Effect of Leadership Style, Motivation, and Giving Incentives on the Performance of Employees—PT. Kurnia Wijaya Various Industries
Bibliographic record
Abstract
This study aims to identify and examine the importance of leadership style, motivation, and incentives to improve employee performance. Variables examined as factors that affect performance of employees were style of leadership (X1), motivation (X2), and the provision of incentives (X3). The population of this study was all employees in the Sales Department MT PT. Kurnia Wijaya Various Industries, amounting to 20 people. Data collection techniques were documentation and questionnaires. This study also used multiple linear regressions to analyze the data. It indicates that the level of the relationship between leadership style (X1), motivation (X2), and the provision of incentives (X3) on employee performance (Y) is very strong. This is because the value of the correlation coefficient is R = 0,985a is in the interval between 0.80 ̶ coefficient of 1.000. While the coefficient of determination of R2 = 0.971, indicating that the independent variable (leadership style, motivation, and incentives) can influence the dependent variable (performance of employees) at 97.1% while the remaining 2.9% is influenced by other factors not examined.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".