O.R. Applications A survey analysis of service quality for domestic airlines Yu-Hern Chang a , Chung-Hsing Yeh b, * a Department of Transportation Management, National Cheng Kung University, Tainan, Taiwan, ROC b School of Business Systems, Monash University, Clayton, Victoria 3800, Australia Received 14 June 1999; accepted 27 March 2001 Abstract This paper presents an effective approach for evaluating service quality of domestic passenger airlines by customer surveys. To reflect the inherent subjectiveness and imprecision of the customers’ perceptions to the quality levels provided by airlines with respect to multiple service attributes, crisp survey results are represented and processed as fuzzy sets. A fuzzy multicriteria analysis (MA) model is used to formulate the evaluation problem. The model is solved by an effective algorithm which incorporates the decision maker’s attitude or preference for customers’ assessments on criteria weights and performance ratings. An empirical study of domestic airlines on a highly competitive route in Taiwan is conducted to demonstrate the effectiveness of the approach. The evaluation outcome provides airlines with their internal and external competitive advantages, relative to competitors in terms of customer-perceived quality levels of service. Ó 2002 Elsevier Science B.V. All rights reserved. Keywords: Survey; Fuzzy sets; Airlines; Service quality; Multicriteria analysis 1. Introduction The rapid growth in passenger traffic has been experienced in the deregulated domestic commer- cial airline market worldwide. Competition is ever increasing as airlines try to acquire and retain customers. Price is initially used as the primary competitive weapon. However, airlines soon rea- lise that competition on price alone represents a no-win situation in the long term. This is mainly due to the fact that airlines are relatively efficient in responding to competitors’ price changes (Jones and Sasser, 1995). In addition, the regulators of the airline system may interfere in the price com- petition as it often results in declined service quality and may affect flight safety. This implies that airlines’ competitive advantages based on price alone are not sustainable. In a highly com- petitive environment, where all airlines have comparable fares and matching frequent flyer programs (such as Taiwan’s domestic airline market), airline’s competitive advantages lie in the service quality perceived by customers. The study by Abrahams (1983) provides empirical support European Journal of Operational Research 139 (2002) 166–177 www.elsevier.com/locate/dsw * Corresponding author. Tel.: +61-3-9905-5808; fax: +61-3- 9905-5159. E-mail address: [email protected](C.-H. Yeh). 0377-2217/02/$ - see front matter Ó 2002 Elsevier Science B.V. All rights reserved. PII:S0377-2217(01)00148-5
Transcript
O.R. Applications
A survey analysis of service quality for domestic airlines
Yu-Hern Chang a, Chung-Hsing Yeh b,*
a Department of Transportation Management, National Cheng Kung University, Tainan, Taiwan, ROCb School of Business Systems, Monash University, Clayton, Victoria 3800, Australia
Received 14 June 1999; accepted 27 March 2001
Abstract
This paper presents an effective approach for evaluating service quality of domestic passenger airlines by customer
surveys. To reflect the inherent subjectiveness and imprecision of the customers’ perceptions to the quality levels
provided by airlines with respect to multiple service attributes, crisp survey results are represented and processed as
fuzzy sets. A fuzzy multicriteria analysis (MA) model is used to formulate the evaluation problem. The model is solved
by an effective algorithm which incorporates the decision maker’s attitude or preference for customers’ assessments on
criteria weights and performance ratings. An empirical study of domestic airlines on a highly competitive route in
Taiwan is conducted to demonstrate the effectiveness of the approach. The evaluation outcome provides airlines with
their internal and external competitive advantages, relative to competitors in terms of customer-perceived quality levels
of service. � 2002 Elsevier Science B.V. All rights reserved.
