Exploring consumer responses to user-generated AI ads: A comparative analysis
Jing Yang · Journal of Marketing Communications · 2025
As Gen-AI tools become more accessible, not only marketers but also consumers are beginning to leverage AI to create brand content. This shift has given rise to a new type of advertising, user-generated (UGC) AI ads. Most existing studies on AI advertising focus on marketer-generated content (MGC), the UGC AI ads remain underexplored. This study provides an early examination of the effectiveness of UGC AI ads by comparing consumer responses with those for MGC AI ads. Using topic modeling and sentiment analysis, it analyzes consumer comments on a sample of high-visibility AI-generated ads across both groups. The results show that in general user-generated AI ads are more effective at driving consumer engagement, while marketer-generated AI ads are more successful in fostering positive consumer sentiment. User-generated AI ads are seen as more original than marketer-generated AI ads. These findings reveal an engagement-sentiment trade-off between UGC and MGC AI ads. These results offer meaningful implications for both marketing researchers and practitioners seeking to understand and leverage consumer-created AI ads. This study contributes theoretically by introducing a trade-off framework for AI ad source effects, establishes boundary conditions, identifies AI-specific mechanisms, and provides actionable managerial guidance for leveraging UGC and MGC AI ads effectively.