Board of director performance: a group dynamics perspective
Bibliographic record
Abstract
Purpose This paper proposes mainly that boards of directors (BOD) are teams that share characteristics with many other kinds of teams. As a consequence, some of the factors that lead to board effectiveness are the same factors that lead to team effectiveness in general. By integrating the organizational behaviour literature on teams with the governance literature, a comprehensive model of BOD performance is proposed. Design/methodology/approach This conceptual paper proposes a model to assess the performance of a board and situates board performance as one input into firm performance. Findings This paper outlines the dynamic interplay between board characteristics, functionality and performance and proposes a comprehensive model, based largely on the group dynamics literature. Research limitations/implications Suggests that future research attempt to empirically address some (or all) of the items in the conceptual model. Acknowledges that operationalizing certain variables will prove challenging, but suggests that ethnographic accounts of how these variables (and potentially others) interact may be a valuable first step in more fully understanding board composition, functioning and performance. Practical implications It is argued that by extending traditional passive agency roles, BOD may be able to provide a wider range of contributions to enhance shareholder value. Originality/value This interdisciplinary paper integrates the group dynamics literature with the governance literature to propose a comprehensive model of BOD performance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".