Systematic Analyze-Weight-Evaluate (AWE) Approach into Decision Making: A Derivation via Externative Organizational Factors
Bibliographic record
Abstract
Research reveals the externative organizational factors, their impact on the weight of managers in the decision-making process for a development and creation of sustainable leaderism.Emphasizing that the factors of the managerial environment constantly have an effect and produce changes, bringing challenges for managers to make decisions.Research will analyze their impact and the attention that managers pay to this unstructured and non-routine dimension of decisions.This research is based on the derivation of analyzes through the Correlational Field Study (CFS), the use of some models for measuring impact and sustainability such as General Linear Model (GLM) the analysis of consistency index (CI) measurements for decision making (DM) through the Analytical Hierarchy Process (AHP).Research highlights the SEM-PLS approach by closely identifying the inter-connection and the weight of the interlinkage between externative factors and decision-making.Study was conducted in 100 study organizations in Kosovo.Firstly, brings the correlation analysis between the factors by looking more closely at their correlation and decision making, secondly the impact on the weight that these factors lading during managerial analyses, thirdly through the AHP method we highlight the clear analysis of the consistency index (CI) and random consistency (CR) proving that decision making is influenced day-to-day by extern factors such as: uncertainty, risk, turbulence dynamics etc. Inevitably be considered for future research the new era of business peripherically changes such: competitiveness, ambiguity and ambidextrous.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".