The role of psychology capital, knowledge sharing and commitment toward managers’ performance in manufacturing company
Bibliographic record
Abstract
The performance of the manufacturing industry lies in the managers who hold crucial roles. In the revolution industry, data or knowledge holds an important role besides managers’ commitment to work optimally. As intrinsic factors, psychological capital is fundamental for managers’ behavior such as commitment and initiative to share knowledge that simultaneously enables managers’ performance. This research aimed to find the psychology capital’s effect on managers’ performance in manufacturing companies by taking into account sharing knowledge and organization commitment as moderation. Hypothesis testing was done by using data measured with a Likert Scale from 208 managers of a manufacturing company as a representative from each company stationed in the Indonesia Stock Exchange. The results of empirical testing using SEM Lisrel shows that psychological capital affects performance moderated by a variable such as managers’ commitment and knowledge sharing. Based on affected value, the initiative to share knowledge gives greater value to the correlation between psychological capital and managers’ performance in manufacturing companies; compared to commitment. Manufacturing practitioners should be able to facilitate a conducive climate to encourage their managers to share knowledge voluntarily so that the decision-making process and performance are better.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| 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".