a gap analysis model for improving

8/11/2019 A Gap Analysis Model for Improving

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A gap analysis model for improving

airport service qualityWen-Hsien Tsai

a, Wei Hsu

b& Wen-Chin Chou

c

aDepartment of Business Administration , National CentralUniversity , Jhongli, Taoyuan, 32001, TaiwanbDepartment of Business and Entrepreneurial Management ,

Kainan University , Luzhu, Taoyuan, 33857, TaiwancDepartment of Finance , Yu Da University , Chaochiao, Miaoli,

36143, Taiwan

Published online: 20 Sep 2011.

To cite this article:Wen-Hsien Tsai , Wei Hsu & Wen-Chin Chou (2011) A gap analysis model

for improving airport service quality, Total Quality Management & Business Excellence, 22:10,

1025-1040, DOI: 10.1080/14783363.2011.611326

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A gap analysis model for improving airport service quality

Wen-Hsien Tsaia, Wei Hsub and Wen-Chin Chouc

aDepartment of Business Administration, National Central University, Jhongli, Taoyuan 32001,

Taiwan; bDepartment of Business and Entrepreneurial Management, Kainan University, Luzhu,Taoyuan 33857, Taiwan;

cDepartment of Finance, Yu Da University, Chaochiao, Miaoli 36143,

Taiwan

Due to the increasing importance of customer orientation to a competitive advantage,airport managers emphasise passengers perceptions and expectations of airportservices quality. This paper aims to develop a multi-criteria evaluation model toevaluate the gap between passengers perceptions (perceived service quality) and theirexpectations (expected service quality), and to diagnose managerial strategies of gapreduction within the airport passenger service context. This multi-criteria evaluationmodel is combined with the analytic hierarchy process method, the VIKOR(VIsekriterijumska optimizacija i KOmpromisno Resenje in Serbian, which meansMulticriteria Optimization and Compromise Solution) method, and the importanceperformance analysis (IPA) technique. The multi-criteria model can not only overcomethe weaknesses of traditional IPA, it can also consider passenger preferences andsatisfaction simultaneously to analyse managerial strategies for reducing the customergap, thus improving service quality and meeting passengers expectations. This paperalso provides an empirical case study of passenger services at Taoyuan InternationalAirport in Taiwan, demonstrating the suitability and effectiveness of the multi-criteriaevaluation model.

Keywords:air transport; passenger service evaluation; perceived service quality; gapanalysis; multiple criteria decision analysis

1. Introduction

Nowadays, there is an increasing urgency among airport managers to differentiate their

airports by meeting passengers needs; managers clearly understand the importance of pas-

senger perceptions of airport service quality (Fodness & Murray, 2007). While passengers

perceptions of airport service quality are only one of several variables that contribute to

overall airport attractiveness, it is nevertheless an important factor because of the

increasing importance of customer orientations to a competitive advantage in the industry

(Fodness & Murray, 2007). Thus, researchers measure passenger perceptions of airportservice quality based on the voice of the customer, using these measurements to build

performance benchmarks (Chen, 2002; Fodness & Murray, 2007) and to identify opportu-

nities for service improvement (Yeh & Kuo, 2003).

The service quality for an airport is often expressed in terms of perceived level of

service delivered to the airport user (Francis, Humphreys, & Fry, 2003). Zeithaml and

Bitner (2003) introduced the service quality gap model, which identifies the customer

gap occurring between perceived service quality and expected service quality. Service

providers satisfy customers and fulfil their expectations by closing this customer gap.

ISSN 1478-3363 print/ISSN 1478-3371 online

# 2011 Taylor & Francis

http://dx.doi.org/10.1080/14783363.2011.611326

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Corresponding author. Email: [email protected]

Total Quality Management

Vol. 22, No. 10, October 2011, 10251040


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Based on the concept of the customer gap, managers in the airport industry find multiple

criteria that enable service evaluations, which help managers to achieve the degree of

expected/aspired levels; the gap notion also helps analyse performance-value distances,

which helps managers to improve services or set improvement goals, ultimately develop-

ing a win win strategy.

