A Survey Analysis of Service Quality for Domestic Airlines

European Journal of Operational Research 139 (2002) 166–177 www.elsevier.com/locate/dsw

O.R. Applications

A survey analysis of service quality for domestic airlines Yu-Hern Chang a, Chung-Hsing Yeh a

b,*

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. surveys. To reflect reflect the inherent subjectivene subjectiveness ss and imprecision imprecision of the customers customers’’ perceptions 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 commercial airline market worldwide. worldwide. Competition Competition is ever incr increa easi sing ng as airl airlin ines es try try to acqu acquir iree and and reta retain in custom customers ers.. Price Price is initia initially lly used used as the primar primary y competitive weapon. However, airlines soon realise that competition on price alone represents a

*

Corresponding author. Tel.: +61-3-9905-5808; fax: +61-39905-5159. E-mail address: address: [email protected] (C.-H. Yeh). 0377-2217/02/$ - see front matter PII: S0377-2217(01)00148-5

Ó

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 competi petiti tion on as it ofte often n resu result ltss in decli decline ned d serv servic icee quality and may affect flight safety. This implies that that airline airlines’ s’ compet competiti itive ve advant advantage agess based based on price alone are not sustainable. In a highly competi petiti tive ve envi enviro ronme nment nt,, wher wheree all all airl airlin ines es have have comp compar arab able le fare faress and and ma matc tchi hing ng freq freque uent nt flyer flyer prog progra rams ms (suc (such h as Taiw Taiwan an’s ’s dome domest stic ic airl airlin inee market), airline’s competitive advantages lie in the service quality perceived by customers. The study by Abrahams Abrahams (1983) (1983) provides provides empirical empirical support support

2002 Elsevier Science B.V. All rights reserved.

Y.-H. Chang, C.-H. Yeh / European Journal of Operational Research 139 (2002) 166–177

for the theory of service quality competition in the airline industry. Empirical studies of demand for airline services show that service quality is central to the choice of airlines for both business and leisure travellers (Bureau of Transport and Communications Economics, BTCE, 1994). An empirical study by Ostrowski et al. (1993) shows that continuing to provide perceived high quality services would help airlines acquire and retain customer loyalty. An airline would lead the market if it offers superior quality services relative to its competitors. It is therefore of strategic importance for airlines to understand their relative competitive advantages on service quality. This paper addresses the performance evaluation problem of service quality for domestic passenger airlines based on customer surveys. Empirical research has demonstrated the importance of the customer interactions in the assessment of overall quality with services (Bitner et al., 1990). However, due to the intangible nature of airline services, airline customers may find it difficult to precisely assess their perceptions of service quality based on their experiences relative to expectations. To better reflect the inherent subjectiveness and imprecision of customers’ assessments, the concept of fuzzy sets (Zadeh, 1965) is used for representing the survey results. An effective fuzzy multicriteria analysis (MA) approach is thus developed to rank airlines based on customers’ assessments with respect to multiple service quality attributes. This approach allows the decision-maker’s (DM’s) attitude or preference for the customers’ assessments on criteria weights and performance ratings to be incorporated into the evaluation process. The evaluation outcome would help airlines better understand how the customers view their services relative to their competitors, thus motivating airlines to provide appropriate levels of services. In subsequent sections, we first discuss the quality measures of airline services and present the criteria suitable for evaluating Taiwan’s domestic airlines. Next we explain how the passenger’s point estimates of quality level are represented by fuzzy sets. As a result, we formulate the evaluation problem as a fuzzy MA model and present an effective algorithm for solving the problem. Finally,

167

an empirical study on a domestic route in Taiwan is conducted. A competitiveness analysis is carried out to explore the relative competitive strengths and weaknesses of the airlines studied.

