Creating sustainable performance in the fourth industrial revolution era: The effect of employee’s work well-being on job performance
Bibliographic record
Abstract
In the beginning of the fourth industrial revolution, competition in acquiring and retaining best talents in concert with talents' unfamiliarity of what they will face and obtain in the course of working at a company are two major problems. It is essential for a company to make its employees satisfied and pleased with their jobs. These very satisfaction and pleasure are hoped to serve as a key for motivating employees to perform and contribute their best for the company. Numerous research studies have proven the positive effect of work well-being on job performance, but the findings came with inconsistencies and controversies. This fact has caused reluctance in a good many companies to invest in their employees' work well-being. A survey of 509 millennial employees in the Indonesian startup digital industry was conducted in this research. The results show that employee's work well-being had a significant, positive effect on job performance. Theoretically, this research has contributed in responding to inconsistencies in literature. It is hoped that this research will also offer practical contribution to individual employees as well as human resources department and increase company executives' confidence in making organization-related strategic decisions to attain sustainable performance. Finally, we propose a number of intervention suggestions for performance improvement through work well-being.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".