An improvement strategy for perceived service quality can only be effective and effi-

cient if it is based on an appropriate selection of service attributes in need of improvement

(Lin, Chan, & Tsai, 2009). In this regard, an importanceperformance analysis (IPA)

technique (Martilla & James, 1977) can be used to set priorities that are based on the

voice of the customer (Mikulic & Prebezac, 2008). In our evaluation model, we

employed the analytic hierarchy process (AHP) method and the VIKOR (VIsekriteri-

jumska optimizacija i KOmpromisno Resenje in Serbian, which means Multicriteria

Optimization and Compromise Solution; Opricovic, 1998, 2011) method to overcome

the shortcomings of the traditional IPA.

This paper aims to evaluate the customer gap between passenger perceptions (passen-

gers perceived service quality) and expectations (passengers expected service quality)and to analyse appropriate strategies to reduce the gap within the context of airport

passenger service quality. This evaluation is involved in a multiple criteria decision-

making (MCDM) problem. Chang and Yeh (2002) had used an MCDM model to evaluate

service quality for domestic airlines in Taiwan. In terms of the analysis of airport passen-

ger service quality, we provide a multi-criteria model, combining AHP, VIKOR and IPA

methods, which can consider airport passenger preferences and satisfaction simul-

taneously. In this study, the AHP method was employed to measure the relative impor-

tance of each criterion as the passenger preference of airport passenger service; then,

the VIKOR method was used to integrate the passenger preferences (obtained from

AHP) and satisfaction simultaneously in order to compute the customer gaps of airportpassenger service. After evaluating these gaps, we address managerial strategies to

improve airport passenger services by reducing the gaps based on IPA techniques.

2. Literature review

Service quality is a function of service quality gaps (Candido, 2005). The service quality

gap model, developed by Parasuraman, Zeithmal and Berry in the late 1980s (Parasura-

man, Zeithaml, & Berry, 1985; Parasuraman, Berry, & Zeithaml, 1988, 1991), positions

the key concepts in services marketing in a manner that begins with the customer. The

organisations tasks are then built around what is needed to close the gap between custo-

mer expectations (customers expected service quality) and perceptions (customers per-

ceived service quality) (Zeithaml & Bitner, 2003). Customer perceptions are subjective

assessments of actual service experiences; customer expectations are beliefs about

service delivery that function as reference or ideal points against which performance is

evaluated. Customers not only compare their perceptions of performance with these

ideal points when evaluating service, but they also perceive services in terms of the

quality of the service and how satisfied they are with their overall experiences. The

central focus of the service quality gap model is the customer gap, shown in Figure 1,

which represents the difference between customer expectations and perceptions (Zeithaml

& Bitner, 2003). This gap needs to be closed in order to satisfy customers, enabling firms

to build long-term relationship with their customers. Customer perceptions of a service are

focused evaluations of satisfactions that reflect the customers perceptions of the three

elements, which are physical environment, interaction and outcome (Zeithaml &

1026 W.H. Tsai et al.


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Bitner, 2003). Accordingly, passengers will judge airport services based on theirperceptions of the technical outcome, the process by which that outcome was delivered

and the quality of the physical surroundings where the service was delivered.

An IPA technique (Martilla & James, 1977) is useful to diagnose the managerial strat-

egies by analysing passenger preferences (importance) and passenger satisfaction (per-

formance). IPA has been popular among researchers and practitioners involved in

customer satisfaction and quality management, and several empirical studies have used

the IPA technique to identify critical attributes or features of various products (Matzler,

Bailom, Hinterhuber, Renzl, & Pichler, 2004). Passenger satisfaction is a key performance

indicator for the operation of an airport (Yeh & Kuo, 2003) and should play a central role

in the airports total quality management (Eklof & Westlund, 1998). For traditional IPA,researchers have examined actual or relative performances of customer satisfaction. In

previous airport service studies, the majority of passenger satisfaction surveys only ana-

lysed the actual performance and did not consider the performance relative to an ideal

level (e.g. Chang, Liu, Wen, & Lin, 2008; Park, 2007). Actual performance scores are

not compared with the competition or the reference point and do not enable researches

to discern the split point between high and low performance (e.g. Matzler et al, 2004).

When examining relative performance, researchers can easily use the midpoint of the

performance axis as the split point to discriminate between high and low performance.

