMENT, ETHICAL AI, DIGITAL MARKETING, TOE FRAMEWORK

Abstract

This study looks at how AI recommendation engines help make hyper-personalization easier in online marketing and how that affects customer engagement, specifically in the Indian context. We used a mix of research methods and the Technology-Organization-Environment (TOE) framework to look at data from 475 responses from India, including consumers, marketers, and AI professionals. Hyper-personalization goes up a lot with human-based recommendation systems ( R² = 0.62, p < 0.001). This leads to big jumps in consumer metrics like click-through rates (CTR: r = 0.72, p < 0.01), conversion rates (r = 0.68, p < 0.01), and customer loyalty (r = 0.75, p < 0.01). Yet, its mass use is constrained by technical, organizational, and ethical reasons, and the most applicable constraint is ethical concerns (mean = 4.5). Qualitative results show how important morals and good AI practices are for reducing consumer worries about data privacy and algorithmic fairness (β = 0.45, p < 0.001).