The impact of artificial intelligence-based advertisement placement on consumer purchase sentiment
Bibliographic record
Abstract
The advertising industry is embracing artificial intelligence technologies to improve the accuracy and effectiveness of advertisements. In today's digital marketing field, artificial intelligence (AI)-based ad implementation has become a key strategy to increase consumers' purchase intention. In terms of consumers' perceived value, how to effectively influence consumers' perceived benefits, risks and final purchase decisions through intelligent ad implantation has become a hot topic. This study explores advertisement implantation based on AI and its impact on consumers' perceived value and purchase intention. Results indicate that personalized and relevant AI-driven ad placements significantly enhance perceived benefits, thereby increasing purchase intent. Additionally, ad delivery through reputable platforms mitigates perceived risks, further fostering positive purchase sentiment. Conversely, certain ad display methods may not consistently enhance perceived benefits and could detract from consumer engagement if not well-aligned with audience expectations. These findings offer strategic insights into optimizing AI applications in digital advertising, underscoring the importance of aligning ad content with consumer expectations to maximize positive emotional responses and drive purchasing behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".