Signal competition in AI-generated retail advertising: Credibility, eeriness, and trust formation

Artificial intelligence generated advertising is widely adopted in retailing and consumer services, yet its effects on trust, engagement, and purchase intention remain theoretically inconsistent. This study addresses this anomaly by reconceptualising advertising effectiveness under algorithmic authorship as a process of signal resolution rather than additive persuasion. Drawing on signalling theory and advertising value theory, the study specifies advertising value as a formative signal system composed of informativeness, entertainment, and executional credibility, which simultaneously activates competing inferential pathways of perceived credibility and perceived eeriness. Using a theory-driven PLS-SEM model estimated on a quota-based U.S. consumer sample (N = 412), the results provide associative evidence of systematic asymmetry and suppression effects: identical executional cues strengthen credibility while concurrently amplifying eeriness, with trust patterns consistent with the relative dominance of these opposing inferences rather than from overall message quality. Trust functions as a conditional transmission mechanism to engagement and purchase intention, and AI disclosure is associated with shifts in signal weighting by attenuating credibility-based pathways and amplifying eeriness-based suppression. By identifying signal competition as a structural feature associated with AI-generated advertising, the study extends current theoretical understanding of algorithmic persuasion by introducing signal competition as a structural feature associated with AI-generated advertising, departing from human-centric models and clarifying why creative AI execution often fails to yield behavioural conversion.