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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".