Generative AI Disclosure in Advertising: From Intent- to Production-Based Disclosure

Generative artificial intelligence (GenAI) is transforming advertising production and introducing new forms of disclosure at the point of exposure. Existing research treats disclosure primarily as a cue signaling persuasive intent, yet GenAI disclosures increasingly communicate how content was produced rather than why it was created. This paper reconceptualizes disclosure in AI-mediated advertising by positioning GenAI disclosure as a signal of production conditions. Integrating signaling theory, source credibility theory, the persuasion knowledge model, and the heuristic–systematic model, the paper develops a conceptual framework that differentiates declarative, regulatory, and infrastructural disclosure as distinct rendering conditions. The framework proposes that GenAI disclosure influences consumer evaluations through two complementary inferential pathways: perceived source attribution and production-based agent knowledge, defined as consumers’ beliefs about how persuasive content was generated and what those production processes imply for evaluating the persuasion agent. It further specifies how consumers’ motivation and ability shape the likelihood of heuristic versus systematic processing. The paper advances advertising theory by explaining how disclosure operates when its primary referent shifts from persuasive intent to production conditions and by providing a framework for understanding why similar disclosure cues may generate divergent evaluative responses across consumers, contexts, and forms of GenAI use.