Emotional Dynamics in Conflict and Negotiation: Individual, Dyadic, and Group Processes
Bibliographic record
Abstract
Conflict is an emotional enterprise. We provide an integrative synthesis of theory and research on emotional dynamics in conflict and negotiation at three levels of analysis: the individual, the dyad, and the group. At the individual level, experienced moods and emotions shape negotiators' cognition and behavior. At the dyadic level, emotional expressions influence counterparts' cognitive, affective, and behavioral responses. At the group level, patterns of emotional experience and/or expression can instigate cooperation, coordination, and conformity, or competition, conflict, and deviance. Intrapersonal (individual-level) effects of diffuse moods can be explained by affect priming and affect-as-information models, whereas effects of discrete emotions are better explained by the appraisal-tendency framework. Interpersonal (dyadic- and group-level) effects of emotions are mediated by affective (e.g., emotional contagion) and inferential (e.g., reverse appraisal) responses, whose relative predictive power can be understood through the lens of emotions as social information (EASI) theory. We offer a critical assessment of the current literature, discuss practical implications for negotiation and conflict management, and sketch an agenda for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".