U.S. Airline Customer Complaint Trends: A Decade-Long Analysis (2013-2022) Including COVID-19 Impacts
Bibliographic record
Abstract
The Airline Deregulation Act of 1978 significantly reshaped the U.S. aviation industry, transitioning from government-controlled pricing and service standards to market-driven dynamics. This shift led to the rise of low-cost carriers, reduced service quality, and increased customer complaints. In response to fluctuating service standards, the Air Travel Consumer Report (ATCR) was introduced in 1987 to provide transparency in airline service quality. This study leverages ATCR data from 2013 to 2022 to analyze trends in customer complaints across major U.S. airlines. The primary objectives are identifying the major customer complaints and determining which airlines received the most complaints during the studied period including the COVID-19 pandemic era. Results indicate that flight problems, refunds, baggage issues, customer service, and reservations/ticketing/boarding are the top complaints, with refunds peaking during the COVID-19 pandemic due to travel restrictions. Airlines such as Frontier, Spirit, and United consistently received higher complaints, while Alaska, Delta, and SkyWest had fewer complaints. The study underscores the importance of service quality in fostering customer satisfaction and loyalty, suggesting that airlines should focus on reducing complaints in key areas to enhance service quality. The findings provide valuable insights for both airlines and consumers, highlighting areas for improvement and aiding consumers in making informed choices. This study also emphasizes the need for comprehensive service quality measures beyond consumer complaints to assess airline performance accurately.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".