Determinants that influence cargo performance in the airport industry with knowledge sharing and knowledge management as mediating variables
Bibliographic record
Abstract
As one of the busiest airports in the world, organizational performance is the main focus. Problems that occur in organizations are many employees who complain about their work, employees who cannot fully comply with applicable regulations and policies, many employees who often come late to the office and do not comply with working hours according to the work times determined by the company. This research aims to analyze and test the influence of Quality of Work Life (QWL) and Knowledge Management on Organizational Citizenship Behavior which is mediated by Job Satisfaction, moderated by Knowledge Sharing and Self Efficacy at Angkasa Pura. The population in this research was Angkasa Pura Indonesia employees, totaling 151 people. The sample used was 110 employees which was calculated based on the Slovin formula. The sampling method uses convenience sampling. The data collection method uses a survey method, with the research instrument being a questionnaire. This research method uses the Partial Least Square (PLS) data analysis method using SmartPLS 3.0 software. The research results show that the quality of work life has no effect on organizational citizenship behavior. Knowledge management has a positive and significant effect on organizational citizenship behavior. Quality of work life and Knowledge management has a positive and significant effect on job satisfaction. Job satisfaction has a positive and significant effect on organizational citizenship behavior. Job satisfaction is unable to mediate the influence of quality of work life on organizational citizenship behavior. Job satisfaction is able to partially mediate the influence of knowledge management on organizational citizenship behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".