Keywords: Survey; Fuzzy sets; Airlines; Service quality; Multicriteria analysis
1. Introduction
The rapid growth in passenger traffic has beenexperienced in the deregulated domestic commer-cial airline market worldwide. Competition is everincreasing as airlines try to acquire and retaincustomers. Price is initially used as the primarycompetitive weapon. However, airlines soon rea-lise that competition on price alone represents a
no-win situation in the long term. This is mainlydue to the fact that airlines are relatively efficientin responding to competitors’ price changes (Jonesand Sasser, 1995). In addition, the regulators ofthe airline system may interfere in the price com-petition as it often results in declined servicequality and may affect flight safety. This impliesthat airlines’ competitive advantages based onprice alone are not sustainable. In a highly com-petitive environment, where all airlines havecomparable fares and matching frequent flyerprograms (such as Taiwan’s domestic airlinemarket), airline’s competitive advantages lie in theservice quality perceived by customers. The studyby Abrahams (1983) provides empirical support
European Journal of Operational Research 139 (2002) 166–177www.elsevier.com/locate/dsw
0377-2217/02/$ - see front matter � 2002 Elsevier Science B.V. All rights reserved.
PII: S0377-2217 (01 )00148-5
for the theory of service quality competition in theairline industry.
Empirical studies of demand for airline servicesshow that service quality is central to the choice ofairlines for both business and leisure travellers(Bureau of Transport and Communications Eco-nomics, BTCE, 1994). An empirical study by Os-trowski et al. (1993) shows that continuing toprovide perceived high quality services would helpairlines acquire and retain customer loyalty. Anairline would lead the market if it offers superiorquality services relative to its competitors. It istherefore of strategic importance for airlines tounderstand their relative competitive advantageson service quality.
This paper addresses the performance evalua-tion problem of service quality for domestic pas-senger airlines based on customer surveys.Empirical research has demonstrated the impor-tance of the customer interactions in the assessmentof overall quality with services (Bitner et al., 1990).However, due to the intangible nature of airlineservices, airline customers may find it difficult toprecisely assess their perceptions of service qualitybased on their experiences relative to expectations.To better reflect the inherent subjectiveness andimprecision of customers’ assessments, the conceptof fuzzy sets (Zadeh, 1965) is used for representingthe survey results. An effective fuzzy multicriteriaanalysis (MA) approach is thus developed to rankairlines based on customers’ assessments with re-spect to multiple service quality attributes. Thisapproach allows the decision-maker’s (DM’s) at-titude or preference for the customers’ assessmentson criteria weights and performance ratings to beincorporated into the evaluation process. Theevaluation outcome would help airlines better un-derstand how the customers view their servicesrelative to their competitors, thus motivating air-lines to provide appropriate levels of services.
In subsequent sections, we first discuss thequality measures of airline services and present thecriteria suitable for evaluating Taiwan’s domesticairlines. Next we explain how the passenger’s pointestimates of quality level are represented by fuzzysets. As a result, we formulate the evaluationproblem as a fuzzy MA model and present an ef-fective algorithm for solving the problem. Finally,
an empirical study on a domestic route in Taiwanis conducted. A competitiveness analysis is carriedout to explore the relative competitive strengthsand weaknesses of the airlines studied.
2. Measuring quality of airline services
In the passenger airline industry, only the cus-tomer can truly define service quality (Butler andKeller, 1992). The quality of airline service is dif-ficult to describe and measure due to its hetero-geneity, intangibility and inseparability. Nevertheless, quite a few conceptual and empiricalstudies have been devoted to investigate the servicequality issues in the passenger airline industry.Various schemes for defining service quality di-mensions or attributes have been proposed fromthe perspective of passengers. Most of theseschemes are presented as quality measures for ex-amining the relationships between service qualityand related issues such as airline choice (Ritchie etal., 1980; Etherington and Var, 1984; Wells andRichey, 1996), customer satisfaction (Alotaibi,1992), customer loyalty (Ostrowski et al., 1993;Young et al., 1994), passenger type (Alotaibi,1992; White, 1994), airline type (Jones and Cocke,1981), airline class (Etherington and Var, 1984;Alotaibi, 1992), aircraft type (Truitt and Haynes,1994), productivity (Ozment and Morash, 1998),changes in quality levels over time (BTCE, 1992),total transportation service offering (Morash andOzment, 1994), assessment group (Gourdin andKloppenborg, 1991) and attribute dependency(Elliot and Roach, 1993).
The results of existing studies on service qualitysuggest that the definitions and perceptions ofairline service quality are quite diverse, and do notseem to fit any single existing quality model (Hy-nes and Percy, 1994). This implies that servicequality attributes are context-dependent andshould be selected to reflect the service environ-ment investigated. While the definition of servicequality and its influential characteristics continueto be important research issues, the understandingof service quality levels being offered relative tocompetitors is of significant importance to airline
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strategic management. This is the service qualityissue to be addressed.