However, previous researchers calculated performance scores by comparing the relative

performance to that of the best competitor (e.g. Garver, 2003). Problems arise when the

competitors have equivalent or worse performances, which render the competitors as

inappropriate reference points (Tsai, Hsu, & Lin, 2011). In order to avoid this problem,

we employed the VIKOR method; it is a multiple criteria method to build a ranking

index based on the particular measure of closeness to the ideal level and allows the

decision-maker to evaluate the relative distances between perceived performances and

the ideal performance (Opricovic & Tzeng, 2004).

The traditional IPA uses stated or statistically inferred methods to determine the

importance ratings for attributes (e.g. Garver, 2003; Matzler et al., 2004). Stated impor-

tance ratings often display an inability on the customers part to discriminate between pre-

ferences of attributes (Myers, 2001). Customers often deem that everything is very

important. Analysis reveals that 78% of customer service attributes are very important,with little variance in importance between these attributes (Garver, 2003). The purpose of

IPA is to determine the relative importance of attributes and to prioritise improvement

Figure 1. Service quality gap model (source: Zeithaml & Bitner, 2003, p. 533).

Total Quality Management 1027


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opportunities accordingly. This purpose is defeated when everything is classified as very

important. Although statistically inferred methods can overcome the shortcomings of

stated importance ratings, they carry the assumptions of relatively normal data, linear

relationships between independent and dependent variables, and the relatively low

multi-collinearity between independent variables; in customer satisfaction research,

these assumptions are almost always violated (Garver, 2003).

The AHP method can overcome these weaknesses and has been introduced to deal with

passenger preferences of airport service (Yoo & Choi, 2006). The AHP method is a multi-

criteria decision analysis tool that uses pair-wise comparisons to determine the relative

ranking or preferences of decision alternatives. It has an advantage in obtaining a set of

weights from measuring relative importance of service attributes or criteria; this set of

weights, represented as customer preferences, can be subsequently involved in an evalu-

ation of the customer gaps in terms of improving airport passenger services. According to

the service quality gap model, quality airports are those that can eliminate the gap between

perceived and expected services. To be successful, airport managers must be able to

integrate customer requirements and expectations into a service strategy. In this paper,we apply the IPA technique to devise managerial strategies that efficiently reduce the

customer gaps in airport passenger service within the empirical cases of Taiwans inter-

national airport.

3. The evaluation model

The evaluation model, shown in Figure 2, built in the present paper was combined with the

AHP method, the VIKOR method and the IPA technique. The AHP method was employed

to measure the relative importance (passenger preferences) of each attribute or feature of

airport passenger service; the VIKOR method was used to integrate passenger preferencesand satisfactions simultaneously in order to compute the customer gaps of airport

passenger service. After evaluating the customer gaps of airport passenger services, the

IPA technique was employed to integrate the relative importance from AHP and the

distances of unimproved gaps to analyse managerial strategies to improve the airport

passenger service by reducing the gaps. The following sections describe the three

methods of our proposed evaluation model in greater detail.

Figure 2. A scheme of the evaluation model.



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3.1. The AHP method

The AHP method for determining the relative values of various criteria or features of

airport passenger service was used to model passenger preferences in response to experi-

mentally designed services. This method has been used successfully to detect the prefer-

ences of decision-makers in a variety of academic fields, including airline service quality

(Liou & Tzeng, 2007), hospitality management (Tzeng, Teng, Chen, & Opricovic, 2002),

conflict management (Lam & Chin, 2005), public transportation (Tzeng, Lin, & Opricovic,

2005), Internet marketing system performance evaluation (Shih & Hu, 2008), Internet

retailing pricing (Tsai & Hung, 2009a) and green supply chain optimisation (Tsai &

Hung, 2009b). The AHP method is also a measurement theory that prioritises the hierarchy

and consistency of judgmental data provided by a group of decision-makers. Other

approaches that can define the factor utilities may also be used for priority purposes, but

AHP is a robust approach that is easy to implement (Seol & Sarkis, 2005).

AHP is a systematic procedure used to represent the elements of a problem hierarchi-

cally. The AHP method was developed by Saaty in 1971 (Saaty, 1980), with the three

main procedures summarised as following steps:

Step 1: Structuring the hierarchy for evaluation. The AHP method is used to make

the decomposition (or structuring) of the problem as a hierarchy. Thus, the systematical

hierarchy of multiple criteria should be structured in this step.

