Algorithmic Synchronicity and Perceived Relational Reciprocity: A Multi‐Method Investigation of Consumer Trust and Loyalty in AI ‐Driven Retail Ecosystems

ABSTRACT As artificial intelligence (AI) becomes increasingly embedded in retail service delivery, consumers are confronted not only with functional outputs but also with the social dynamics of AI–human interaction. This research introduces two novel constructs, algorithmic synchronicity (AS) and perceived relational reciprocity (PRR), to explain how consumers infer trust and develop loyalty towards AI‐enabled retail systems. Across three studies, a 2 × 2 experimental design with PLS‐SEM ( n = 326), a large‐scale NLP sentiment analysis of over 260,000 reviews and expert interviews triangulated with 2‐year panel data analysed using system generalised method of moments (SGMM), we demonstrate that perceived rhythm alignment and emotional reciprocation significantly enhance consumer trust. Trust, in turn, mediates loyalty intent, though the effects of AS vary across contexts. The findings extend consumer behaviour theory beyond static personalisation paradigms, offering a dynamic, interactional perspective rooted in synchrony theory and relational norms. Practical implications include design principles for emotionally intelligent AI, transparency protocols and governance frameworks for affective algorithmic engagement.