Corporate board decision-making: applying collective versus personal values
Bibliographic record
Abstract
Purpose This paper aims to offer a solution to the dilemma of board members using their personal values to drive decision-making and strategy. Board members are asked to discuss the collective values at the onset of strategy planning. Design/methodology/approach Six questions, developed over a 15-year period of working in the area of strategy and governance, unite research on values in organizations, and provide a guide for arriving at a set of agreed-upon values for decision-making. Findings Two examples from practice showcase how agreeing on values before beginning the strategy process has assisted boards with better decision-making. Research limitations/implications The questions and process are meant to be a reflective tool for board members to consider when discussing values and decision-making rather than predicting behaviour or explaining outcomes. The process is most effective for boards whose culture supports a desire for improvement and therefore a willingness to experiment with new processes. The process can be enhanced by using an external facilitator having the ability to extrapolate meaning as the discussion unfolds. Practical implications This work empowers board members to be more effective in assessing strategic options and in communicating the inner logic and meaning of the strategy throughout the organization and to the external stakeholders. Originality/value Advocating that boards engage in focused discussion around values at the beginning of the strategic process improves decision-making and provides a litmus test for evaluating the strategic options. Agreeing on a set of values also makes board members more aware of the implications of each option in the long term.
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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 | 0.043 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".