Factors affecting employees’ motivation
Bibliographic record
Abstract
This study primordially aimed at determining the effects of different factors affecting employee’s motivation at Groz-Beckert Vietnam Co. Ltd. Upon perusal and review of related literature, the researchers found out that there is a dearth of studies that have been conducted relative to this subject appertaining to MNCs in Asian countries. Thus, this paper intends to provide empirical evidences towards the link between and among selected variables being covered in this study. The researchers used the quantitative research analysis method which have surveyed and floated questionnaires to 300 employees from various units, departments, divisions at Groz-Beckert Vietnam Company Ltd and retrieved 265 validated responses. Research result further revealed that, out of the 7 factors: income and benefit, superior, work result, colleague, work condition, training and promotion, work nature positively and significantly impacts the staffs and workers of the said organization. Based on research results and findings, the researchers will provide some viable and palatable recommendations for the human resource development policies and such proposals can enhance employee’s motivation in the said entity. For ease of data collection, convenience sampling was adopted. Data were analyzed using SPSS version 21 that included descriptive and inferential statistics. The outcomes of the study showed that almost all the variables have positive correlation with the workers motivation in performing their jobs at the workplace.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".