The Impact of the COVID‐19 Pandemic on the Passenger Satisfaction and Service Quality of the Airport Passenger Terminal
Bibliographic record
Abstract
The COVID‐19 pandemic has profoundly disrupted global aviation, raising new challenges for passenger satisfaction and service quality in airport terminals. While the SERVQUAL model has long been used to measure expectation–perception gaps, it has been critiqued for treating all service attributes as if they contribute symmetrically to satisfaction. Also, most studies on airport service quality and passenger satisfaction were conducted prepandemic, leaving a gap in understanding COVID‐19’s impact. This study addresses how the pandemic reshaped passenger expectations and satisfaction, providing a comprehensive analysis of airport service quality during COVID‐19 and the unique challenges it introduced. In this regard, the performance of an airport passenger terminal service level and the factors affecting user satisfaction during the COVID‐19 pandemic were evaluated using SERVQUAL and Kano analyses at Imam Khomeini International Airport (IKIA). To validate the model and the estimated parameters, Kolmogorov–Smirnov, Wilcoxon, Friedman ranking, and Spearman correlation coefficient tests were applied. The SERVQUAL findings showed a notable quality gap between the expected and received passenger services, with responsiveness and reliability factors exerting the greatest influence on satisfaction. The Kano analysis further highlighted that while some service features were mandatory, others acted as attractive and functional factors that could significantly enhance the passenger experience. The Kolmogorov–Smirnov test showed that the research data do not follow a normal distribution; thus, nonparametric tests were applied. Wilcoxon’s nonparametric test confirmed that the gap between respondents’ expectations and perceptions across all dimensions was not influenced by other factors. In addition, the Friedman test revealed that the average perception scores were high, showing a significant difference in the rankings, with the highest influence of assurance and tangibility variables. By combining these approaches, this study provides a postpandemic dual‐method framework that quantifies service quality gaps and prioritizes attributes by their impact on satisfaction and dissatisfaction. The findings guide airports in identifying improvement priorities, adapting to evolving passenger needs, and building resilience for future health crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".