A Critical Evaluation of U.S. Airlines’ Service Quality Performance: Lower Costs vs. Satisfied Customers
Bibliographic record
Abstract
Providing good quality services enables airlines to retain customer satisfaction, loyalty, market-share, and ultimately profitability. However, U.S. airlines compete primarily on price and are not known for good quality service. There have been a growing number of low-cost airlines. In such a business landscape, we study whether a full-service carrier indeed outperforms a low-cost carrier in terms of service quality when we control for the operational costs. We are also interested to find out which dimensions of service quality have the greatest potential for improvement and how these potential improvement areas differ for low-cost and full-service carriers. We contribute to the service operations literature that looks at efficiency by incorporating customer service quality outputs which has never been done before for the airline industry. We find that major airlines in the industry are lacking staff enthusiasm, adequate cabin presence, and behavioral consistency. Moreover, 33.3% of firms need to deliver more comfortable seats, better meals, in-flight entertainment, and cleaner surroundings. On the other hand, notably, U.S. airlines are operating quite efficiently when it comes to service supply chain quality. We also provide managerial guidelines for U.S. airlines to improve their service quality and overall customer satisfaction.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".