Bibliographic record
Abstract
Purpose This paper aims to use the argument that formal regulations alone may not be the defining factor in improving corporate governance and the decision making process of the BOD. Design/methodology/approach Based on 20 semi‐structured interviews with board chairs, members of corporate boards, CEOs, and upper/executive management at 12 Canadian companies, a bird's eye view is taken from the top. A content analysis of the interviews was performed. a clear picture about the interaction and behaviour of directors emerged. Subsequently, three objectives that are required for effective decision‐making were developed: knowledge; motivation; and transmission channels/internal control. The analysis offers three critical objectives, which all boards should endeavour to accomplish. Findings These interviews demystify board process and provide the bases for three critical objectives for effective corporate governance: ascertain and embellish the knowledge base of directors; motivate directors to share and gather information; and ensure clear and fluent transmission channels exist. Practical implications The usual board measures such as CEO duality, insider and outsider ratio, number of board members and directors' share ownership may not be the only critical determinants of board effectiveness. Originality/value Conventional notions of decision making have neglected key human faculties and individual characteristics that combine to determine organizational outcomes. This paper fulfils a need for research in the area of board processes and board decision making and provides a roadmap to improve corporate governance within organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".