Hamayesh Arwin Alborz همایش آروین البرز EAB JOURNAL فصلنامه پژوهش‌های نوین در اقتصاد، حسابداری و بازرگانی (EABJ) 2 2 2026 09 22 The Impact of Perceived Authenticity of AI-Generated Marketing Content on Brand Trust and Purchase Intention: The Mediating Role of Perceived Brand Transparency The Impact of Perceived Authenticity of AI-Generated Marketing Content on Brand Trust and Purchase Intention: The Mediating Role of Perceived Brand Transparency 286 300 FA Reza Zeinali Independent Researcher, Tehran, Iran Marketing and Consumer Behavior Track Reza Zeinali Independent Researcher, Tehran, Iran Marketing and Consumer Behavior Track 2026 09 12 Generative artificial intelligence (GenAI) is rapidly becoming part of the marketing communication infrastructure, yet its ability to create efficient and visually persuasive content has generated a parallel concern: consumers may question whether machine-assisted messages are genuine, accountable, and worthy of trust. This conceptual study examines how the perceived authenticity of AI-generated marketing content may shape brand trust and purchase intention, with perceived brand transparency proposed as a central mediating mechanism. Drawing on Signaling Theory and the Stimulus–Organism–Response (S–O–R) framework, the paper integrates recent evidence on AI authorship, disclosure, brand authenticity, transparency, algorithm aversion/appreciation, and consumer trust. The proposed model argues that authentic-seeming AI content can function as a credibility cue that reduces ambiguity surrounding algorithmic authorship. Consumers who interpret AI-generated content as coherent with a brand’s identity, honest in origin, and non-manipulative are more likely to infer that the brand communicates openly and voluntarily. Such perceived transparency should strengthen brand trust and, in turn, purchase intention. The framework also allows for direct effects of authenticity on trust and purchase intention, creating a serial mediation pathway from perceived authenticity to transparency, trust, and behavioral intention. The study contributes by separating content authenticity from brand transparency, explaining why transparency is not equivalent to mere AI disclosure, and reconciling conflicting findings showing that disclosure can either strengthen or weaken consumer responses. Managerially, the model suggests that firms should combine AI efficiency with human oversight, recognizable brand voice, clear but proportionate disclosure, verifiable claims, and accessible explanations of AI’s role. The article concludes with a detailed empirical validation agenda using experimental and structural-equation approaches. Generative artificial intelligence (GenAI) is rapidly becoming part of the marketing communication infrastructure, yet its ability to create efficient and visually persuasive content has generated a parallel concern: consumers may question whether machine-assisted messages are genuine, accountable, and worthy of trust. This conceptual study examines how the perceived authenticity of AI-generated marketing content may shape brand trust and purchase intention, with perceived brand transparency proposed as a central mediating mechanism. Drawing on Signaling Theory and the Stimulus–Organism–Response (S–O–R) framework, the paper integrates recent evidence on AI authorship, disclosure, brand authenticity, transparency, algorithm aversion/appreciation, and consumer trust. The proposed model argues that authentic-seeming AI content can function as a credibility cue that reduces ambiguity surrounding algorithmic authorship. Consumers who interpret AI-generated content as coherent with a brand’s identity, honest in origin, and non-manipulative are more likely to infer that the brand communicates openly and voluntarily. Such perceived transparency should strengthen brand trust and, in turn, purchase intention. The framework also allows for direct effects of authenticity on trust and purchase intention, creating a serial mediation pathway from perceived authenticity to transparency, trust, and behavioral intention. The study contributes by separating content authenticity from brand transparency, explaining why transparency is not equivalent to mere AI disclosure, and reconciling conflicting findings showing that disclosure can either strengthen or weaken consumer responses. Managerially, the model suggests that firms should combine AI efficiency with human oversight, recognizable brand voice, clear but proportionate disclosure, verifiable claims, and accessible explanations of AI’s role. The article concludes with a detailed empirical validation agenda using experimental and structural-equation approaches. https://eabj.ir/downloadfilepdf/13260