Dignity, face, and honor cultures: A study of negotiation strategy and outcomes in three cultures
Bibliographic record
Abstract
Summary This study compares negotiation strategy and outcomes in countries illustrating dignity, face, and honor cultures. Hypotheses predict cultural differences in negotiators' aspirations, use of strategy, and outcomes based on the implications of differences in self‐worth and social structures in dignity, face, and honor cultures. Data were from a face‐to‐face negotiation simulation; participants were intra‐cultural samples from the USA (dignity), China (face), and Qatar (honor). The empirical results provide strong evidence for the predictions concerning the reliance on more competitive negotiation strategies in honor and face cultures relative to dignity cultures in this context of negotiating a new business relationship. The study makes two important theoretical contributions. First, it proposes how and why people in a previously understudied part of the world, that is, the Middle East, use negotiation strategy. Second, it addresses a conundrum in the East Asian literature on negotiation: the theory and research that emphasize the norms of harmony and cooperation in social interaction versus empirical evidence that negotiations in East Asia are highly competitive. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".