Career development, work compensation and workload impact on work motivation by job satisfaction mediation in airline companies
Bibliographic record
Abstract
With satisfaction of job acting as a crucial mediating variable in the link between work compensation, career development, and Work Load, this study attempts to examine how these elements affect work motivation at Xyz Aviation Transportation Company. All 130 workers at the Jakarta-based Xyz Aviation Transportation Company, including managers, supervisors, engineers, marketers, administrative staff, and field workers, made up the study's population. The Structural Equation Model and Smart-PLS methodologies are utilized in an explanatory quantitative research design with total sampling (census sampling) as the sample methodology. Finding the influence value of the relationship between each study variable is the anticipated research outcome. Effect Size is a measure of the relationship's effect value between the research variables. The managerial implications, which include recommendations, follow-up, and tactics that company management must implement to establish dynamic working settings and circumstances with high job satisfaction and work motivation, will be prepared once the Effect Size value has been determined. It is anticipated that this study would offer insightful suggestions to the management of Xyz Aviation Transportation Company for developing practical plans to boost worker motivation and job happiness, which will enhance the caliber of services offered by the company.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".