Team Performance and Satisfaction: A Link to Cognitive Style Within a Process Framework
Bibliographic record
Abstract
Effective teamwork is becoming increasingly important to organizational success. Advances in network and communication technology have allowed companies to widen their potential team member base, however we still need to better understand how to structure top‐performing teams. This paper proposes forming teams based on their cognitive style, rather than personality, within a process framework. An experiment was conducted to investigate the innovative performance of problem solving groups with three different blends of cognitive styles. As predicted, groups with a heterogeneous blend of styles outperformed groups with completely or partially homogeneous blends. On the other hand, team members' satisfaction scores were lower for heterogeneous teams than either the completely or partially homogeneous teams. There was preliminary evidence that among groups with heterogeneous blends, those with smaller style dispersions might be expected to outperform those with larger style dispersions. There was also room for some speculation that a curvilinear relationship might exist for team members' satisfaction as a function of diversity in team member cognitive style. Implications of these finding are discussed.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".