2. Measuring quality of airline services

In the passenger airline industry, only the customer can truly define service quality (Butler and Keller, 1992). The quality of airline service is difficult to describe and measure due to its heterogeneity, intangibility and inseparability. Never theless, quite a few conceptual and empirical studies have been devoted to investigate the service quality issues in the passenger airline industry. Various schemes for defining service quality dimensions or attributes have been proposed from the perspective of passengers. Most of these schemes are presented as quality measures for examining the relationships between service quality and related issues such as airline choice (Ritchie et al., 1980; Etherington and Var, 1984; Wells and Richey, 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 and Ozment, 1994), assessment group (Gourdin and Kloppenborg, 1991) and attribute dependency (Elliot and Roach, 1993). The results of existing studies on service quality suggest that the definitions and perceptions of airline service quality are quite diverse, and do not seem to fit any single existing quality model (Hynes and Percy, 1994). This implies that service quality attributes are context-dependent and should be selected to reflect the service environment investigated. While the definition of service quality and its influential characteristics continue to be important research issues, the understanding of service quality levels being offered relative to competitors is of significant importance to airline

168


strategic management. This is the service quality issue to be addressed. Since the 1980s, the mainstream research on service quality has been conducted based on the notion that quality of service is perceived and evaluated by customers (Gronroos, 1990). Service quality, as perceived by customers, can be measured by an evaluation analysis which results from a comparison between customers’ expectations and experiences. The most widely used customerperceived service quality model is perhaps the Gap Analysis and SERVQUAL model by Parasuraman et al. (1985, 1988). Despite its validation in concept, there is no quantitative yardstick available. In fact, this model may have inherent problems in actually measuring customer expectations of service quality. Gronroos (1993), thus, suggests that measuring customer experiences of service quality, as providing a close approximation, is a theoretically valid way of measuring perceived quality. In practice, this simplifies the process of data collection and analysis via survey questionnaires. In fact, service experiences are perceptions of reality, in which prior expectations are inherent. This concept is in line with the consumer behaviour research that views customer attitude as a global evaluation of a product or service. In the context of service quality, attitude can be regarded as an overall evaluation of a service perceived by customers based on their likes and dislikes (Bolton and Drew, 1991; Engel et al., 1995). Customers’ attitude towards a service depends on: (a) the strength of their beliefs about various features or attributes associated with the service and (b) the weight of attributes. Customers’ beliefs typically involve perceived associations between the service and its associated attributes, stemming from their direct experiences with the service. The weight of attributes refers to the relative importance of each attribute as perceived by customers. The best known formulation of attitude models is probably the Fishbein’s multiattribute model (Fishbein and Ajzen, 1975; Engel et al., 1995). The model states that a customer’s attitude towards a given object (e.g. a service) is based on the summed set of beliefs about the service’s attribute weighted by 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 directly interact with the utility functions of the customer (BTCE, 1992). This concept coincides with MA methods based on multiattribute utility (or value) theory (Hwang and Yoon, 1981; Dyer et al., 1992; Stewart, 1992) for ranking a finite set of alternatives characterised by multiple, usually conflicting criteria (attributes). MA in this context has been applied to a wide range of decision problems which require a cardinal preference or ranking of the alternatives (e.g. van Gennip et al., 1997; Raju and Pillai, 1999; Yeh et al., 1999b). This is the fundamental methodology on which the approach presented in this paper is based.

3. Evaluation criteria of service quality for Taiwan’s domestic airlines

The airline’s service quality perceived by customers is normally represented and measured by a number of manageable, distinct dimensions or attributes. As suggested by existing research results, context-dependent service quality attributes are to be identified for evaluating Taiwan’s domestic airlines. To this end, a comprehensive investigation was conducted by consulting airline managers, government officials, expert academics and travel agents in Taiwan. As a result, 15 service attributes embodied by five categories (construct factors) were selected, as given in Fig. 1. These attributes are independent of each other, thus constituting the criteria ( C 1 , C 2 ; . . . ; C 15 ) used in the fuzzy MA model for evaluating service quality performance of Taiwan’s domestic airlines. The evaluation criteria in Fig. 1 reflect the major concerns of passengers travelling on shorthaul routes between two cities in a Taiwanese context. They also represent the service attributes over which Taiwan’s domestic airlines have control and with which they can differentiate themselves from other competitors. They correspond to the expressive performance of airline services, known as the functional quality. The functional quality is concerned with the service delivery process, thus reflecting customers’ experiences of service quality. Research has shown that the functional quality plays the most critical role in


169

Fig. 1. Criteria used for service quality evaluation of Taiwan’s domestic airlines.

customer’s overall quality perception, and successful service management means the continuous improvement of the functional quality of services (Gronroos, 1984, 1993).