Step 2: Constructing the pair-wise comparison matrix. After structuring the hierarchy,

the pair-wise comparisons should be conducted for discovering the relative importance of

different criteria with respect to attributes.

Step 3: Calculating the weights and testing the consistency. For each pair-wise com-

parison matrix (A); we used the theory of eigenvector, i.e. (A lmaxI)w = 0, to calculate

the eigenvalue .. and the eigenvector (w = (w1,w2,..., wn)) (weights can be estimated).

Finally, the consistency of the comparison matrix was tested and the opinions of theregional decision-maker group were integrated. In the consistency test (Saaty, 1990), a

consistency index (CI = (lmax n)/(n 1))was used to verify the consistency of the

comparison matrix, where RI represents the average consistency index over numerous

random entries of same order reciprocal matrices, and a consistency ratio (CR = CI/RI)

was utilised to determine the degree of consistency. When CR 0.1, it is acceptable

(Saaty, 1990).

3.2. The VIKOR method

The VIKOR method was developed to solve MCDM problems, assuming that compromis-

ing is acceptable for conflict resolution, the decision-maker wants a solution that is the

closest to the ideal, and the alternatives are evaluated according to all established criteria

(Opricovic & Tzeng, 2007). VIKOR has been applied to several different fields such as

hospitality management (Tzeng et al., 2002), public transportation (Tzeng et al., 2005),

policy-making (Yang & Wang, 2006), university development (Chen & Chen, 2008)

and water resources planning (Opricovic, 2011). Wang, Ho, Feng and Yang (2004)

applied another compromise ranking method, Technique for Order Preferences by

Similarity to an Ideal Solution (TOPSIS), to evaluate the operational performance of

Taiwans airports. However, Opricovic and Tzeng (2004) compared VIKOR and

TOPSIS, which are based on an aggregate function representing closeness to the ideal

point, and also demonstrated that TOPSIS does not consider the relative importance of

these distances. The VIKOR method provides measurements of determining the aggregate

relative distance between a perceived alternative and the ideal point (Tsai, Chou, & Lai,



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2010), and it is appropriate and useful for this study. In order to involve passenger prefer-

ences in the computation of the relative distance between a compromise solution and the

ideal point, we applied the weights of criteria obtained from the AHP method into the

VIKOR method. In terms of simultaneously considering both passenger preferences and

satisfaction to determine the relative distance between the perceived alternative (customer

satisfaction of perceptions/perceived services) and the ideal point (customer satisfaction of

expectations/expected services), we finally used the VIKOR method to integrate both of

the weights of passenger preferences and satisfaction and to analyse the customer gaps

of airport passenger services.

The multi-criteria merit for the VIKOR method is developed from the dqmetric used

in the compromise programming method (Zeleny, 1982). The alternative airport in this

paper is Taoyuan International Airport; the n criteria of airport passenger services will

be denoted as c1, c2, ..., cn. fi is the evaluation value of ith criterion (ci) function and

the larger fi represents better performance.

Figure 3 illustrates the best possible point, F = (f1, f

2), the worst possible point,

F = (f1 , f2 ), and a perceived alternative Fp = (fp1 ,fp2 ) for a two-criterion problem.Compromise programming method introduces thedqmetric as an aggregate function.

The development of the VIKOR method started with the following form of the dqmetric:

dq =ni=1

wi(f

i fi)

(fi f

i )

q 1/q, 1 q 1. (1)

The VIKOR method was used here according to the following steps:

Step 4: Determining the values of the best possible point and worst possible point.Based on the concept of VIKOR, the best possible point (fi ) represents the best perform-

ance value, and the worst possible point (fi ) means the worst performance value. In Figure

3 (a two-criterion problem), the perceived alternative (Fp) can be mapped out according to

the best possible point (F) and the worst possible point (F). Therefore, the values of the

best possible point and the worst possible point should be determined before the process

of analysis. In this study, we set all fi = 5 and f

i = 1, and the relative distance to best

possible point and worst possible point ((fi fi)/(f

i f

i )) can be calculated.

