A Consumer Perspective of Service Quality in the Airline Industry
Bibliographic record
Abstract
The airline Service quality has received much attention from both academicians and practitioners. Various studies have used SERVQUAL, AIRQUAL, the Kano Model, etc. for measuring the customer service quality in the airline industry. However, a review of the airline service quality literature shows a lack of research about the use of latent semantic analysis (LSA) in uncovering the underlying factors affecting the quality of service provided by the airline companies. The purpose of this study is to explore the generic service quality characteristics pertaining to the airline industry by mining the comments provided by the passengers of various airline companies across the globe. Passengers are under no pressure to express their concerns, opinions, or suggestions for improvement of service quality. Therefore, we posit that the customers’ comments are reflections of their perception of quality of service that they have already experienced. This study will help the stakeholders better understand the characteristics of service quality in the airline industry. The findings will provide managers in the airline industry with insights for managing and improving the quality of service rendered to their customers. We collected 1,069 customer comments on eleven airline companies and conducted an LSA on them to identify five factors affecting the service quality in the industry. The findings suggest that caring and friendly crews, luggage handling, in-flight meals, in-flight entertainment, and service expectation are the five critical factors of the airline service quality in the eyes of the customers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".