Since the 1980s, the mainstream research onservice quality has been conducted based on thenotion that quality of service is perceived andevaluated by customers (Gronroos, 1990). Servicequality, as perceived by customers, can be mea-sured by an evaluation analysis which results froma comparison between customers’ expectationsand experiences. The most widely used customer-perceived service quality model is perhaps the GapAnalysis and SERVQUAL model by Parasuramanet al. (1985, 1988). Despite its validation in con-cept, there is no quantitative yardstick available.In fact, this model may have inherent problems inactually measuring customer expectations of ser-vice quality. Gronroos (1993), thus, suggests thatmeasuring customer experiences of service quality,as providing a close approximation, is a theoreti-cally valid way of measuring perceived quality. Inpractice, this simplifies the process of data collec-tion and analysis via survey questionnaires. Infact, service experiences are perceptions of reality,in which prior expectations are inherent. Thisconcept is in line with the consumer behaviourresearch that views customer attitude as a globalevaluation of a product or service.
In the context of service quality, attitude can beregarded as an overall evaluation of a service per-ceived by customers based on their likes and dis-likes (Bolton and Drew, 1991; Engel et al., 1995).Customers’ attitude towards a service depends on:(a) the strength of their beliefs about various fea-tures or attributes associated with the service and(b) the weight of attributes. Customers’ beliefstypically involve perceived associations betweenthe service and its associated attributes, stemmingfrom their direct experiences with the service. Theweight of attributes refers to the relative impor-tance of each attribute as perceived by customers.The best known formulation of attitude models isprobably the Fishbein’s multiattribute model(Fishbein and Ajzen, 1975; Engel et al., 1995). Themodel states that a customer’s attitude towards agiven object (e.g. a service) is based on the summedset of beliefs about the service’s attribute weightedby the importance of these attributes. In this sense,service quality refers to the quantities of the char-
acteristics that are embodied in a service and di-rectly interact with the utility functions of thecustomer (BTCE, 1992). This concept coincideswith MA methods based on multiattribute utility(or value) theory (Hwang and Yoon, 1981; Dyer etal., 1992; Stewart, 1992) for ranking a finite set ofalternatives characterised by multiple, usuallyconflicting criteria (attributes). MA in this contexthas been applied to a wide range of decisionproblems which require a cardinal preference orranking of the alternatives (e.g. van Gennip et al.,1997; Raju and Pillai, 1999; Yeh et al., 1999b). Thisis the fundamental methodology on which the ap-proach presented in this paper is based.
3. Evaluation criteria of service quality for Taiwan’s
domestic airlines
The airline’s service quality perceived by cus-tomers is normally represented and measured by anumber of manageable, distinct dimensions or at-tributes. As suggested by existing research results,context-dependent service quality attributes are tobe identified for evaluating Taiwan’s domesticairlines. To this end, a comprehensive investiga-tion was conducted by consulting airline manag-ers, government officials, expert academics andtravel agents in Taiwan. As a result, 15 serviceattributes embodied by five categories (constructfactors) were selected, as given in Fig. 1. Theseattributes are independent of each other, thusconstituting the criteria (C1, C2; . . . ;C15) used inthe fuzzy MA model for evaluating service qualityperformance of Taiwan’s domestic airlines.
The evaluation criteria in Fig. 1 reflect themajor concerns of passengers travelling on short-haul routes between two cities in a Taiwanesecontext. They also represent the service attributesover which Taiwan’s domestic airlines have con-trol and with which they can differentiate them-selves from other competitors. They correspond tothe expressive performance of airline services,known as the functional quality. The functionalquality is concerned with the service delivery pro-cess, thus reflecting customers’ experiences of ser-vice quality. Research has shown that thefunctional quality plays the most critical role in
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customer’s overall quality perception, and suc-cessful service management means the continuousimprovement of the functional quality of services(Gronroos, 1984, 1993).
