Starting Out on the Right Foot: Negotiation Schemas When Cultures Collide
Bibliographic record
Abstract
Abstract We investigate the intercultural negotiation schemas of 100 experienced Japanese and U.S. negotiators. Specifically, we examine the assumptions negotiators make about appropriate behavior when primed to negotiate with an intercultural (vs. intracultural) counterpart. We find that intercultural negotiation schemas clash on six of nine elements, meaning U.S. and Japanese negotiators have significantly different expectations about what it is like to negotiate with the other. This clash occurs not because negotiators stay anchored on their own cultural assumptions about negotiating, but rather because they try to adjust to their counterpart’s cultural assumptions about negotiating. But negotiators adjust their schemas by thinking about how their counterpart negotiates in an intracultural rather than intercultural setting. That is, they fail to account for the fact that their counterpart would also adjust expectations for the intercultural context. The phenomenon we uncover is one of schematic overcompensation, whereby negotiators’ intercultural schemas do not match because each negotiator expects the encounter to be just like the counterpart’s within‐culture negotiations. Our theory of schematic overcompensation receives some support, and negotiators’ perceived knowledge and experience with the other culture somewhat attenuates the phenomenon. Implications for negotiator cognition, intercultural negotiation, and global management are discussed.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".