Investigating the impact of organisational cohesion on employees' productivity of Mashhad bus organisation, using the adaptive neuro fuzzy inference system
Bibliographic record
Abstract
Achieving the high levels of productivity is one of the most important ideals of managers. Organisational cohesion as an emerging concept that has many managerial requirements, and is expected to contribute to improving employees' productivity. This study used fuzzy neural networks (ANFIS) to measure this relationship, which has more predictive power than other statistical methods due to its networking architecture and learning algorithms. The statistical population was all employees of Mashhad Bus Organization (58 people). A questionnaire was used for data collection and the results were analysed using the ANFIS. The results indicated that although all aspects of organisational cohesion had an effect on improving employees' productivity and by improving them, employees' productivity would be increased, the components of fundamental values, leadership's style and coordination, would facilitate the impact of other components on productivity. It seems that they should be the prioritised on the top of the list in the organisation's administrative transformation programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".