رویکرد مجموعه راف برتری محور با سازگاری متغیر برای تدوین و فرموله کردن استراتژی های خدمات هواپیمایی

This study differs from previous ones applying multivariate statistical analysis and multiple criteria decision-making (MCDM) methods. We use the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) to formulate airline service strategies by generating airline service decision rules that model passenger preferences for airline service quality. Flow graphs are applied to infer decision rules and variables. This combined method considers decision-maker inconsistency. The use of flow graphs to visualize rules makes them more reasonable and understandable than traditional methods. To validate the effectiveness of our model, a large sample is surveyed. Managerial improvements needed for carriers to achieve the aspired-to level of customer satisfaction are also discussed.

Service quality can significantly affect customer satisfaction, loyalty, and retention. Higher service quality leads to higher passenger satisfaction, better branding, and higher passenger demand, which in turn leads to higher revenue [1]. Previous studies in airline service quality have either used multivariate statistical analysis [11], [12], [13] and [14] or multiple criteria decision-making (MCDM) methods [6], [7], [8] and [9]. In such surveys, natural language or linguistic variables are used to describe customer purchase patterns. Unfortunately, this can create an environment of imprecision, uncertainty, and partiality with regard to knowledge. These linguistic variables are then transformed into quantitative values, after which factor, cluster, and discriminant analyses are conducted. However, the semantic imprecision of natural languages leads to problems of computation, especially when the information described in a natural language is beyond the reach of existing bivalent logic and probability theory techniques [30]. The major problem with most multivariate statistical analysis or MCDM methods is that they rely on predetermined or fitting model measurements and it is not easy to derive managing implications directly from the results. Although data mining methods have been applied in related fields [2], [3], [4], [5] and [7], empirical analyses of airline service quality are relatively scarce in the existing literature. However, most machine learning techniques neglect the inconsistency of decision makes (e.g., if the service and food are “good,” then the overall satisfaction rating is “poor”). This inconsistency is common in some human decision-making processes, especially airline service quality [5]. Therefore, our goal is to use the data mining technique, called the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA), to analyze data from a survey on airline service quality. A set of “if antecedent, then consequent” decision rules are induced from the passenger preference data that express the relationships between attributes’ values and the overall service ratings in the minds of the passengers. Although the Classical Rough Set Approach (CRSA) is a powerful tool for handling many problems, it is not able to deal with dominance-relationships originating from the criteria, e.g., attributes with preference-ordered domains (scale) like product quality, market share and debt ratio [20]. Dominance-based rough set analysis (DRSA) can handle the dominance-relationship within the decision making process. However, a completely consistent dominance-relationship within a large real-life data set is rare. As a result, decision rules derived from the lower approximations are very weak, that is supported by only a few objectives [20]. A modification of DRSA called Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) is thus applied in this study. The VC-DRSA relaxes the conditions for assignment of objectives to lower approximations. With this technique, some inconsistent data can be assigned to the lower approximations but in a controlled way. The parameter controlling the inconsistency in VC-DRSA is named the consistency level [20]. The advantage of the VC-DRSA over the CRSA or DRSA is that it has access to an information table that displays comprehensive dominance relations. It is able to deal with the problem of inconsistency in large real-life data, by setting a consistency level to increase the objectives to lower approximations. There are several other advantages to using the VC-DRSA. First, the airline service decision rules are formulated in natural language and are easy to understand. Second, we may be able to eliminate some services associated with dispensable attributes without affecting the overall service rating. Third, we may be able to decrease some services to a minimum without lowering passenger perception of the airline service quality [5] and [7]. To visualize the cause-and-effect relationship derived from the VC-DRSA, the flow graph can be applied to illustrate the characteristics of airline service quality. In this study, we find VC-DRSA rules combined with flow graphs will provide greater insight for improving service quality. The rest of this paper is organized as follows: Section 2 summarizes some important research on the airline service quality. Section 3 introduces the basic concepts of VC-DRSA. Section 4 describes our empirical data. Section 5 presents our results and analysis. Section 6 concludes the paper.

In this study, the usefulness of the VC-DRSA approach as an operational tool for improving service quality in the airline market is illustrated. Our proposed prediction model takes the form of decision rules expressed using natural language, which makes them easier to understand than is the case with implications from traditional methods. Without any assumptions about the survey data, the derived rules are supported by real examples, describing only the most relevant attributes/criteria. The classical rough set theory handles attributes without preferences, a technique that does not always accurately represent the real world. The VC-DRSA is constructed by extending the classical rough set theory to include qualitative reasoning for preference-based passenger perceptions of service quality. It also considers the decision-makers’ inconsistency. This is done by replacing the indiscernibility relation with the dominance relation and setting a consistency level. In this way we can represent the conflicting preference relations that affect passengers’ perceptions regarding the service quality more objectively, without introducing the equivalence class concept of classical rough set theory. The first goal of decision analysis is to explain past decisions. The second goal is to give recommendations for future decisions. VC-DRSA achieves both these goals. Our results show that by improving both information and convenience, airlines could avoid a poor service rating, while good information, baggage handling and check-in processes would guarantee at least a good rating. Onboard comfort, employee service, being on-time and schedule are not important attributes for obtaining customer satisfaction in Taiwan's domestic market. Airline managers could thus exert minimum efforts on those service items. To safeguard confidentiality, our discussion has been limited to four domestic airlines, but an individual airline could use the same methodology to derive its own strategies for service improvement. Taking an approach differing from the traditional statistical analysis, we provide an alternative way to help airline management to decrease or eliminate those services that do not affect an airline's overall service rating.