Technical capabilities and work environment in power plant operational performance: A project management assessmen
Bibliographic record
Abstract
Recognizing the critical importance of worker technical capability and the work environment is a pressing need to enhance employee performance within a power plant. Prioritizing the improvement of worker capabilities and fostering favorable work environments is urgently required for project managers to align their workforce with project objectives and enhance overall operational results. This study aims to explore the impact of key operational and human resource management (HRM) practices on the performance of the Tanjung Jati B Steam Power Plant in Jepara, Indonesia. A sample of 110 employees was selected through a simple random sampling method, and data analyzed by multiple regression analysis with SPSS software. The results reveal significant effects of work ability, work discipline, and work environment and employee performance. Furthermore, an ANOVA test underscores that these three independent variables collectively have a positive and significant impact on employee performance. This research emphasizes the critical role of HRM practices and operational aspects in enhancing power plant performance. In the context of project management, these findings highlight the significance of developing and maintaining a workforce with the necessary skills, discipline, and conducive work environments to ensure the successful execution of operational success of a power plant. By comprehending the impact of worker technical capability on employee performance, project managers can make informed decisions and implement strategies to enhance project outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".