Determinants of airport train operational performance
Bibliographic record
Abstract
This study aims to analyze the improvement of the operational performance of Indonesia’s Soekarno-Hatta Airport train through service quality which is mediated by train passenger loyalty and passenger satisfaction. The main problems in this study are the use of the same railway for long-distance train, airport train, and commuter Line train, the limited use of airport railway with four schedules, the headway which becomes 30 minutes since the number of travels becomes 82 trips, and the tariff being applied now is considered as burdening the passengers. The research method uses a quantitative analysis approach with the technique of Structural Equation Modeling-Lisrel. Data collection is done through observation and questionnaire distribution. The respondents are 306 passengers of trains heading for Soekarno-Hatta Airport. The benefit of this study for the domestic railway company is that by improving service quality, passenger loyalty and satisfaction, it will improve the operational performance of airport trains. The result of this research indicates the significant influence of service quality variable on passenger loyalty through passenger satisfaction, the significant influence of service quality variable on operational performance through passenger satisfaction and passenger loyalty as well as the significant influence of passenger satisfaction variable on operational performance through passenger loyalty.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".