The The Effects of AI-Generated Content on Consumer Perceptions: A Structured Review and Conceptual Model
DOI:
https://doi.org/10.31004/riggs.v5i1.6189Keywords:
AI-Generated Content, Generative AI, Consumer Perception, Authenticity, Disclosure, Advertising, Persuasion KnowledgeAbstract
Generative Artificial Intelligence (GenAI) has fundamentally transformed marketing by enabling firms to produce persuasive and scalable marketing content at unprecedented speeds. Despite its operational advantages, consumer reactions to AI-generated content (AIGC) remain deeply ambivalent. This article provides a comprehensive synthesis of peer-reviewed evidence regarding consumer responses to AIGC and develops an integrative conceptual model to explain the psychological shifts in consumer perceptions. Using a structured search and thematic synthesis, we identify three recurring psychological mechanisms that drive these responses: (1) persuasion-knowledge activation, where AI disclosures trigger inferences of manipulative intent and strategic cost-cutting; (2) competence-fit and algorithm beliefs, which encompass the dynamics of algorithm aversion versus appreciation based on the perceived capability of the AI for specific tasks; and (3) socio-emotional inferences, specifically regarding authenticity, moral disgust, and unease. These emotional reactions are found to be especially salient when AI is positioned as the primary author of affect-laden or empathic messages. Furthermore, we consolidate critical boundary conditions at the consumer level (AI literacy, privacy concerns), the message level (appeal type, modality), and the governance level (disclosure framing, human-oversight cues). The proposed framework offers a series of testable propositions and provides actionable guidance for marketing practitioners on how to strategically deploy GenAI. Ultimately, this research emphasizes the importance of maintaining human-centric oversight to protect consumer trust and preserve long-term brand equity in an increasingly automated digital landscape.
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Copyright (c) 2026 Muhammad Aqshel Revinzky Nizar, Zidny Ilma Hasan, Kurnia Khafidhatur Rafiah

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