THE IMPACT OF AI-GENERATED CONTENT ON CONSUMER TRUST IN BRANDS: AN EMPIRICAL STUDY

Abstract

The article provides a systematic analytical study of the impact of artificial intelligence-generated content on consumer trust in brands. As generative AI technologies such as ChatGPT, DALL-E, and Midjourney become increasingly embedded in marketing workflows, their influence on consumer perceptions, brand attitudes, and purchasing behavior emerges as a critical area of academic inquiry. The purpose of this study is to systematically analyze and synthesize the findings of leading empirical studies from 2023 to 2025 on how AI-generated marketing content affects consumer trust, taking into account the moderating roles of product type, digital literacy, and the transparency of AI usage disclosure. The research methodology is based on a comparative analysis of results from major empirical investigations, including those conducted by the Nuremberg Institute for Market Decisions, Getty Images, NielsenIQ, Accenture, and published in leading international journals. The theoretical framework integrates the Persuasion Knowledge Model with Trust Transfer Theory and the concept of algorithmic aversion, providing a comprehensive lens for understanding how AI disclosure influences consumers' cognitive and affective evaluations of brand communications. The analysis demonstrates that explicit disclosure of AI involvement in content creation consistently reduces perceived brand trust, advertisement credibility, and purchase intentions across multiple studies and cultural contexts. This negative effect is substantially moderated by consumer digital literacy and the type of product being advertised, with hedonic products experiencing greater trust erosion than utilitarian ones. The study contributes to the literature by providing a comprehensive synthesis applicable to the Ukrainian and Eastern European markets, which remain underexplored. The practical implications include a strategic matrix for AI content deployment in brand communications that differentiates recommendations by product category and target audience characteristics, emphasizing the importance of the human-in-the-loop approach to maintain consumer trust while leveraging generative AI efficiency.