4. Representing customers’ assessments as fuzzy sets
The level of the functional quality perceived bycustomers is to be assessed in a subjective mannervia a survey process. The result of this subjectiveassessment is intrinsically imprecise due to thecharacteristics of airline services. This imprecisionis inevitable, especially when the time frameavailable for airline passengers to make estimatesof quality levels is typically short. To reflect thesubjectiveness and imprecision involved in thesurvey process, the assessments made by all pas-sengers with respect to criteria weights and per-formance ratings of each airline on each criterionare represented as fuzzy sets. Modelling usingfuzzy sets has proven to be an effective way for
formulating decision problems where the infor-mation available is subjective and imprecise(Zimmermann, 1996; Hellendoorn, 1997).
Customer-perceived service quality has beenuniversally measured on a point estimate basis(Rust et al., 1999). For example, in a survey pro-cess, each of N passengers of an airline is asked togive a rating xk ðk 2 f1; 2; . . . ; LgÞ on an L-pointLikert-type scale for an assessment item such asthe importance of a criterion or the performancerating of an airline with respect to an evaluationcriterion. The assessments of all N passengers ofthe airline with respect to the assessment item areaggregated and represented by a discrete fuzzy set,whose membership function is given as
lAðxkÞ ¼a1x1
þ a2x2
þ � � � þ akxk
þ � � � þ aLxL
;
k ¼ 1; 2; . . . ; L;ð1Þ
where the ‘‘–’’ sign is used to link the elementsxk ðk ¼ 1; 2; . . . ; LÞ of A with their corresponding
Fig. 1. Criteria used for service quality evaluation of Taiwan’s domestic airlines.
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degrees of membership ak ðk ¼ 1; 2; . . . ; LÞ in A,and the ‘‘+’’ sign indicates that the listed pairs ofelements and membership degrees collectivelyform the definition of the fuzzy set A (Klir andYuan, 1995). The degree of membership of theelements ak in A is defined as
ak ¼Nk
N;XLk¼1
Nk ¼ N ; 06 ak 6 1;
k ¼ 1; 2; . . . ; L; ð2Þ
where Nk is the number of passengers who give anxk rating for the assessment item. As an overallassessment result for an airline’s performance on aservice attribute (evaluation criterion), ak repre-sents the possibility of the airline having an xkrating on the service attribute. This implies thatthe values for the assessments obtained for eachassessment item are regarded as possibilities whichare measured using fuzzy sets.
With the representation of all passengers’ as-sessments on as assessment item as a fuzzy set,there is no need for a consensus test such asKendall’s coefficient of concordance among thepassengers, as often required by the mean method.This is because all the passengers’ perceived ratingsare incorporated into the fuzzy set to represent theassessment result of the passengers as a whole.
5. The fuzzy multicriteria analysis approach
In this paper, we formulate the performanceevaluation of service quality for Taiwan’s domesticairlines as a fuzzy MA problem with customer-perceived performance ratings and criteria weightsrepresented as fuzzy sets.
5.1. The service quality evaluation problem
The problem usually involves a set of n alter-natives (airlines) Ai ði ¼ 1; 2; . . . ; nÞ: The servicequality levels provided by these alternatives are tobe evaluated by their customers in terms of a set ofm criteria Cj ðj ¼ 1; 2; . . . ;mÞ, which are indepen-dent of each other. A fuzzy matrix (referred to as
the decision matrix) for m criteria and n alterna-tives is to be given as
where xij represent the overall assessments of theservice quality level of alternative Ai ði ¼1; 2; . . . ; nÞ with respect to criterion Cj ðj ¼1; 2; . . . ;mÞ. Expressed as in (1), xij are fuzzy setscharacterised by the point estimates of all cus-tomers of alternative Ai.
A fuzzy weighting vector representing the rel-ative importance of the criteria perceived by cus-tomers is to be given as
W ¼ ðw1;w2; . . . ;wmÞ: ð4Þ
Expressed as in (1), wj (j ¼ 1; 2; . . . ;m) are fuzzysets characterised by the point estimates of allcustomers involved.
