The effects of self‐emotion, counterpart emotion, and counterpart behavior on negotiator behavior: a comparison of individual‐level and dyad‐level dynamics
Bibliographic record
Abstract
Abstract This study expands the negotiation literature by examining how negotiator behavior is predicted by various emotions felt by the negotiators and their counterparts and by counterpart negotiation behavior. Using hierarchical linear modeling, we also compare individual‐ and dyad‐level processes that lead to negotiator behavior and outcomes. The results from a dyadic negotiation simulation showed that both the valence and agency of negotiator and counterpart emotions need to be considered to understand the roles of emotion in negotiator behavior. Negotiators tend to reciprocate counterparts' integrating, compromising, and dominating behaviors, but they also offer complementary (or matching) responses to the counterparts' dominating and yielding behaviors. Integrating behavior was more dependent on dyad‐level interpersonal dynamics than were the other behaviors. The comparison of negotiator‐level and dyad‐level results suggests that negotiation needs to be understood in the context of collective exchanges as well as individual‐level cognitive processes. Copyright © 2005 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".