Personality and Integrative Negotiations: A Hexaco Investigation of Actor, Partner, and Actor–Partner Interaction Effects on Objective and Subjective Outcomes
Bibliographic record
Abstract
The present study sought to expand the literature on the relations of major dimensions of personality with integrative negotiation outcomes by introducing the HEXACO model, investigating both effects of the negotiators’ and their counterparts’ personality traits on objective and subjective negotiation outcomes, and investigating two interactions between the negotiators’ and counterparts’ personalities. One hundred forty–eight participants completed the HEXACO–100 measure of personality. Participants then engaged in a dyadic negotiation task that contained a mix of distributive and integrative elements (74 dyads). Measures of subjective experience and objective economic value were obtained, and actor–partner interdependence models were estimated. Personality was generally a better predictor of subjective experience than objective economic value. In particular, partner honesty–humility, extraversion, and openness predicted more positive negotiation experiences. An actor–partner interaction effect was found for actor–agreeableness by partner–honesty–humility on economic outcomes; agreeable actors achieved worse (better) economic outcomes when negotiating with partners that were low (high) on honesty–humility. © 2018 European Association of Personality Psychology
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".