Passenger satisfaction assessment in the aviation industry using Type-2 fuzzy TOPSIS

Assessment of passenger satisfaction (PS) ratings is a noteworthy component of evaluating the service quality metrics used by airline companies. One of the most popular ways for airline companies to gauge customer satisfaction (CS) and determine what needs to be improved is by conducting surveys of their customers. In this study, we used an extended version of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique, which is one of the most important multi-criteria decision-making (MCDM) methods, with type-2 fuzzy sets to evaluate PS for the first time in the literature. Using this systematic technique, we have reflected to the model the uncertainty that may affect the evaluations of the passengers when making their assessments. Our study is considerably beneficial since it enables not only the PS evaluation of airline companies but also it is a generalization to analyze any type of CS that may be found in the aviation sector. We used our technique on questionnaires answered by 129,880 US Airlines passengers concerning 14 criteria and compared our results with studies in the literature using the same dataset. Unlike the literature, in this paper, passenger segmentation has been done to obtain effective results. Different scenarios are created for each emerging segment. While creating the scenarios, the passenger profiles of overall satisfaction, flight class, and customer loyalty are considered and different priorities are given to these variables in each scenario. We have utilized these scenarios to help airlines determine the demands of each consumer segment to improve service quality. Our study provides airline companies with an integrated decision system, with a holistic perspective, in which they can take into account not only their customers as one type, but also the differences they may experience in evaluating both their flight habits and flight experiences. •Factors affecting satisfaction for different passenger segments have been prioritized.•A flexible decision support system has been proposed for the aviation indusrty.•A large dataset has been used compared to MCDM methods in the literature.