Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction

Improving passengers' satisfaction is crucial for airline industry and requires in-depth understandings regarding the complex effects of various factors. This study investigates the importance, complex nonlinear effects and interaction effects of various factors (including passenger characteristics and service attributes) on airline travel satisfaction in data-driven manners leveraging machine-learning (ML) approaches. The results show that ML algorithms such as Random Forest have superiority in modeling airline travel satisfaction as compared to conventional logistic regressions. The quantitative importance of various factors is estimated and compared to reveal key determinants of passengers' satisfaction using permutation-based importance and accumulated local effect analysis. More importantly, results suggest that the main effects of service attributes present piecewise nonlinear patterns. There are piecewise interaction effects between passenger characteristics and service attributes and among service attributes on airline travel satisfaction. Practical implications on efficient and cost-effective measures of promoting satisfaction are derived and discussed based on the findings.

Automated summary

This study examines the impact of various factors on airline travel satisfaction using machine learning methods, identifying the complexity of importance and interaction effects, as well as the nonlinear patterns of service attributes. The results reveal key determinants of passenger satisfaction and suggest efficient measures for promoting satisfaction.