Identification and Ranking Paramount Factors Affecting the Organizational Health Using AHP Method (Case Study: Gas Transmission Office in Area 7)
Bibliographic record
Abstract
The organizational health is amongst the overriding concepts in management employed to indicate the overall condition of organizations and companies. Organizational health in national organizations would culminate into appropriate decisions, policy-making and due application of policies. Hence, the aim of this study was to identify and rank paramount factors affecting the organizational health. The data gathering instrument was researcher-made questionnaires. 500 questionnaires were distributed among the employees of Gas Transfer Office in Area 7 and 230 of them were collected and analyzed by virtue of structural equations modeling in LISREL Software. The verification procedure was carried out through the “Pearson Correlation Test” using SPSS. The ranking of aforementioned factors was carried out through the use of AHP analysis in Expert Choice Software. The results showed that there was a significant relationship between organizational health and the factors such as trust, motivation, responsiveness, reputation, capabilities, outward tendency, path-objective, collaboration, coordination, innovation, ethics, communication, commitment, leadership, performance identifying ,culture, employee effectiveness, and, resource usage. The final model was validated. Also, the final model was validated. Communication factor is ranked as the first paramount factor while capability factor is the 18th factor.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".