Integrating fuzzy Kano model with importance-performance analysis to identify the key determinants of customer retention for airline services

Abstract

The global economic recession as well as emerging low-cost carriers have led to declining revenues for worldwide airlines. A novel framework is presented to seek critical service attributes (SAs) that can enhance customer satisfaction and customer retention. Initially, fuzzy Kano model is employed to capture customer perceptions of SAs and convert them into quantitative degrees of customer satisfaction. Then, multiple regression and logistic regression are used to extract the weights of SAs and identify the key SAs for forecasting customer retention, respectively. Finally, the importance-performance analysis is conducted to offer managerial insights. Furthermore, support vector machine is used to justify the validity of customer retention. In summary, the main contributions are described as follows: (1) capturing passenger perceptions of airline services, (2) indicating which SAs should be improved first to enhance passenger satisfaction, and (3) using the identified key predictors to forecast customer retention.