Personality and negotiation revisited: toward a cognitive model of dyadic negotiation
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the studies of personality and negotiation and argues that the relationship between personality and negotiation is worth re‐examination and more research attention should be devoted to this area. Design/methodology/approach A cognitive model of personality and negotiation is constructed by integrating cognitive and social factors into the exploration of negotiation processes. The mediating roles of negotiator cognitions are discussed within this framework and relationships between personality and three negotiator cognitions: win–lose orientation, face‐saving and trusting are proposed. Research limitations/implications This study provides an integrative model for studying the relationship between personality, negotiator cognition, negotiation behaviors and outcomes, and thus has impotent implications for future studies on negotiation. Practical implications The knowledge of the relationship between personality and negotiation will help organizations use personality assessment for better decisions about selection, promotion and training for improvement in negotiation skills. Originality/value This study attempts a complete exploration on the framework that integrates personality factors and negotiation behavior and outcomes, and provides potential directions for future studies on personality and negotiation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".