Using different ELECTRE methods in strategic planning in the presence of human behavioral resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the multicriteria strategic planning of an organization, management should often be aware of employees' resistance to change before making new decisions; otherwise, a chosen strategy, though technologically acceptable, may not be efficient in the long term. This paper, using a sample case study within an organization, shows how different versions of ELECTRE methods can be used in choosing efficient strategies that account for both human behavioral resistance and technical elements. The effect of resistance from each subsystem of the organization is studied to ensure the reliability of the chosen strategy. The comparison of results from a select number of compensatory and noncompensatory models (ELECTRE I, III, IV, IS; TOPSIS; SAW; MaxMin) suggests that when employee resistance is a decision factor in the multicriteria strategic planning problem, the models can yield low-resistance strategies; however, ELECTRE seems to show more reasonable sensitivity.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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