Algorithmic Authenticity: Impact Of AI Personalization On Perceived Brand Trust

Abstract

The rise of AI based personalization in digital marketing has changed the way brands interact with consumers by providing hyper relevant content at scale. However, empirical understanding of how different types of personalization logic shape perceived authenticity and brand trust is still developing. Although there is ample literature on and conceptualizations of personalization, prior research collapses personalization into a single type, has underemphasized the role of authenticity as a mediating factor, and has largely neglected to account for cueing algorithm disclosure/algorithmic transparency. This study, in response to this research gap, assesses the relationship between four types of personalization (generic, content based, hybrid-disclosed, and hybrid-opaque), using an algorithmic audit with an experiment to compare empirical net effects of each personalization type on authenticity and trust. This research was guided by signalling theory and literature on relationship marketing and forming hypotheses to examine perceived authenticity as mediating the impact of a personalization type on trust measures. The results show that hybrid-disclosed personalization has the most effect on authenticity, which had an equally palpable effect on trust, even with a constant message relevance. By merging a technical audit and findings from consumer response, this research contributes to personalization literature, tenure towards design decisions that contribute to ethical practices, and situates perceived authenticity as a relevant process in establishing and maintaining integrative and sustainable brand relationships that can be traceable back to ownership & algorithmic branded strategies