The Effect of Visual Structure of Pictorial Metaphors on Advertisement Attitudes
Bibliographic record
Abstract
With the development of the economy, the advertising industry has also flourished, and visual metaphors have been frequently used in advertising. Through the use of visual metaphor, advertisers are eager to give consumers a deep impression, and the ultimate goal is to sell the products in the ads. Previous visual metaphor research has contributes more to understand the needs of consumers better, and offered advertisers some guidance in the advertising design. The current study works on the liking degree of people of different backgrounds toward the ads using visual metaphor of hybrid structure. This study takes 101 Chinese college students as participants to measure the extent to which they appreciate the ads using visual metaphors. 20 sets of ad pictures, covering a wide range of products, categorized conceptually and perceptually, are tested in terms of ad liking, elaboration, and comprehension. The results indicate that the visual pictures that have both similar functions and shapes rank top in all the perspectives examined, followed by those having dissimilar shape and similar function and those having similar shape and dissimilar function. The visual pictures that are dissimilar both in shape and in function are least favored. In addition, demographical analysis was performed, showing a higher appreciation for the ad visual metaphors among participants of bachelor’s degree and master’s degree, females and younger generation. Moreover, the findings, with specific marketing implication for designing and managing visual metaphors in ads, are very valuable for marketers who target at college students.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".