4. Representing customers’ assessments as fuzzy sets

The level of the functional quality perceived by customers is to be assessed in a subjective manner via a survey process. The result of this subjective assessment is intrinsically imprecise due to the characteristics of airline services. This imprecision is inevitable, especially when the time frame available for airline passengers to make estimates of quality levels is typically short. To reflect the subjectiveness and imprecision involved in the survey process, the assessments made by all passengers with respect to criteria weights and performance ratings of each airline on each criterion are represented as fuzzy sets. Modelling using fuzzy sets has proven to be an effective way for

formulating decision problems where the information available is subjective and imprecise (Zimmermann, 1996; Hellendoorn, 1997). Customer-perceived service quality has been universally measured on a point estimate basis (Rust et al., 1999). For example, in a survey process, each of N passengers of an airline is asked to give a rating xk ðk 2 f 1; 2; . . . ; LgÞ on an L-point Likert-type scale for an assessment item such as the importance of a criterion or the performance rating of an airline with respect to an evaluation criterion. The assessments of all N passengers of the airline with respect to the assessment item are aggregated and represented by a discrete fuzzy set, whose membership function is given as l A ð xk Þ ¼

a1 a2 ak a L þ þÁÁÁ þ þÁÁÁ þ ; x1 x2 xk x L

ð1Þ

k ¼ 1; 2; . . . ; L;

where the ‘‘ – ’ ’ sign is used to link the elements xk ðk ¼ 1; 2; . . . ; LÞ of A with their corresponding


170

degrees of membership ak ðk ¼ 1; 2; . . . ; LÞ in A, and the ‘‘+’’ sign indicates that the listed pairs of elements and membership degrees collectively form the definition of the fuzzy set A (Klir and Yuan, 1995). The degree of membership of the elements ak in A is defined as ak ¼

N k ; N

L

X

N k ¼ N ;

0 6 ak 6 1;

k ¼1

k ¼ 1; 2; . . . ; L;

ð2Þ

where N k is the number of passengers who give an xk rating for the assessment item. As an overall assessment result for an airline’s performance on a service attribute (evaluation criterion), ak represents the possibility of the airline having an xk rating on the service attribute. This implies that the values for the assessments obtained for each assessment item are regarded as possibilities which are measured using fuzzy sets. With the representation of all passengers’ assessments on as assessment item as a fuzzy set, there is no need for a consensus test such as Kendall’s coefficient of concordance among the passengers, as often required by the mean method. This is because all the passengers’ perceived ratings are incorporated into the fuzzy set to represent the assessment result of the passengers as a whole.

5. The fuzzy multicriteria analysis approach

In this paper, we formulate the performance evaluation of service quality for Taiwan’s domestic airlines as a fuzzy MA problem with customerperceived performance ratings and criteria weights represented as fuzzy sets.

5.1. The service quality evaluation problem

The problem usually involves a set of n alternatives (airlines) Ai ði ¼ 1; 2; . . . ; nÞ: The service quality levels provided by these alternatives are to be evaluated by their customers in terms of a set of m criteria C j ð j ¼ 1; 2; . . . ; mÞ, which are independent of each other. A fuzzy matrix (referred to as

the decision matrix) for m criteria and n alternatives is to be given as

X ¼

2 64

x11 x21

x12 x22

...

...

xn1

xn2

. .. . .. ... . ..

x1m x2m

... xnm

3 75

;

ð3Þ

where xij represent the overall assessments of the service quality level of alternative Ai ði ¼ 1; 2; . . . ; nÞ with respect to criterion C j ð j ¼ 1; 2; . . . ; mÞ. Expressed as in (1), xij are fuzzy sets characterised by the point estimates of all customers of alternative Ai . A fuzzy weighting vector representing the relative importance of the criteria perceived by customers is to be given as W ¼ ðw1 ; w2 ; . . . ; wm Þ:

ð4Þ

Expressed as in (1), w j ( j ¼ 1; 2; . . . ; m) are fuzzy sets characterised by the point estimates of all customers involved.