5.2. The solution procedure
With the problem structure defined above,mainstream fuzzy MA models in the context ofmultiattribute utility theory are developed basedon a two-phase approach (Zimmermann, 1987;Chen and Hwang, 1992). First, the fuzzy assess-ments with respect to all criteria for each alterna-tive are aggregated. Second, alternatives areranked based on the comparison of their aggre-gated overall assessments represented as fuzzy sets.The main problem with this approach lies in thefact that the comparison of fuzzy sets is not alwaysstraightforward and reliable (Zimmermann, 1987;Chen and Hwang, 1992; Chen and Klein, 1997).
To overcome the problem of comparing fuzzysets, we present an effective algorithm for gener-ating a crisp performance index for each alterna-tive. The algorithm is based on the concepts of thedegree of optimality and the ideal solution (Hwangand Yoon, 1981; Zeleny, 1982). These two con-cepts have been widely used in different decisioncontexts due to their simplicity and applicability insolving various MA problems (e.g. Chen and
170 Y.-H. Chang, C.-H. Yeh / European Journal of Operational Research 139 (2002) 166–177
Hwang, 1992; Zeleny, 1998; Liang, 1999; Yeh etal., 1999a). With a process of transforming a fuzzyvector into a fuzzy singleton (Zadeh, 1973) vector,the algorithm can incorporate the DM’s attitudeor preference for the assessments into the evalua-tion procedure. This transformation process ispresented below.
Given a fuzzy vector V ¼ ðv1; v2; . . . ; vmÞ, suchas ðx1j; x2j; . . . ; xnjÞ of the decision matrix for cri-terion Cj or the weighting vector W, the degree towhich vj is the best result in V is calculated bycomparing it with the fuzzy maximum ðMj
maxÞ(Yager, 1980; Zadeh, 1998), given as
uRj ¼XLk¼1
minðlvjðxkÞ; lMjmax
ðxkÞÞmaxðlvjðxkÞ;lMj
maxðxkÞÞ
!( ),L;
j ¼ 1; 2; . . . ;m: ð5Þ
lvjðxkÞ is defined in (1) and the membership func-tion of Mj
max is defined as
lMjmax
ðxkÞ ¼xk xjmin
xjmax xjmin
; k ¼ 1; 2 . . . ; L; ð6Þ
where
xjmax ¼ sup[ni¼1
fxk; xk 2 R and 06 lvjðxkÞ6 1g;
xjmin ¼ inf[ni¼1
fxk; xk 2 R and 06 lvjðxkÞ6 1g:
uRj represents the overall similarity degree betweenvj and the fuzzy maximum (Mj
max). The similaritymeasure used in (5) denotes the average of thesimilarity degrees on all elements in V. It has thesignificance of average (i.e. each element in V playsan equal role) as compared with other similaritymeasures (Wang, 1997).
The similarity concept used in (5) coincideswith possibility theory on fuzzy sets (Klir andYuan, 1995). The similarity degree between V andan ideal solution V þ (for which the possibilitydegree is 1) is expressed by a suitable distancebetween V and V þ (the possibility of V ) defined interms of relevant attributes of the elements in V.Thus, uRj in (5) reflects the highest degree of pos-sibility of vj’s performance to the ideal solution
(represented by the fuzzy maximum), thus reflect-ing the DM’s optimistic view.
In line with this concept, the DM’s pessimisticview can be represented by the degree to which vjis not the worst result. This can be calculated bycomparing it with the fuzzy minimum ðMj
minÞ, gi-ven as
uLj ¼ 1XLk¼1
minðlvjðxkÞ; lMjminðxkÞÞ
maxðlvjðxkÞ; lMjminðxkÞÞ
!( ),L;
j ¼ 1; 2; . . . ;m; ð7Þ
where
lMjminðxkÞ ¼
xjmax xkxjmax xjmin
; k ¼ 1; 2; . . . ; L; ð8Þ
xjmax ¼ sup[ni¼1
fx; x 2 R and 06lvjðxÞ6 1g;
xjmin ¼ inf[ni¼1
fx; x 2 R and 06 lvjðxÞ6 1g:
In actual decision settings, the DM’s attitude isnot necessarily to be absolutely optimistic or pes-simistic, but somewhere in between. An attitudeindex k in the range of 0 and 1 is thus used toindicate the DM’s relative preference between uRj
and uLj . Incorporated with the attitude index k, afuzzy singleton vector S ¼ ðs1; s2; . . . ; smÞ is deter-mined by
sj ¼ kuRj þ ð1 kÞuLj ; j ¼ 1; 2; . . . ;m; ð9Þ
where sj indicates the degree of optimality of vj orits degree of preferability over all others in V.
