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Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
Gao, Kun Yang, Ying Qu, Xiaobo
Gao, Kun
Yang, Ying
Qu, Xiaobo
Author
Abstract
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.
Keywords
travel satisfaction, machine learning, nonlinear effect, interactions, data-driven approaches
Date
2021
Type
Journal article
Journal
Transportation Research Part D
Book
Volume
97
Issue
Page Range
1-23
Article Number
Article 102957
ACU Department
School of Behavioural and Health Sciences
Faculty of Health Sciences
Faculty of Health Sciences
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled