The top team: examining board composition and firm performance
Bibliographic record
Abstract
Purpose This paper seeks to argue that boards can be playing a more proactive role in contributing to organizational effectiveness and that their composition requires greater research attention. By integrating the organizational behaviour literature on teams with the governance literature, the paper empirically examines the relationship between key board composition variables and firm performance. Design/methodology/approach At this stage in the development of the approach, the focus is on a sub‐set of the elements proposed in the group dynamics literature. The population for this study comprises all companies included in the Canadian TSE 300 Composite Index (renamed the S&P/TSX Composite Index). This study uses cross‐sectional regression analyses to examine the nature of the relationships between board composition and firm performance. Findings The data analyses revealed that high levels of experience, appropriate team size, moderate levels of variation in age and team tenure were correlated with firm performance. Research limitations/implications Boards of directors (BOD) are teams whose effectiveness can be assessed through group dynamic constructs in the organizational behaviour literature. Further research is needed to examine the intricate dynamics that might moderate or mediate the relationship between board characteristics and firm performance. Practical implications The findings provide a much‐needed benchmark to consider whether the composition of boards is optimal, given the functions and mandate. In addition, the study highlights the opportunity costs of boards, restricting their roles to agency issues. Originality/value This interdisciplinary paper tests some of the many variables that can be extrapolated from the group dynamics research. The paper calls on boards to examine what BOD functionality really entails, and argues for more proactive behaviours aimed at strategic firm issues.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".