The effect of job stress to employee performance: Case study of manufacturing industry in Indonesia
Bibliographic record
Abstract
Abstract The manufacturing industry in Indonesia is proliferating, contributing almost a quarter of Indonesia’s gross domestic product (GDP). This development encourages companies engaging in the industry to increase production capacity. As a result, the employees are demanded to work harder as the manufacturing sector rises. Overwork can result in fatigue, stress and various health problems. However, work stress is a common problem faced in almost all industries and often affects employee performance. Therefore, the main purpose of this study is to analyze the effect of work stress on employee performance in the manufacturing industry in Indonesia. The sample of this research is 93 employees at the staff level who work in various manufacturing companies. This study uses partial structural data analysis techniques using SPSS version 20.0. These techniques are used to analyze the effect of work stress and work environment on employee performance. The results showed that work stress and work environment has a significant impact on employee performance with the value of R = 0.972. Based on the survey result, non-standard working hours and poor relationship with colleagues/superiors contribute to the creation of work stress which has an impact on low performance. Thus, this study suggests the organization to perform a proper stress management as a solution to work stress by implementing flexible working hours and holding discussion forums and meetings between employees. Therefore, employees will be more motivated to improve work productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".