Rating Effective Factors on Motivating Employees for Job Persistence by Using Fuzzy AHP Method: A Case Study among the Employees of the Deqat Khodro Kousha (IPACO) Company
Bibliographic record
Abstract
The goal of this research is to prioritize effective factors on motivating employees to keep on working and determining the most important effective factors on the employees' motivation. In this paper, to grade effective factors on the employees' motivation for keeping on to work, the Fuzzy AHP method, which is one of the multi-standard decision-making methods was utilized. Field research and library research methods were used for collecting the needed information. Results indicated that among the effective factors on the employees' motivation for job persistence, the health factor is the most important and financial status is the second most important factor. The least importance is given to the significance of the work for that person. In this paper, the effective factors on the employees' motivation for job persistence were rated for the first time. Results of this research are very useful in devising strategies that are related to keeping employees for the human resources' executives. The results of this paper are not applicable to all organizations. Furthermore, in this research, only the factors with positive impacts on employees for job persistence were rated.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".