In practical applications, k ¼ 1, k ¼ 0:5 ork ¼ 0 can be used to indicate that the DM has anoptimistic, moderate or pessimistic view, respec-tively, on assessment results represented as fuzzysets. An optimistic DM is apt to prefer highervalues of the fuzzy sets, while a pessimistic DMtends to favour lower values. In the context of thecase study presented in this paper, an optimisticDM would pay more attention on favourable as-sessments (high ratings), while a pessimistic DM ismore concerned about unfavourable assessments(low ratings).
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Incorporated with the transformation processdescribed above, the algorithm for ranking al-ternatives with the weighting vector and the de-cision matrix given as fuzzy sets is presented asfollows:Step 1. Obtain the fuzzy singleton vector
Y ¼ ðy1; y2; . . . ; ymÞ for the criteria weights from theweighting vectorW by (5)–(9) with a given attitudeindex kw.Step 2. Obtain the weighted decision matrix by
multiplying Y obtained at Step 1 by X given in (3)using fuzzy arithmetic (Kaufmann and Gupta,1991).Step 3. Set the attitude index kr for performance
ratings of the decision matrix and determine thedegree of optimality of each alternative regardingeach criterion based on the weighted decisionmatrix obtained at Step 2 by (5)–(9), resulting in afuzzy singleton performance matrix, given as
where zij ði ¼ 1; 2; . . . ; n; j ¼ 1; 2; . . . ;m) indicatesthe degree of preferability of alternative Ai over allother alternatives in regard to criterion Cj.Step 4. Determine the positive ideal solution
ðzþÞ and the negative ideal solution ðzÞ (Hwangand Yoon, 1981; Zeleny, 1982) by
Step 5. Calculate the Hamming distance be-tween each alternative and the positive ideal so-lution ðzþÞ and the negative ideal solution ðzÞ,respectively, by
dþi ¼
Xmj¼1
ðzþj zijÞ;
di ¼
Xmj¼1
ðzij zj Þ; i ¼ 1; 2; . . . ; n:
ð13Þ
Step 6. Compute the overall crisp performanceindex for each alternative by
Pi ¼di
dþi þ d
i; i ¼ 1; 2; . . . ; n: ð14Þ
The larger the performance index, the morepreferred the alternative. This is based on theconcept that the most preferred alternative shouldnot only have the shortest distance from the pos-itive ideal solution, but also have the longest dis-tance from the negative ideal solution.
6. Empirical study
Taiwan’s domestic passenger airline market hasbecome a major transport service sector with anannual average growth rate of nearly 20% sincederegulation in 1987. To examine passengers’perceptions of service quality for Taiwan’s do-mestic airlines, the Taipei–Tainan route was cho-sen. Being a major route with more than 60scheduled flights per day, the Taipei–Tainan routeis served by four airlines, namely, Far Eastern Airtransport ðA1Þ, TransAsia Airways ðA2Þ, Eva AirðA3Þ and Great China Airlines ðA4Þ.
A survey questionnaire was designed to mea-sure the existing quality levels of services perceivedby passengers of four airlines. The passengers ofeach airline were asked to rate the importance ofthe evaluation criteria (service attributes) in Fig. 1and assess the performance of the airline on eachcriterion on an 11-point scale ranging from 10(extremely high) to 0 (extremely low). This scoringmethod is familiar to the general public in Taiwan,thus, better reflecting their perceptions on an as-sessment item in terms of scores. The question-naire form is given in Appendix A.
The survey process was conducted at TainanAirport in the morning and afternoon over a pe-riod of one month. The survey questionnaire wasrandomly given face-to-face to both arriving anddeparting passengers who have flown the Tainan–Taipei route with the same airline at least twice inthe last six months. Frequent travelers were pre-ferred as first-time travelers may find it difficult toevaluate and discern differences in service quality,especially before the service (Turley, 1990). A total
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of 390 respondents were selected and 354 effectiveresponses (93 for A1, 80 for A2, 109 for A3 and 72for A4Þ were received. Most respondents were ableto complete the questionnaire within 10 minutes.
