Examining Deep-Level Diversity in Top Management Teams: Team Power Distribution and Team Cognitive Diversity
Bibliographic record
Abstract
This empirical study draws on research in sociology and cognition to examine the relationship between two deep-level diversity constructs: team power distribution and team cognitive diversity. Team power distribution reflects the extent to which power is distributed among team members evenly (team power equality) or unevenly (team power inequality). Team cognitive diversity reflects the extent to which strategic beliefs are held in common by the whole team (strategic consensus) or subgroups within the team (strategic dissent). Rather than using demographic measures as proxies for team cognition, this study employs a cognitive elicitation method to capture the mental models of 342 top managers from 49 US hospitals. I find that distinct power patterns are associated with distinct patterns of cognitive diversity, which suggests that the extent to which power is distributed within the team may affect the extent of strategic consensus and dissent within top management teams (TMTs). These findings contribute to the growing literature on group processes and managerial cognition.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".