Figure 3. Illustration of VIKOR (source: Tzeng et al., 2002).Notes: The x-axis indicates values of satisfactions of Criterion 1; the y-axis indicates values ofsatisfactions of Criterion 2. Fp indicates the perceived alternative; F represents the best possiblepoint and F, the axe, represents the worst possible point.



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Step 5: Computing the degree of majority utility and degree of regret . By the com-

promise ranking method, the compromise solution is determined, which could be accepted

by the decision-makers because it provides a maximum group utility of the majority

(with measureCrepresenting the degree of majority utility) and a minimum individual

regret of the opponent (with measure D representing the degree of regret). Thus, we

computed the values of measures C and D, the measure C of the best possible point

(C*), the measure Cof the worst possible point (C), the measure D of the best possible

point (D*) and the measure D of the worst possible point (D) by the relations:

C= dq=1 =ni=1

wi(f

i fi)

(fi f

i ), (2)

D = dq=1 = maxi

(fi fi)

(fi f

i )

i = 1, 2, . . . , n

, (3)

where the weights of the criteria (wi) are introduced to express the relative importance of

the criteria calculated by the AHP method. The smaller value of measure Cindicates the

larger group utility of the majority; the smaller value of measure D indicates the

smaller individual regret of the opponent.

Step 6: Calculating the aggregate distance to the best possible point. We computed the

values of measure G as the aggregate distance to the best possible point by the relation

G =v(C C)

(C C)+

(1 v)(D D)

(D D) , 0 v 1, (4)

where C is the measure Cof the best possible point, C is the measure Cof the worst

possible point, D is the measure D of the best possible point, D is the measure D of

the worst possible point, v is introduced as the weight of the strategy of the majority of

criteria (or the maximum group utility) and 1 v is the weight of the individual

regret. The smaller value of measure G represents the smaller customer gap.

In other words, when v . 0.5, it is an indication thatCis emphasised more than D in

Equation (4), whereas whenv , 0.5, it is an indication that D is emphasised more than C

in Equation (4). More specifically, when v=1, it represents a decision-making process that

could use a strategy of maximum group utility; whereas when v = 0, it represents a

decision-making process that could use a strategy of minimum individual regret, which

is obtained among maximum individual regrets/gaps of lower level criteria. The weight

(v) would affect the measurement of the aggregate gap, which is usually determined by

the experts or decision-makers, usuallyv = 0.5.

3.3. The IPA method

In the strategy analysis phase, we employed the IPA method to diagnose the managerial

strategies for reducing customer gaps. In the traditional IPA method, attributes or criteria

pertaining to a particular service are evaluated on the basis of how important each factor is

to the customer, and how the services performance is perceived according to each

attribute (Sampson & Showalter, 1999). In this study, we first applied the AHP weight

of each criterion as the relative importance value of each criterion, and then we used



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the unweighted relative distance (obtained from VIKOR) to replace the performance and

to represent the relatively unimproved distance.

Step 7: Determining the values of importance and unimproved distance. We applied

the AHP weight (wi) as the relative importance value of the ith criterion (ci). Then, we

used the unweighted relative distance (fi fi)/(f

i f

i ) to indicate the relative unim-

proved distance of the ith criterion (ci) (i.e. fi is the evaluated value of the ith criterion

(ci) of the best possible point; f

i is the evaluated value of the ith criterion (ci) of the

worst possible point and fi is the evaluated value of the ith criterion (ci)).

Step 8: Depicting the improvement strategy map and diagnosing the strategies. We

map the values of unimproved distance (x-axis) and importance (y-axis) of each criterion

on a two-axis map, which is the improvement strategy map shown in Figure 4.

The two-axis improvement strategy map can be interpreted in a straightforward

manner by categorising the mapping of airport criteria/features into one of four types.

In Figure 4, criteria or features in Quadrant I refer to those of relatively higher importance

and larger unimproved distance. The higher importance (AHP weight) of an individual

feature indicates that improving the same spaces of this feature will lead to largerreductions of the aggregate gap than that of another feature with lower importance (i.e.

a feature with the higher importance has more potential to rapidly reduce the aggregate

gap). The larger unimproved distance creates more opportunities of gap reductions

(improvable spaces); therefore, managers should treat these features as a higher priority

for improvement. Features in Quadrant II refer to those with relative high importance

and small unimproved distance; that is, the same improvement leads to greater gap

reductions, but the smaller unimproved distance creates fewer opportunities of gap

reductions. Therefore, managers should try to find the possibility of improvement.

