Investigation and Prioritizing the Effective Factors on Increasing the Human Resources Productivity in Agriculture Bank Using Multi-Attribute Decision Making Model
Bibliographic record
Abstract
As organizations are going to develop, the need for efficient manpower becomes more apparent. Obviously, productivity of the manpower requires the attention of managers to the complexity of human behavior and appropriate utilization of the principles, techniques and skills of the management. This study aims to prioritize the effective factors on productivity of human resources in the Agriculture Bank. Productivity is beyond the performance, it also contains the effectiveness concept, and in other words, productivity is not just doing the right things. An activity may be done correctly and in the best way, while it has no role in achieving the goal. In this case, the performance is available but there is no productivity. Difference between the performance and is rooted in the effectiveness or in the direction of doing a work. The current paper is a descriptive survey. Statistical population includes all experts in the Research and Strategic Planning center of the Agricultural Bank (33 persons). The data obtained from the questionnaire were analyzed using descriptive statistics in the form of frequency table. Questions were examined based on the one-group- t-test and using SPSS. Effective factors on increasing the human resources productivity were prioritized using Multi- Attribute Decision Making (MADM). After comparison of the alternatives, the related tables were prepared and prioritizing or ranking were done by determining the weight of each factor indexes and finally determining the weight of the four main factors. TOPSIS was used to evaluate the results of the MADM. Our research aims to prioritize the four factors according to the MADM.
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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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 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".