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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".