Content Analysis of Passengers’ Perceptions of Airport Service Quality: The Case of Honolulu International Airport
Bibliographic record
Abstract
This paper explores passengers’ perceptions toward airport service quality through a content analysis. Using 1341 review comments posted on the Skytrax website, we identify satisfiers, dissatisfiers, and performance factors that determine passengers’ experiences at the Honolulu International Airport and the world’s leading airports (Singapore Changi Airport, Haneda Airport, Incheon International Airport, Hamad International Airport, and Hong Kong International Airport). The results show that the Honolulu International Airport needs to improve cleanliness of the facilities, signage, and staff courtesy. A context-specific examination reveals that security, check, flight, line, and staff are the most frequently occurring words used by dissatisfied passengers. The most common words mentioned by satisfied users of the world’s leading airports include staff, terminal, clean, time, immigration, and free. These findings provide suggestions and implications concerning customers’ perspectives and may help airport managers enhance airport service quality and renovate airport facilities.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".