Awareness of Employee Compensation and its Effect on Employee Motivation
Bibliographic record
Abstract
Motivation of the employees plays an essential role to help an organization achieve effectively the objectives in terms of productivity and commitment of its employees. Considering the importance of employees’ motivation, we conducted a research in Nangarhar province of Afghanistan to find whether employees are really affected by the compensation. In other words, what factors can influence the motivation of employees within a company? The data for the study is randomly obtained from 350 employees of distinct private and public organizations through five-likert scale adopted questionnaire. To obtain consistent study results, the ordinary least square an econometric assessment method was used. The results show that rewards have positive and statistically significant impacts on the motivation of employees. Our findings also show that the impact on employee motivation is positive on financial and non-financial benefits. This means that organizations provide their employees with both financial and non-financial benefits, thus strengthening employee motivation. However, the findings also indicate that intrinsic rewards, extrinsic rewards, and job satisfaction have a considerable influence on employee motivation. Therefore, we strongly recommend both private and public organizations to motivate their employees through compensations.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".