Because all surveys conducted were essentiallyidentical at around the same time, the survey re-sults can be aggregated and used as the existingpassenger’s overall perceptions of criteria weightsand performance ratings on service quality pro-vided by four airlines. In what follows, we brieflyillustrate how the fuzzy MA approach presented inthe previous section is used to rank four airlinesbased on the respondents’ assessments.
We first consider the situation where the DMhas a moderate attitude toward the respondents’assessments, that is, kw ¼ 0:5 and kr ¼ 0:5. Thisindicates the DM weights all the responses equally.Given all the respondents’ valid assessments oncriteria weights and kw ¼ 0:5, a fuzzy singletonvector Y, as shown in Table 1, is obtained by (1)and (2) and (5)–(9). Given the assessments of allthe respondents of individual airlines, the decisionmatrix X, expressed as in (3) with n ¼ 4 and
m ¼ 15, can be determined. A weighted decisionmatrix is accordingly generated by multiplying Yby X. Given the weighted decision matrix andkr ¼ 0:5, a fuzzy singleton performance matrix Z,as shown in Table 2, is obtained by (5)–(9).
With the performance matrix in Table 1, theoverall performance index Pi of four airlinesAi ði ¼ 1; 2; 3; 4Þ can be obtained by (11)–(14). Thevalues of Ri ði ¼ 1; 2; 3; 4Þ are 0.6782, 0.4233,0.7832, and 0.2227, respectively, indicating theranking order of their service quality performanceis A3 > A1 > A2 > A4.
To examine how the DM’s attitude or prefer-ence for the customers’ assessments may affect theevaluation outcome, further experiments werecarried out by changing the values of kw and kr.Some representative results are given in Table 3.
The evaluation outcome presented in Table 3reflects that the passengers of four airlines havedifferent views on the quality level of their services,consistent with the survey results. The overallrankings of four airlines are clearly affected by theDM’s preference for: (a) favourable responses
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(high ratings) when kw or kr is close to 1 or (b)unfavourable responses (low ratings) when kw or kr
is close to 0. This analysis would help airlinemanagement understand how their passengers’opinions are distributed relative to their competi-tors. For example, airline A1 ranks the highestwhen the unfavourable opinions are weightedmore, and ranks the lowest when the favourableopinions are weighted more. This implies that rel-atively fewer passengers of airline A1 have givenlow ratings or high ratings on their service perfor-mance as compared with other airlines. This meansthat the assessments made by airline A1’s passen-gers are relatively consistent. Airline A3 performsthe best consistently except for the situations whereunfavourable opinions are weighted more. Thisindicates that relatively more passengers of airlineA3 think their service performance is the best, whilesome passengers have different views.
The evaluation process and the correspondingoutcomes can help an airline identify its competi-tive advantages relative to its competitors in aspecific context. The airline can concentrate onimprovement of certain service attributes that areimportant in affecting relative rankings. To ex-amine the airlines’ relative competitive strengthsand weaknesses on service attributes identified as
important to their customers, a competitivenessanalysis can be carried out based on the weightedperformance evaluation result in Table 2. In theanalysis, we regard the best or worst five serviceattributes assessed by passengers of an airline asthe internal strengths or weaknesses of the airline.For a particular service attribute, the two airlineswith higher or lower performance rankings areregarded as having external strengths or weak-nesses on the attribute. By combining the com-petitive strengths and weaknesses both internallyand externally, the overall competitiveness of air-lines on individual service attributes can be ob-tained. Table 4 shows the results, which are basedon the situation where all the passengers’ assess-ments are weighted equally.
