An application of TOPSIS method for ranking different strategic planning methodology
Why this work is in the frame
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Bibliographic record
Abstract
Strategic planning is one of the most popular methods for setting up long-term objectives, which normally deals with various criteria. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method for ranking different alternatives based on various criteria. The method has been widely used among practitioners in different industries. This paper presents an empirical investigation to rank different business development strategies for information technology improvement. The study considers five different strategies including Critical Success Factors Analysis, Business Systems Planning, Porter's forces model, SWOT analysis, Value chain Analysis and MIN and rank them based on TOPSIS technique. The results of the implementation of TOPSIS has indicated that MIN method is ranked first as the most important factor followed by Business Systems Planning, Porter's forces model, Value chain Analysis, SWOT and CSF for development of strategic planning for information technology in municipality organization.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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