5.2. The solution procedure

With the problem structure defined above, mainstream fuzzy MA models in the context of multiattribute utility theory are developed based on a two-phase approach (Zimmermann, 1987; Chen and Hwang, 1992). First, the fuzzy assessments with respect to all criteria for each alternative are aggregated. Second, alternatives are ranked based on the comparison of their aggregated overall assessments represented as fuzzy sets. The main problem with this approach lies in the fact that the comparison of fuzzy sets is not always straightforward and reliable (Zimmermann, 1987; Chen and Hwang, 1992; Chen and Klein, 1997). To overcome the problem of comparing fuzzy sets, we present an effective algorithm for generating a crisp performance index for each alternative. The algorithm is based on the concepts of the degree of optimality and the ideal solution (Hwang and Yoon, 1981; Zeleny, 1982). These two concepts have been widely used in different decision contexts due to their simplicity and applicability in solving various MA problems (e.g. Chen and


Hwang, 1992; Zeleny, 1998; Liang, 1999; Yeh et al., 1999a). With a process of transforming a fuzzy vector into a fuzzy singleton (Zadeh, 1973) vector, the algorithm can incorporate the DM’s attitude or preference for the assessments into the evaluation procedure. This transformation process is presented below. Given a fuzzy vector V ¼ ð v1 ; v2 ; . . . ; vm Þ, such as ð x1 j ; x2 j ; . . . ; xnj Þ of the decision matrix for criterion C j or the weighting vector W , the degree to which v j is the best result in V is calculated by comparing it with the fuzzy maximum ð M jmax Þ (Yager, 1980; Zadeh, 1998), given as

(X

minðlvj ð xk Þ; l M jmax ð xk ÞÞ

L

u R j ¼

maxðlvj ð xk Þ; l M jmax ð xk ÞÞ

k ¼1

!),

L;

j ¼ 1; 2; . . . ; m:

171

(represented by the fuzzy maximum), thus reflecting the DM’s optimistic view. In line with this concept, the DM’s pessimistic view can be represented by the degree to which v j is not the worst result. This can be calculated by comparing it with the fuzzy minimum ð M jmin Þ, given as

(X L

u L j ¼ 1 À

k ¼1

minðlv j ð xk Þ; l M j ð xk ÞÞ min

maxðlv j ð xk Þ; l M j ð xk ÞÞ min

!),

j ¼ 1; 2; . . . ; m;

L;

ð7Þ

where l M j ð xk Þ ¼ min

ð5Þ

x jmax À xk j

j

xmax À xmin

;

k ¼ 1; 2; . . . ; L;

ð8Þ

n

lvj ð xk Þ is defined in (1) and the membership function of M jmax is defined as l

j M max

ð xk Þ ¼

xk À j

j xmin j xmin

xmax À

k ¼ 1; 2 . . . ; L;

ð6Þ

where n

x jmax

[ [

¼ sup

f xk ; xk 2 R and 0 6 lv j ð xk Þ 6 1g;

i¼1

n

j xmin

¼ inf

f xk ; xk 2 R and 0 6 lv j ð xk Þ 6 1g:

i¼1

u Rj represents the overall similarity degree between v j and the fuzzy maximum ( M jmax ). The similarity measure used in (5) denotes the average of the similarity degrees on all elements in V . It has the significance of average (i.e. each element in V plays an equal role) as compared with other similarity measures (Wang, 1997). The similarity concept used in (5) coincides with possibility theory on fuzzy sets (Klir and Yuan, 1995). The similarity degree between V and an ideal solution V þ (for which the possibility degree is 1) is expressed by a suitable distance between V and V þ (the possibility of V ) defined in terms of relevant attributes of the elements in V . Thus, u Rj in (5) reflects the highest degree of possibility of v j ’s performance to the ideal solution

f x; x 2 R and 0 6 lv j ð xÞ 6 1g;

i¼1

n

j

;

