How language affects consumers' processing of numerical cues
Bibliographic record
Abstract
Abstract We show that linguistic numeral structures affect consumers' comparative evaluations of numbers, prices, and alphanumeric brand names. For example, 80 (eighty) in English is perceived as 4 × 20 ( quatre‐vingts or four twenties) in French and as 8 × 10 ( ba‐shi or eight tens) in Chinese. Thus, the difference between 80 and 20 is expressed with different degrees of numerosity, the number of units into which a stimulus is divided: (a) 2 × 10 versus 8 × 10 in Chinese, (b) 20 versus 4 × 20 in French, or (c) simply 20 versus 80 in English. In four studies involving a total of 732 bilinguals who speak two of these three languages, we examine how different linguistic properties can lead to differences in comparison of numerical values and inferences made about product attributes. We demonstrate the mediating role of numerosity induced by certain linguistic structures while ruling out alternative explanations for this phenomenon such as cultural differences, processing fluency, and numeracy. Our research contributes to literatures on number cognition, numerosity, branding, and linguistics while providing insights for international marketers by encouraging practitioners to use different numbers in their marketing, branding, and pricing efforts in ways that best fit the linguistic structure of the country in which they sell a product.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".