Contribution of work ability and work motivation with performance and its impact on work productivity
Bibliographic record
Abstract
One of the important tasks is to improve work ability, work motivation, performance and work productivity in business. The research objective is to examine the contribution and influence of work ability, work motivation, performance, and their impacts on work productivity. The research was conducted among all 105 cooperatives in the city of Pasuruan, East Java. The research method used survey methods and data analysis techniques using path analysis. The results showed that work ability and work motivation had significant effects on the performance (=0.226, Sig. = 0.000). Work ability, work motivation, and performance had significant effects on work productivity (=0.481, = 0.000). The ability to work had a significant direct effect on the performance (=0.393, Sig. = 0.000). Work ability had a significant direct effect on productivity (=0.578, Sig. = 0.043). Performance had a significant direct effect on work productivity (=0.542, = 0.000). Furthermore, work ability had a significant effect on work productivity through performance (t-value=2.083>1.983). Finally, work motivation had a significant indirect effect on productivity through performance (t-value=2.921>1.983).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".