A SYSTEMATIC METHOD TO ARTICULATE STRATEGIC VISION: AN ILLUSTRATION WITH A SMALL BUSINESS OWNER-MANAGER
Bibliographic record
Abstract
The concept of strategic vision has attracted growing interest in recent years. However, very few methods exist to help enterprise leaders make their strategic vision more explicit. The goal of this research is to present and illustrate a new systematic method to help enterprise leaders articulate and question their strategic visions. The method is based on cognitive mapping, and can be broken down into four phases: an exploration phase, in which the leader explores his or her own ideas using a specially designed grid; a validation phase, to verify the credibility or "validity" of the cognitive map resulting from the first phase; an analysis phase, where the semantic network formed by the concepts and links of the map are analyzed using the Decision Explorer software; and a finalization phase, where the leader, after being informed of the results of validated map analysis and the researcher's interpretation of them, confirms or modifies the strategic vision. The paper contains a detailed description of the four phases together with the results obtained with the owner-manager of a small manufacturing business in Québec. Since the method described supports thinking and thus action, it is likely to be of interest to practitioners and consultants. Academics may also benefit from it, since it can be used to study strategic vision and its impact.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".