When AI Doesn’t Sell Prada: Why Using AI-Generated Advertisements Backfires for Luxury Brands
Bibliographic record
Abstract
The current research uncovers the potential negative consequence of utilizing AI-generated imagery in luxury brands’ advertising efforts. Across three experiments (field and lab studies) using only AI-generated ads, the authors find that when luxury brands feature and disclose the use of AI-generated imagery in their advertisements, consumers respond to the ads more negatively (Study 1). The results further reveal the underlying rationale for this negative outcome: AI-generated advertisements are perceived to be made with lower effort, which results in AI-generated luxury ads being evaluated as less authentic of the brand (Study 2). Finally, the authors explore a potential strategy that mitigates the negative impact of disclosing the use of AI-generated imagery on luxury ads’ evaluations (Study 3). Specifically, the authors find that the negative outcomes associated with AI-generated luxury ads are attenuated when luxury brands use generative AI to generate highly creative ad imagery rather than standard creative ad imagery. This research highlights how luxury brands should strategically approach AI usage and the important managerial implications of employing generative AI in brands’ advertising efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".