The Impact of Generative AI Images on Consumer Attitudes in Advertising
Lei Zhang, Chung Hur
Abstract
While the capability of generative AI to generate high-quality content is well-recognized, there is still a lack of in-depth research on its actual impact on marketing effectiveness within real-world marketing environments. This study addresses this gap by conducting experiments to examine the effects of AI-generated advertisement images, created using text-to-image diffusion models, on consumer responses and the boundary conditions of these effects. Study 1 (n = 130) found that for coffee ads, attitudes were descriptively higher toward AI-generated images (ηp2 = 0.17), whereas for medical-aesthetics and public-service ads, evaluations favored human-made images; none of these differences reached significance. Study 2 (n = 79) revealed that when consumers were informed about the source of the image (AI or human), they showed significantly more positive attitudes toward human-made images than those generated by AI (d = 0.52). Study 3 (n = 209) demonstrated that in commercial advertising contexts where usage motivations were disclosed, consumers’ negative reactions to AI-generated images were moderated by the specific usage motivation (η2 = 0.04). When the motivation was privacy protection, evaluations were comparable to human-made images. In contrast, visual appeal produced slightly lower but non-significant ratings, whereas cost efficiency led to significant declines in trust and purchase intention, with attitude showing only marginal decreases and preference no differences. This study aims to understand the innovative potential of generative AI and provides critical insights for businesses, consumers, and policymakers regarding its effective utilization.