Factors affecting task special employee’s motivation at the State Bank of Vietnam
Bibliographic record
Abstract
In the context of Vietnam's socio-economy is gradually developing and actively participating in the industrial revolution 4.0, the State Bank of Vietnam (SBV) is making constant efforts to realize its mission and development orientation. Human resource development plays an increasingly important role at the State Bank, that always needs to be perfected and renovated. The effective and practical system of work performance evaluation, working motivation evaluation and remuneration will be the main basis for the State Bank's human resource development plan at present and in the coming years. This study aims to evaluate the factors affecting the SBV employee’s motivation; with data collected from 454 SBV’s employees around the Vietnam, the research applied the quantitative method, using the Exploratory factor analysis (EFA), regression model and the Structural Equation Model (SEM) to determine the factors affecting the SBV employee’s motivation. Research results show that there are 5 factors (job characteristics, working environment, Reward policy, compensation and benefit, development opportunities) with different impacts on SBV employee’s motivation. Based on the results, the study provides recommendations to enhance job performance results of SBV task special employees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".