Tailored or Overloaded? Hyper-Personalization in Digital Thrift Consumer Behaviour

Abstract

AI-driven hyper-personalization (HP) is transforming digital marketing by delivering highly tailored consumer experiences through big data, artificial intelligence (AI), and machine learning. This shift is especially significant in the growing digital thrift and second-hand fashion market, where Generation Z and Millennials seek affordability, uniqueness, and sustainable consumption. However, excessive personalization through aggressive retargeting, irrelevant ads, and constant notifications can lead to cognitive overload and privacy concerns. This study examines the balance between providing a “Tailored” experience and creating “Overload” in digital thrift platforms. It explores how personalization intensity, moderated by trust in the platform, affects consumer purchase intention through cognitive overload. Findings suggest that moderate personalization improves user satisfaction and purchase intention, while excessive personalization negatively impacts consumer experience. The study highlights the importance of ethical, transparent, and user-controlled personalization strategies to build long-term trust and support sustainable digital consumption.