Creative Automation with Artificial Intelligence in Digital Advertising: Visual Performance and Disclosure Effects across Two Open Datasets
DOI:
https://doi.org/10.64747/x410dg25Keywords:
generative artificial intelligence, digital advertising, computational creativity, synthetic images, AI disclosure, advertising attitudeAbstract
Generative artificial intelligence enables rapid production of multiple advertising variants, yet the value of such automation depends on both visual performance and audience responses when AI involvement is disclosed. This study examined these dimensions through independent secondary analyses of two open datasets. GenImageNet comprised 10,320 images, 103,200 human ratings, seven models, and eight domains; descriptive statistics and factorial ANOVAs were estimated for quality, realism, and aesthetics. A separate Instagram advertising experiment was reproduced with an analytical sample of 161 participants assigned to control, image-AI disclosure, or text-AI disclosure conditions. Ad attitude, brand attitude, source credibility, and lower perceived manipulativeness were compared using Holm correction. Generative model, image domain, and their interaction were associated with all three visual outcomes (all p < .001). The largest effect was the model effect on realism, partial eta squared = .127. Firefly 2 had the highest descriptive mean for quality (5.453; n = 240), whereas Realistic Vision had the highest realism mean (5.411; n = 2,400). In the experiment, disclosure lowered ad attitude relative to control: difference = .555, 95% CI [.218, .892], Holm p = .011. Planned contrasts were also observed for brand attitude, credibility, and lower perceived manipulativeness, although only the omnibus test for ad attitude remained significant after correcting four tests. Image- and text-specific AI disclosures did not differ. Creative automation therefore offers no universally superior model, and transparency may carry reception costs. Tool selection should be validated by domain, and disclosure should be treated as part of communication design without abandoning transparency obligations.
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