Evaluating airline service quality using a Pythagorean fuzzy multi-criteria group decision-making framework

Growing competition in the airline industry has made service quality evaluation an increasingly important yet complex Multi-Criteria Group Decision-Making (MCGDM) problem. Service quality depends on several interrelated attributes, and passenger or expert assessments are often subjective, uncertain, and expressed linguistically. To address these challenges, this study develops an integrated Pythagorean fuzzy framework for selecting airlines according to their service quality. Experts first assess each airline against the evaluation criteria using linguistic ratings. The Pythagorean fuzzy Modified Preference Selection Index (MPSI) method then determines the relative importance of the experts. Their judgments are subsequently aggregated into a collective Pythagorean fuzzy decision matrix. Criteria weights are calculated using the Pythagorean fuzzy Weights by ENvelope and SLOpe (WENSLO) method, after which an improved COmpromise Ranking from Alternative Solutions (CORASO) approach ranks the airlines. The framework is demonstrated through a case study involving five Indian airlines and seven service-quality attributes. Sensitivity analysis indicates that the rankings remain stable across different values of the adjustment coefficient. Comparisons with established Pythagorean fuzzy decision-making methods further support the reliability and robustness of the results. The proposed ranking method therefore provides a robust decision support tool for evaluating airline service quality under uncertain and varied expert judgments.