[ [

x jmax ¼ sup xmin ¼ inf

f x; x 2 R and 0 6 lv j ð xÞ 6 1g:

i¼1

In actual decision settings, the DM’s attitude is not necessarily to be absolutely optimistic or pessimistic, but somewhere in between. An attitude index k in the range of 0 and 1 is thus used to indicate the DM’s relative preference between u R j and u L j . Incorporated with the attitude index k, a fuzzy singleton vector S ¼ ð s1 ; s2 ; . . . ; sm Þ is determined by s j ¼ ku R j þ ð 1 À kÞu L j ; j ¼ 1; 2; . . . ; m; ð9Þ where s j indicates the degree of optimality of v j or its degree of preferability over all others in V . In practical applications, k ¼ 1, k ¼ 0:5 or k ¼ 0 can be used to indicate that the DM has an optimistic, moderate or pessimistic view, respectively, on assessment results represented as fuzzy sets. An optimistic DM is apt to prefer higher values of the fuzzy sets, while a pessimistic DM tends to favour lower values. In the context of the case study presented in this paper, an optimistic DM would pay more attention on favourable assessments (high ratings), while a pessimistic DM is more concerned about unfavourable assessments (low ratings).


172

Incorporated with the transformation process described above, the algorithm for ranking alternatives with the weighting vector and the decision matrix given as fuzzy sets is presented as follows: Step 1. Obtain the fuzzy singleton vector Y ¼ ð y 1 ; y 2 ; . . . ; y m Þ for the criteria weights from the weighting vector W by (5)–(9) with a given attitude index 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 the degree of optimality of each alternative regarding each criterion based on the weighted decision matrix obtained at Step 2 by (5)–(9), resulting in a fuzzy singleton performance matrix, given as

Z ¼

2 64

z 11 z 21

z 12 z 22

...

...

z n1

z n2

. .. . .. ... . ..

z 1m z 2m

... z nm

3 75

;

ð10Þ

where z ij ði ¼ 1; 2; . . . ; n; j ¼ 1; 2; . . . ; m) indicates the degree of preferability of alternative Ai over all other alternatives in regard to criterion C j . Step 4. Determine the positive ideal solution þ ð z Þ and the negative ideal solution ð z À Þ (Hwang and Yoon, 1981; Zeleny, 1982) by þ þ z þ ¼ ð z þ 1 ; z 2 ; . . . ; z m Þ;

À À z À ¼ ð z À 1 ; z 2 ; . . . ; z m Þ;

ð11Þ

where z jþ ¼ supð z 1 j ; z 2 j ; . . . ; z n j Þ; z jÀ ¼ inf ð z 1 j ; z 2 j ; . . . ; z nj Þ; j ¼ 1; 2; . . . ; m:

ð12Þ

Step 5. Calculate the Hamming distance between each alternative and the positive ideal solution ð z þ Þ and the negative ideal solution ð z À Þ, respectively, by m

þ

d i ¼

X X

ð z jþ À z ij Þ;

j¼1

ð13Þ

m

d iÀ ¼

j¼1

ð z ij À z jÀ Þ;

i ¼ 1; 2; . . . ; n:

Step 6 . Compute the overall crisp performance index for each alternative by d À P i ¼ þ i À ; i ¼ 1; 2; . . . ; n: ð14Þ d i þ d i

The larger the performance index, the more preferred the alternative. This is based on the concept that the most preferred alternative should not only have the shortest distance from the positive ideal solution, but also have the longest distance from the negative ideal solution.

