Strategic Orientations: Multiple Ways for Implementing Sustainable Performance
Bibliographic record
Abstract
The Four Phase Model®, created by prof. dr. Teun W. Hardjono [1] in 1995, distinguishes four ideal type strategic orientations and shows that these strategies brighten and dim in a specific sequence, adding the most required competences to the organization, and creating a natural rhythm to corporate dynamics. By applying this theory one can understand the nature and whereabouts of the organization’s systemic constraints, revealing the basic features for creating a roadmap towards sustainable performance improvement and competence development. The model generates the top priorities, selects the most adequate (ideal type) interventions and key performance indicators. Combining strategic “situations” as indicated by the Four Phase Model and phase-wise “contexts” as introduced by Spiral Dynamics [2], provides a conceptual synergy with four innovative outcomes: Firstly, aligned with specific contexts, the strategic interventions and KPI’s can be made more specific and practical, thus creating a roadmap for performance improvement and organizational development. Secondly, it structures change management into four distinctive hierarchical complexity levels: 1) enhancing fundamental skills, structures and procedures (vitalizing); 2) improving contemporary levels, aligned with the dominant value system (optimizing); 3) new re-orientations while continuing within current systems (shifting) and 4) a transformation to a more complex context or emerging value system (transforming). Thirdly, powered with the combined understanding of above concepts, one can deduct the specific context and situation for each intervention, instrument or approach to be applied effectively. Fourthly, the combination provided the bases for the so-called Strategy Scan and Strategic Sustainability Scan.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".