DEUDORES

An airline company should provide services targeting customer satisfaction, accurately identifying customer/passenger expectations and preferences in order to gain a competitive advantage over the other airline companies. In this study, expectations for the quality of service and the performance of the operators were evaluated. The results of this study can help airlines to understand their relative positions with respect to competitors leading them to improved and more effective strategies for fulfilling the needs of customers. The customer- driven approach to service quality used in this study enables airlines to determine their position, including their strengths and weaknesses in service quality relative to their competitors. In addition, airlines are compared using their performance based on each criterion. The results can enable airlines to manage their competitive advantages and provide incentives to develop quality levels of specific services as compared to their competitors.

The most important criteria for passengers (seeAppendix A) are as follows: (1) Individual attention to passenger, (2) The quality of the reservation services, (3) Flight problems (cancellations, delays and deviations from schedules), (4) The advertising and image of the airline company, and (5) Safety (security). The less important criteria for passengers are as follows: (1) Responsiveness of crew, (2) Modern and proper aircraft, (3) Food service and

34

Another finding in this study is that the manager of an airline company may be interested in the top five service criteriaA1airlinehas to improve as soon as possible. These criteria are as follows:

(1) Flight problems (cancellations, delays and deviations from schedules) (2) Food service and drink services, and their quality (3) The advertising and image of the airline company (4) Crew’s speed handling request, and (5) Comfort and cleanness of seat, enough space between seats.

The top five service criteriaA2airlinehas to improve as soon as possible. These criteria are as follows:

(1) Customer complaint handling (delayed flights etc.) (2) Clear and precise cabin announcements (3) In-flight entertainment services and programs (4) Availability of enough flight staffs and crew, and (5) Modern and proper aircraft.

The top five service criteriaA3airlinehas to improve as soon as possible. These criteria are as follows:

(1) Helpful attitudes and courtesy of check in personnel and boarding employee (2) Courtesy, prompt, ability to language, and appearance of crew (3) The approach of staff at the ticket cancellations (4) Individual attention to passenger, and (5) Safety (security).

The main finding of this analysis is that passengers care for service prioritization and personalization for a better flight experience. Thus, companies should focus on strengths and weaknesses in their service quality and try to put their strengths forward to have the upper hand in the competition. Generally, just responsiveness or supplying modern aircrafts can be expected to achieve passenger satisfaction, but this study indicates that handling customer complaints, flight problems and individual attention could provide better insights for improving the service quality.

By using the proposed approach, the main findings are given as follows:

 Sometimes, survey respondents could not exactly say that “Responsiveness” is three times more important than “Tangibles”. Despite of using triangular or trapezoidal fuzzy numbers, interval valued hesitant fuzzy numbers enables us to model uncertainty by primary and secondary memberships as an indicator of optimistic and pessimistic point of view when evaluating criteria and alternatives. This provides better understanding of respondents’ doubts of making the pairwise comparisons of criteria while conducting the survey.

 When compared with Type 2 fuzzy sets based approaches, interval type 2 hesitant fuzzy decision making process can be managed by the simplification of computing process.

 As seen from the results of proposed methodology, decision maker could realize the alternatives overall scores’ variations according to each criteria and sub criteria (See Table 15 and Appendix A). This provides the better understanding of the service quality problems’ reasons and encourage airline companies to make strategic decisions for improving these criteria. Analyzers can also make comparisons of the difference between the most successful airline company and their company as well.

The practical implications are listed in the following:

 In this study, passenger expectations for the quality of service and the performance of the operators were evaluated by survey without information loss by using interval valued type 2 hesitant fuzzy decision making approach.

 The most important criteria for passengers (see Appendix A) are extracted and additionally, most powerful and weakest sides of service quality criteria according to each airline company are gathered before making strategic decisions for improving domestic airlines’ competiveness.

 Additionally, one at a time sensitivity analysis is conducted for representing the criteria sensitivity and airlines are compared according to their performance of each criterion. This provides the ability to decide the best airline company for each criterion and variations can be interpreted for further service quality improvement suggestions. From the comparison process, one could conclude that the application range of the linguistic terms used in the decision making method as proposed in Hu et al. (2015)’s paper is wider than that of most existing methods, such as seen inLee and Chen (2013)andRodriguez et al. (2013)’s studies. Additionally, the method used in this paper has an obvious advantage over that proposed byRodriguez et al. (2013), due to its more accurate result as seen from the calculations of non-dominance choice degrees whereas that proposed by Rodriguez et al. (2013) leads to information loss. Furthermore, the proposed method translates the linguistic fuzzy terms into IT2HFS, which models uncertainty more accurately than type-1 fuzzy values indicated in Hu et al. (2015)’s study as an advantage. First and foremost, this paper also

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Appendix A

The importance ranking of all criteria.

All Airline Weight A1Airline Weight A2Airline Weight A3Airline Weight

C18 0.09770 C3 0.72155 C21 0.60687 C12 0.35431 C25 0.09749 C9 0.65697 C26 0.40550 C13 0.33896 C21 0.08574 C24 0.65411 C2 0.38627 C4 0.32774 C20 0.08092 C14 0.59942 C1 0.37293 C20 0.31542 C16 0.07829 C7 0.59761 C14 0.36762 C6 0.31163 C23 0.07101 C16 0.59213 C7 0.36651 C22 0.27833 C26 0.06281 C18 0.59213 C16 0.36364 C23 0.27833 C15 0.04976 C13 0.59145 C18 0.36364 C5 0.25723 C22 0.03864 C12 0.57772 C10 0.34067 C11 0.24361 C1 0.03482 C25 0.57678 C17 0.32165 C15 0.24361 C14 0.03351 C5 0.56647 C24 0.29703 C8 0.23315 C7 0.03228 C17 0.56125 C9 0.29611 C10 0.21875 C6 0.02926 C26 0.55318 C11 0.28754 C2 0.19166 C9 0.02869 C4 0.55213 C15 0.28754 C1 0.18504 C13 0.02613 C19 0.53902 C19 0.28645 C19 0.17453 C11 0.02602 C6 0.52499 C25 0.28206 C25 0.14116 C24 0.02319 C8 0.51729 C20 0.25216 C17 0.11709 C4 0.02141 C22 0.48466 C8 0.24956 C3 0.05389 C17 0.01766 C23 0.48466 C22 0.23701 C24 0.04886 C3 0.01763 C11 0.46885 C23 0.23701 C9 0.04692 C19 0.01678 C15 0.46885 C3 0.22456 C21 0.04533 C12 0.01127 C1 0.44203 C5 0.17630 C16 0.04423 C10 0.00803 C10 0.44058 C6 0.16339 C18 0.04423 C2 0.00663 C20 0.43242 C4 0.12013 C26 0.04132 C5 0.00405 C2 0.42207 C13 0.06958 C7 0.03587 C8 0.00026 C21 0.34780 C12 0.06797 C14 0.03297