High‐low context cultures and price‐ending practices
Bibliographic record
Abstract
Purpose Retail prices ending in 0, 5 (even ending), and 9 (odd ending) are common in western countries. The purpose of this paper is to explain variances in odd versus even ending practices in western versus non‐western countries, using Hall's high‐low context construct. Design/methodology/approach A survey of web‐posted prices in ten countries is conducted. Findings Relative to their counterparts in low context, western cultures, consumers in high context, non‐western cultures may be less prone to the illusion of cheapness or gain created by odd endings, and more likely offended by such perceived attempts to “fool” them. Thus, odd endings are predicted to operate at a higher level of value significance to consumers, and to occur less frequently relative to even endings, in high than low, context cultures. Data support the predictions. Research limitations/implications Additional empirical studies are recommended to further test the proposed theory. Practical implications Western firms need to be cautious when replicating odd ending practices in non‐western markets. Even ending is a “safer” pricing format. Odd endings, if used, should convey cheapness or gain that is more “real”. Originality/value The research results indicate that the results of western‐based consumer research cannot be treated as universally applicable. The high‐low context theory supplements prior theories for price ending patterns in non‐western countries, and those based on perceptions and affect in the west. The study also demonstrates the usefulness of the web method in international pricing research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".