Features in Quadrant III refer to those with relative lower importance and small unim-

proved distance; that is, the same improvement leads to smaller gap reductions and thesmaller unimproved distance creates fewer opportunities of reductions. Thus, the best

strategy for managers may be simply to maintain the good performance. Features in Quad-

rant IV refer to those with relative lower importance but larger unimproved distance; that

is, the same improvement leads to smaller gap reductions but the larger unimproved dis-

tance means creates more opportunities of gap reductions. Thus, managers might need to

make these features a lower priority for improvement. Since a feature with higher impor-

tance and larger unimproved distance has more potential to rapidly reduce the aggregate

Figure 4. Improvement strategy map of gap reduction.



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gap and creates more opportunities to lead the perceptive alternative closer to the best

possible point, the feature mapped closer to the top-right point has higher priorities of

improvement. Thus, features in the grey area are identified as opportunities, and features

in the white area are identified as satiated needs.

4. Empirical case study

In this section, we evaluated the passenger service of Taoyuan International Airport in

Taiwan, by using the model mentioned in Section 3. In terms of establishing the hierarchy

of criteria or features for evaluating airport passenger services, we collected in-depth

qualitative data from executives in the Civil Aeronautics Administration of Taiwan,

asking them to suggest attributes and criteria that reflected our conceptual variables for

evaluating airport passenger services. Based on the responses of the executives and experi-

enced airline passengers, a review of existing airport passenger service offerings and a

review of academic and practitioner literature, we designed a framework based on the cus-

tomer evaluation elements (physical environment and interaction and outcome) ofservice quality provided by Zeithaml and Bitner (2003). This framework was then pre-

sented to the five executives in the Civil Aeronautics Administration and two professors

of Business Administration, all of whom were blind to the purpose of the study, who

were then asked to verify our classifications. Table 1 lists the final set of attributes and

their classifications mapped onto various conceptual variables of airport passenger

services.

In this case study, we chose Taoyuan International Airport as the analytical case.

Taoyuan International Airport, the largest international airport of Taiwan, is located in

the north of Taiwan. We randomly found a total of 226 respondents in Taoyuan Inter-

national Airport to fill out both AHP and VIKOR questionnaires during May and Juneof 2008. During the data collection phase of passenger preferences, each respondent

was asked to respond to 15 pair-wise comparison questions of the AHP questionnaire.

All related values can be determined by using a scale of 19, representing a range of

equal importance to extreme importance. The geometric mean was used to aggregate

the relative importance data of respondents. After eliminating incomplete questionnaires,

our total sample size was 204 (52.9% male and 47.1% female). We also applied the con-

sistency test and eliminated data according to the consistency ratio (threshold value is

Table 1. List of elements, attributes and criteria of airport passenger services.

Elements Attributes Criteria/features

Physical environment Airport facilities planning Sanitary condition of lavatory (c1)Environment beauty and cleanliness (c2)Facilities allocation and space design (c3)

Airport circulationplanning

Internal direction line arrangement (c4)Exterior surrounding circulation planning

(c5)Convenience of public transportation (c6)

Interaction andoutcome

Basically proceduralservice

Airport receptionists attitude (c7)Security inspection procedure (c8)Check-in and baggage delivery service (c9)

Flight information service On-time departure of flights (c10)Clarity of broadcasting system (c11)Accuracy of flight information board (c12)



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CR 0.1). After screening for consistency, our total valid sample size was 186, and the

average consistency ratio of the total valid sample size was approximately 0.0001.

For the data collection phase of passenger satisfactions, each respondent was also

asked to respond to 12 questions of the satisfaction questionnaire. A likert five-point

scale with a range of 15, representing a range of very dissatisfactory to very satisfac-

tory, was used to evaluate passenger satisfaction levels of each criterion. The arithmetic

mean was used to aggregate the performance data of respondent satisfactions.

