The impact of web check-in and service quality on reuse intention: The mediating role of passenger trust
Bibliographic record
Abstract
The intention to reuse Garuda Indonesia Airline became the leading indicator in the minimum standard of air transport services. The main problems in this research were the less attractive promotion efforts that hindered passengers from reusing Garuda Indonesia Airline and unclear web check-in procedures, which were difficult for some passengers to understand. This research aimed to know the direct and indirect influences of service quality and web check-in on the intention to reuse Garuda Indonesia Airline mediated by passenger trust at Soekarno-Hatta Airport. The research was quantitative, with the data collecting method using a questionnaire distributed online through a purposive sampling technique to 210 passengers of Garuda Indonesia Airline. The data was analyzed using the partial least squares-structural equation modeling (PLS-SEM) method. This research showed five direct influences: the influence of service quality and web check-in on passenger trust, the influence of service quality, web check-in, and passenger trust on the intention to reuse Garuda Indonesia Airline. Passenger trust could mediate the influence of service quality and web check-in on the intention to reuse Garuda Indonesia Airline at Soekarno-Hatta Airport. From the results of this research, it could be concluded that service quality and web check-in through passenger trust are needed to reach the intention of reusing Garuda Indonesia Airline.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".