6. Empirical study

Taiwan’s domestic passenger airline market has become a major transport service sector with an annual average growth rate of nearly 20% since deregulation in 1987. To examine passengers’ perceptions of service quality for Taiwan’s domestic airlines, the Taipei–Tainan route was chosen. Being a major route with more than 60 scheduled flights per day, the Taipei–Tainan route is served by four airlines, namely, Far Eastern Air transport ð A1 Þ, TransAsia Airways ð A2 Þ, Eva Air ð A3 Þ and Great China Airlines ð A4 Þ. A survey questionnaire was designed to measure the existing quality levels of services perceived by passengers of four airlines. The passengers of each airline were asked to rate the importance of the evaluation criteria (service attributes) in Fig. 1 and assess the performance of the airline on each criterion on an 11-point scale ranging from 10 (extremely high) to 0 (extremely low). This scoring method is familiar to the general public in Taiwan, thus, better reflecting their perceptions on an assessment item in terms of scores. The questionnaire form is given in Appendix A. The survey process was conducted at Tainan Airport in the morning and afternoon over a period of one month. The survey questionnaire was randomly given face-to-face to both arriving and departing passengers who have flown the Tainan– Taipei route with the same airline at least twice in the last six months. Frequent travelers were preferred as first-time travelers may find it difficult to evaluate and discern differences in service quality, especially before the service (Turley, 1990). A total

173


Table 1 Fuzzy singleton weights of evaluation criteria Criteria C 1 Weight

C 2

C 3

C 4

C 5

C 6

C 7

C 8

C 9

C 10

C 11

C 12

C 13

C 14

C 15

0.4482 0.4465 0.4087 0.4541 0.4653 0.4682 0.4536 0.5026 0.5085 0.4867 0.4508 0.4613 0.4513 0.4552 0.4575

Table 2 Fuzzy singleton performance matrix of four airlines Airlines

C 1

C 2

C 3

C 4

C 5

C 6

C 7

A1 A2 A3 A4

0.4120 0.3938 0.4186 0.4244

0.3784 0.4144 0.4120 0.4102

0.4302 0.4136 0.3972 0.3876

0.3916 0.4036 0.3948 0.3928

0.4250 0.3702 0.4226 0.3934

0.3902 0.4132 0.4180 0.3954

0.4134 0.4078 0.3998 0.3978

of 390 respondents were selected and 354 effective responses (93 for A1 , 80 for A2 , 109 for A3 and 72 for A4 Þ were received. Most respondents were able to complete the questionnaire within 10 minutes. Because all surveys conducted were essentially identical at around the same time, the survey results can be aggregated and used as the existing passenger’s overall perceptions of criteria weights and performance ratings on service quality provided by four airlines. In what follows, we briefly illustrate how the fuzzy MA approach presented in the previous section is used to rank four airlines based on the respondents’ assessments. We first consider the situation where the DM has a moderate attitude toward the respondents’ assessments, that is, kw ¼ 0:5 and kr ¼ 0:5. This indicates the DM weights all the responses equally. Given all the respondents’ valid assessments on criteria weights and kw ¼ 0:5, a fuzzy singleton vector Y , as shown in Table 1, is obtained by (1) and (2) and (5)–(9). Given the assessments of all the respondents of individual airlines, the decision matrix X , expressed as in (3) with n ¼ 4 and

C 8

0.4658 0.4640 0.4824 0.4360

C 9

0.4436 0.4418 0.4018 0.4426

C 10

0.4324 0.4182 0.4400 0.4238

C 11

0.4314 0.4340 0.3888 0.3828

C 12

C 13

C 14

C 15

0.4070 0.4120 0.3848 0.4026

0.3766 0.4024 0.3942 0.3822

0.4172 0.3748 0.3962 0.3808

0.4024 0.3834 0.3972 0.3906

m ¼ 15, can be determined. A weighted decision matrix is accordingly generated by multiplying Y by X . Given the weighted decision matrix and kr 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, the overall performance index P i of four airlines Ai ði ¼ 1; 2; 3; 4Þ can be obtained by (11)–(14). The values of Ri ði ¼ 1; 2; 3; 4Þ are 0.6782, 0.4233, 0.7832, and 0.2227, respectively, indicating the ranking order of their service quality performance is A3 > A1 > A2 > A4 . To examine how the DM’s attitude or preference for the customers’ assessments may affect the evaluation outcome, further experiments were carried out by changing the values of kw and kr . Some representative results are given in Table 3. The evaluation outcome presented in Table 3 reflects that the passengers of four airlines have different views on the quality level of their services, consistent with the survey results. The overall rankings of four airlines are clearly affected by the DM’s preference for: (a) favourable responses