The internal competitiveness result in Table 4indicates that the reliability of service ðC8, C9 andC10Þ of airlines as a whole performs much betterthan the handling of abnormal conditions ðC13, C14
and C15Þ. The airlines with more external com-petitive strengths in these two categories (such asA3 and A1Þ have a higher overall ranking. Theoverall competitiveness of airlines indicated inTable 4 is consistent with their performancerankings. These evaluation results would helpairlines better manage their competitive advanta-
Table 4
Competitive strengths and weaknesses of four airlines
Internal External Overall
A1 A2 A3 A4 A1 A2 A3 A4 A1 A2 A3 A4
C1 X O O X X O O X O O
C2 X O O X O O X X O
C3 O X O O X X O X
C4 X X X O O X X
C5 X O O X O X X O
C6 X O X O O X X O
C7 O O X X
C8 O O O O O X O X O O
C9 O O O O O X X O O
C10 O O O O O X O X O O
C11 O O X O O X X O O X
C12 X O O X X X
C13 X X X X X O O X X X
C14 X X X O X O X X X
C15 X X X X O X O X X X
O: Competitive strength; X: Competitive weakness.
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ges and provide an incentive for them to improvequality levels of specific services relative to theircompetitors.
7. Conclusion
The intensive competition of the domestic air-line market under deregulation has made airlinesadopt an attitude towards customer-oriented ser-vice quality. To help airlines better understand howthe customer views their services relative to theircompetitors, a customer-driven evaluation ap-proach of service quality has been presented. Crispassessments of all the customers on quality levels ofservice provided by airlines are modeled as fuzzysets to better reflect the inherent subjectiveness andimprecision of the survey process. A fuzzy MAmodel with effective handling of fuzzy data hasbeen developed to evaluate the relative perfor-mance of airlines in terms of customers’ percep-tions of service quality. The DMs’ attitude orpreference for the customers’ assessments on cri-teria weights and performance ratings can bespecified to reflect their major concerns on variouscustomers’ opinions. An empirical study of a do-mestic route in Taiwan has been conducted todemonstrate the effectiveness of the approach. Theevaluation outcome helps airlines identify theirinternal and external competitive advantages rela-tive to their competitors. It provides a guideline forairlines to provide appropriate levels of service inresponse to customers’ needs. The underlyingconcepts used by the approach are comprehensible,and the survey process and computations requiredare straightforward and simple. The approach isparticularly applicable to major routes betweentwo cities which are served by several airlines.
Acknowledgements
This research was supported in part by theNational Science Council of Taiwan, ROC, underGrant No. NSC88-2811-E006-0013. We aregrateful to the Tainan Airport Administration andfour airlines involved for providing assistance in
problem formulation and data collection. We alsothank Prof. Roman Slowinski, the editor, andanonymous referees for their valuable commentsand advice.
Appendix A. Airline service quality survey
This questionnaire is purely an academic re-search survey, aiming at understanding the cur-rent quality level of airline services on theTaipei– Tainan route. The survey result will bestrictly used for academic purposes only, inwhich no individual responses can be identified.To ensure the fairness and effectiveness of theresponses, we expect that all participants haveflown with the same airline at least twice inthe past six months. It should only take 10minutes to complete.
Section 1The following questions relate to your travel
profile on the Taipei–Tainan route.1. Which airline do you flight with this time (or
last time)?Far Eastern Air Transport TransAsiaAir-ways EvaAir GreatChina Airlines
2. How many times did you fly with the same air-line in the last six months?1 2–5 6–10 11 or more
3. What is your main purpose for taking this trip?Business Commuting Pleasure Per-sonal Other
4. What is your main reason of choosing this air-line?Service quality Discount price TimingAt random Other
5. How many times did you fly in the last year?1 2–5 6–10 11 or more
Section 2Based on your experiences and expectations as
a passenger of domestic airlines, please rate howimportant the following service attributes are toyou when you choose an airline. The score 10represents that the attribute is extremely impor-tant, and the score 0 means that the attribute is notimportant at all. There are no correct answers. Thescore you circle or tick should truly reflect your
Y.-H. Chang, C.-H. Yeh / European Journal of Operational Research 139 (2002) 166–177 175
feelings about the relative importance of airlineservices that would affect your airline choice.
Section 3Based on your experiences and perceptions with
the service of the airline on your previous flight(s),please rate the quality level in terms of the fol-lowing service attributes. The score you circle ortick should truly reflect your feeling about theextent to which the airline service satisfies you. Thescore 10 represents that you are extremely satisfiedwith the service for the attribute, and the score 0means that you are totally dissatisfied with theservice for the attribute.
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