Accounting for heteroskedasticity in understanding the long-term impact of the public health emergency on passengers’ satisfaction in a mid-sized airport

While extensive research has examined the impact of COVID-19 on public transportation modes, changes in airport service quality perception during public health emergencies remain understudied. This study reveals the deeper mechanisms of change in airport passenger satisfaction by analyzing mixed cross-sectional data from 3398 passengers during the pre- and post-pandemic stable periods at a representative medium-sized airport in Italy. Employing a multi-method framework centered on Heteroskedastic Ordered Logistic Modeling (HOLM), we analyze both the location (mean) and scale (variance) effects of the pandemic. The empirical results reveal that after controlling for heteroskedasticity, post-pandemic is positively associated with satisfaction level. Meanwhile, scale effects showed a significant decline in response variation after the pandemic, suggesting more consistent evaluation criteria. However, this macro-level convergence coexisted with a micro-level polarization in assessments of specific health-related services. Further mediation analysis confirms that this satisfaction enhancement was overwhelmingly channeled through service improvements, with enhanced security control emerging as the dominant pathway (accounting for 62 % of the indirect effect). This study challenges the assumption that crises inherently erode consumer judgment and provides a new framework for post-crisis service management, urging a strategic shift from managing average satisfaction to managing evaluative divergence.