The Interplay of Board Control with Board Configuration: Evidence from the UK
Bibliographic record
Abstract
The study aims to explore the control role that board directors undertake and understand the impact of several board characteristics on these roles. Building on existing literature a model was developed to test the hypothesised relationships – i.e. directors’ control role with board characteristics. The responses were collected from 115 directors in UK organisations. Principal component analysis was conducted to reduce the data and propose a set of directors’ roles and correlation as well as regression analyses are utilised in order to test the hypothesised relationships. The results of the statistical analysis propose some impact of the board characteristics on what directors do, extending the limited empirical evidence found in the literature. However, the theoretical framework needs further examination and research. The study is evidenced by various limitations. Firstly, additional constructs can be added as determinants of the directors’ control role. Secondly, the response rate in the survey is relatively low which is regarded as a limitation, although there are limited studies offering quantitative results from board members. Board of directors, board characteristics, agency theory, control role, board structure, independent directors, frequency of board meetings, board size, CEO duality, board tenure
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".