Effect of Organizational Commitment, Competence and Good Governance on Employees Performance and Quality Asset Management
Bibliographic record
Abstract
This research aims to examine and analyze the influence of organizational commitment, competence and governance to employee performance and quality asset management at the regional Work Units (SKPD) of The Makassar city government. This research is an explanatory research, by observing cross-section a on the 203 civil servants who work in the 64 Regional Work Units (SKPD SKPD) Government of Makassar, using total sampling as sampling technique. Analysis of Structural Equation Model (SEM) through Analysis of Moment Structures (AMOS) Ver. 18 is used as a data analysis tool.Hypothesis testing results provide evidence that organizational commitment, competence and good governance has a positive and significant effect on employee performance. Organizational commitments have a negative and significant effect on the quality of asset management. The different results shown on the competence, good governance and employee performance are positive and significant effect on the Quality asset management for local Governments. Organizational commitment and competence indirectly significant effect on the quality asset management for local Governments: The mediating role of employee performance. On the other mediator variable testing, good governance indirectly has a significant effect on the quality of asset management: The mediating role of employee performance.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".