The Impact of Mixed Emotions on Creativity in Negotiation: An Interpersonal Perspective
Bibliographic record
Abstract
Creativity is critical to organizational success. Understanding the antecedents of creativity is important. Although there is a growing body of research on how (mixed) emotions affect creativity, most of the work has focused on intrapersonal processes. We do not know whether contrasting emotions between interacting partners (i.e., interpersonal mixed emotions) have creative consequences. Building on information processing theories of emotion, our research proposes a theoretical account for why interpersonal mixed emotions matter. It hypothesized that mixed- (vs. same-) emotion interactions would predict higher collective creative performance. We tested the hypothesis in two-party integrative negotiations (105 dyads). We manipulated negotiators' emotional expressions (angry-angry, happy-happy, angry-happy dyads) and measured the extent to which they generated creative solutions that tapped into hidden integrative potential in the negotiation for a better joint gain. The results overall supported the hypothesis: (i) there was some evidence that mixed-emotion dyads (i.e., angry-happy) performed better than same-emotion dyads; (ii) mixed-emotion dyads, on average, achieved a high level of joint gain that exceeded the (non-creative) zero-sum threshold, whereas same-emotion dyads did not. The findings add theoretical and actionable insights into our understanding of creativity, emotion, and organization behavior.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".