A risk‐based approach to strategy execution
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This article integrates strategy mapping, risk management and management control into a risk‐based approach to strategy execution. It uses strategy mapping as a tool to visually depict the firm's strategy and then assess its risks. Based on this risk assessment, the firm's management control system is designed to manage those risks which are seen to have the greatest probability to negatively impact firm profitability. The proposed framework can be used on a stand‐alone basis or be used to complement Kaplan and Norton's work on strategy mapping. Design/methodology/approach This article draws from the confluence of the risk management, management control, and strategy mapping literatures to illustrate how firms can improve their handling of risk. Findings Strategy mapping is an effective tool to identify risks, while Simons' Levers of Control provides an effective alternative to manage the risks identified. Practical implications A firm's future profitability depends on its ability to identify and manage risk. Given that firms only profit when they successfully manage risk, the design and application of its management control system must flow from an assessment of the risks assumed in its strategy. The primary advantage of an integrated risk‐based management control system is that it allows managers, in real time, to steer the firm towards the good things that were outlined in its strategy and away from any bad things. Originality/value The article extends Kaplan and Norton's work by proposing strategy mapping as a tool to identify and then to help manage risks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it