How to craft humorous advertisements across diverse cultures? Multi‐country insights from Brazilian, Chinese and American consumers
Bibliographic record
Abstract
Abstract In today's multicultural environment, the influence of cultural orientations on humorous advertising outcomes is increasingly significant for researchers and practitioners. Drawing on a theoretical framework from culture dimensions and theories of humour in advertising, this research examines the moderating role of cultural orientation on the relationship between humour styles and consumers' attitudes towards humorous ads. Empirical results from studies conducted in Brazil, China and the United States show that uncertainty avoidance moderates the effect of cognition‐evoking humorous ads on consumers' responses to the ads, while individualism–collectivism moderates the effect of affect‐evoking humorous ads on these responses. Specifically, consumers from countries with high uncertainty avoidance (e.g., Brazil) have more favourable attitudes towards and perceive greater humour from cognition‐evoking humorous ads with closure than those from countries with low uncertainty avoidance (e.g., China, United States). In addition, consumers from collectivist countries (e.g., China, Brazil) have more favourable attitudes towards and perceive greater humour from affect‐evoking humorous ads with closure than do those from individualist countries (e.g., United States).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".