Table 3 Performance rankings of four airlines under different kw and kr Ranking order (performance index)

kw ¼ 0:0

kr ¼ 0:0

A1 > A3 > A2 > A4 ð0:8215 > 0:5920 > 0:5056 > 0:2010Þ A3 > A1 > A2 > A4 ð0:8946 > 0:7508 > 0:3187 > 0:2407Þ A3 > A4 > A1 > A2 ð0:9960 > 0:4187 > 0:2065 > 0:1910Þ

kr ¼ 0:5 kr ¼ 1:0

kw ¼ 0:5

kw ¼ 1:0

A1 > A2 > A3 > A4

A2 > A1 > A3 > A4

ð0:7089 > 0:6103 > 0:5260 > 0:1752Þ ð0:7182 > 0:3746 > 0:2291 > 0:1755Þ A3 > A1 > A2 > A4

A2 > A3 > A4 > A1

ð0:7832 > 0:6782 > 0:4233 > 0:2227Þ ð0:6590 > 0:4290 > 0:2853 > 0:2554Þ A3 > A4 > A1 > A2

A3 > A4 > A2 > A1

ð0:9989 > 0:4000 > 0:1966 > 0:1190Þ ð0:7641 > 0:4602 > 0:2994 > 0:2727Þ

174


(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 airline management understand how their passengers’ opinions are distributed relative to their competitors. For example, airline A1 ranks the highest when the unfavourable opinions are weighted more, and ranks the lowest when the favourable opinions are weighted more. This implies that relatively fewer passengers of airline A1 have given low ratings or high ratings on their service performance as compared with other airlines. This means that the assessments made by airline A1 ’s passengers are relatively consistent. Airline A3 performs the best consistently except for the situations where unfavourable opinions are weighted more. This indicates that relatively more passengers of airline A3 think their service performance is the best, while some passengers have different views. The evaluation process and the corresponding outcomes can help an airline identify its competitive advantages relative to its competitors in a specific context. The airline can concentrate on improvement of certain service attributes that are important in affecting relative rankings. To examine the airlines’ relative competitive strengths and weaknesses on service attributes identified as

important to their customers, a competitiveness analysis can be carried out based on the weighted performance evaluation result in Table 2. In the analysis, we regard the best or worst five service attributes assessed by passengers of an airline as the internal strengths or weaknesses of the airline. For a particular service attribute, the two airlines with higher or lower performance rankings are regarded as having external strengths or weaknesses on the attribute. By combining the competitive strengths and weaknesses both internally and externally, the overall competitiveness of airlines on individual service attributes can be obtained. Table 4 shows the results, which are based on the situation where all the passengers’ assessments are weighted equally. The internal competitiveness result in Table 4 indicates that the reliability of service ðC 8 , C 9 and C 10 Þ of airlines as a whole performs much better than the handling of abnormal conditions ðC 13 , C 14 and C 15 Þ. The airlines with more external competitive strengths in these two categories (such as A3 and A1 Þ have a higher overall ranking. The overall competitiveness of airlines indicated in Table 4 is consistent with their performance rankings. These evaluation results would help airlines better manage their competitive advanta-

Table 4 Competitive strengths and weaknesses of four airlines Internal A1 C 1 C 2 C 3 C 4 C 5 C 6 C 7 C 8 C 9 C 10 C 11 C 12 C 13 C 14 C 15

X O X

External A2

A3

A4

A1

A2

A3

A4

X O

O

O O X

X X O X O X O O O O O O X O O

X O O O X O O X O X O O O X X

O O X O O O X O X O X X O O O

O X X X X X X X X X X X X X X

X X O O O O X X

Overall

O O O O X X X

X O O O O X X X X

O O O X X X X

O: Competitive strength; X: Competitive weakness.

A1

X O X

A2

A3

A4

X O

O

O X

X X O O O O

O O O

O O O

X X

X X X

X X X


ges and provide an incentive for them to improve quality levels of specific services relative to their competitors.

