Can emotional differences be a strength? Affective diversity and managerial decision performance
Bibliographic record
Abstract
Purpose – The purpose of this paper is to extend earlier findings suggesting that affective diversity is always negative for group performance, by examining its influence on managerial decision performance in a more controlled environment. Design/methodology/approach – In an attempt to mitigate some of the many methodological challenges associated with studies in “real-word” contexts, the authors chose to adopt a quasi-experimental research design involving teams of master of business administration students engaged in managerial decision making. This research design is consistent with previous research conducted in the area of affect and individual or group-level outcomes. Findings – The results indicate that both positive and negative affective diversity are positively associated with managerial decision performance, although only the relationship with negative affective diversity is significant. Overall, these findings support the idea that affective diversity may constitute a strength in the context of managerial decision making. These results contrast with the findings of previous studies. Research limitations/implications – Further quantitative and qualitative investigation is recommended in order to clarify the contradictory results between the current study and previous research. Specifically, this investigation might concern the effect of contingency factors such as type of team (i.e. ad hoc vs long term), type of task and team-level self-regulation ability. Originality/value – Since the seminal work of Barsadeet al.(2000), no further studies have attempted to resolve some of the empirical questions emerging from preliminary research on affective diversity. The paper thus provides new insights into the effects of affective diversity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".