The Impact of Mixed Emotions on Creativity in Negotiation: An Interpersonal Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it