7. Conclusion

The intensive competition of the domestic airline market under deregulation has made airlines adopt an attitude towards customer-oriented service quality. To help airlines better understand how the customer views their services relative to their competitors, a customer-driven evaluation approach of service quality has been presented. Crisp assessments of all the customers on quality levels of service provided by airlines are modeled as fuzzy sets to better reflect the inherent subjectiveness and imprecision of the survey process. A fuzzy MA model with effective handling of fuzzy data has been developed to evaluate the relative performance of airlines in terms of customers’ perceptions of service quality. The DMs’ attitude or preference for the customers’ assessments on criteria weights and performance ratings can be specified to reflect their major concerns on various customers’ opinions. An empirical study of a domestic route in Taiwan has been conducted to demonstrate the effectiveness of the approach. The evaluation outcome helps airlines identify their internal and external competitive advantages relative to their competitors. It provides a guideline for airlines to provide appropriate levels of service in response to customers’ needs. The underlying concepts used by the approach are comprehensible, and the survey process and computations required are straightforward and simple. The approach is particularly applicable to major routes between two cities which are served by several airlines.

Acknowledgements

This research was supported in part by the National Science Council of Taiwan, ROC, under Grant No. NSC88-2811-E006-0013. We are grateful to the Tainan Airport Administration and four airlines involved for providing assistance in

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problem formulation and data collection. We also thank Prof. Roman Slowinski, the editor, and anonymous referees for their valuable comments and advice.

Appendix A. Airline service quality survey

This questionnaire is purely an academic research survey, aiming at understanding the current quality level of airline services on the Taipei– Tainan route. The survey result will be strictly used for academic purposes only, in which no individual responses can be identified. To ensure the fairness and effectiveness of the responses, we expect that all participants have flown with the same airline at least twice in the past six months. It should only take 10 minutes to complete. Section 1 The 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 TransAsiaAirways EvaAir GreatChina Airlines 2. How many times did you fly with the same airline 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 Personal Other 4. What is your main reason of choosing this airline? Service quality Discount price Timing At random Other 5. How many times did you fly in the last year? 1 2–5 6–10 11 or more Section 2 Based on your experiences and expectations as a passenger of domestic airlines, please rate how important the following service attributes are to you when you choose an airline. The score 10 represents that the attribute is extremely important, and the score 0 means that the attribute is not important at all. There are no correct answers. The score you circle or tick should truly reflect your

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feelings about the relative importance of airline services that would affect your airline choice. 1.Cleanliness and low noise level of aircraft 2. Good on-board facilities (seat comfort and spaciousness) 3. Good on-board services (meals, drinks and newspapers) 4. Helpful attitudes and courtesy of check-in personnel 5. Good attention by stewardesses 6. Good appearance and courtesy of airline personnel 7. Good service efficiency of airline personnel 8. Low level of security-related accidents 9. Good airline flight safety and security measures 10. Good on-time performance 11. High service frequency and schedule convenience 12. Convenience of pre-flight and post-flight services 13. Good handling of customer complains 14. Good handling of flight delays 15. Good handling of luggage loss or damage

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10

References 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

Section 3 Based 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 following service attributes. The score you circle or tick should truly reflect your feeling about the extent to which the airline service satisfies you. The score 10 represents that you are extremely satisfied with the service for the attribute, and the score 0 means that you are totally dissatisfied with the service for the attribute. 1. Cleanliness and noise level of aircraft 2. On-board facilities (seat comfort and spaciousness) 3. On-board services (meals, drinks and newspapers) 4. Helpful attitudes and courtesy of check-in personnel 5. Attention by stewardesses 6. Appearance and courtesy of airline personnel

7. Service efficiency of airline personnel 8. Security-related accidents 9. Airline flight safety and security measures 10. On-time performance 11. Service frequency and schedule convenience 12. Convenience of pre-flight and post-flight services 13. Handling of customer complains 14. Handling of flight delays 15. Handling of luggage loss or damage

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

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A Survey Analysis of Service Quality for Domestic Airlines

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