4.1. Assessment of airport passenger preferences

In the assessment of passenger preferences, we obtained the weights of attributes and cri-

teria through the AHP method, shown in Table 2. A software, Super Decisions 1.6.0

(Saaty, 2003), was applied to aid all calculations of AHP. Overall, air passengers empha-

sised interaction and outcome (52.06%) of airport services slightly over physical

environment (47.94%). In reviewing the four attributes of airport passenger service, pas-

sengers focused on flight information services (29.94%), followed by airport circulationplanning (28.49%). In reviewing the 12 criteria, on-time departure of flights (11.85%),

internal direction line arrangement (10.27%) and accuracy of flight information board

(10.06%) were the three most important criteria for air passengers.

4.2. Measurement of the customer gaps

In the case of airport passenger service, we set the value of the best possible point fi (the

best performance of customer satisfaction) as the scale of very satisfactory of each

criterion; the value of the worst possible point fi (the worst performance of customer

satisfaction) as the scale of very dissatisfactory of each criterion of each criterion.

Hence, fi = 5; f

i = 1, i= 1,2,. . .,12. The evaluation results of Taoyuan International

Airport by VIKOR are presented in Table 3. Overall, the averaged perceived performance

values (fi) of all criteria were considered to be satisfactory (the value with more than 3).

Table 2. The AHP weights/importance.

Elements/attributes/criteria Weight (%) Ranking

Physical environment 47.94Airport facilities planning 19.45 (IV)

Sanitary condition of lavatory (c1) 9.67 (4)

Environment beauty and cleanliness (c2) 4.88 (12)Facilities allocation and space design (c3) 4.90 (11)

Airport circulation planning 28.49 (II)Internal direction line arrangement (c4) 10.27 (2)Exterior surrounding circulation planning (c5) 8.73 (6)Convenience of public transportation (c6) 9.49 (5)

Interaction and outcome 52.06Basically procedural service 22.12 (III)

Airport receptionists attitude (c7) 7.43 (9)Security inspection procedure (c8) 6.36 (10)Check-in and baggage delivery service (c9) 8.33 (7)

Flight information service 29.94 (I)

On-time departure of flights (c10) 11.85 (1)Clarity of broadcasting system (c11) 8.03 (8)Accuracy of flight information board (c12) 10.06 (3)



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Then, exterior surrounding circulation planning (c5) and convenience of public

transportation (c6) were the features with the lowest passenger satisfaction; airport

receptionists attitude (c7) was the feature with the highest passenger satisfaction.

By using the VIKOR method, we can map the relative distance between the perceived

alternative and the best possible point. Following Figure 3 as a model, we first mapped the

two-element results. The 12 criteria can be simplified to two elements; one is interaction

and outcome and the other is physical environment. Thus, we calculated the average

passenger satisfaction of the six physical environment criteria and the average passenger

satisfaction of the six interaction and outcome criteria, and then mapped these values on

a two-element sketch map in Figure 5. The x-axis indicates values of satisfactions of

physical environment; the y-axis indicates values of satisfactions of interaction and

outcome. The dotted line in Figure 5 represents the two-element aggregate customer

gap of airport passenger service of Taoyuan International Airport to the best possible

point. The two-element (physical environment and interaction and outcome) result

was computed by the VIKOR method; the measure C, representing degree of majority

utility, is 0.2118; the measure D, representing degree of regret, is 0.2450; and themeasure G (v = 0.5), representing the aggregate distance, is 0.2284.

Similarly, the VIKOR method can help us to compute the relative distance between the

perceived alternative and the best possible point in our 12-criterion problem. More accu-

rate than the two-element results, the 12-criterion result from the VIKOR method is shown

in Table 3. If we rank the unweighted unimproved gaps of the 12 criteria/features, the

highest is exterior surrounding circulation planning (c5). It is obvious that the exterior

surrounding circulation of Taoyuan International Airport may not conform to public sat-

isfactions and is in greatest need of improvement. If we rank the weighted unimproved

gaps of the 12 criteria/features, on-time departure (c10) is the feature with the largest

weighted unimproved distance. The reasons the weighted unimproved gap for on-time

Figure 5. Illustration of the customer gap of the airports.Notes: The dotted line represents the two-element aggregate gap to the ideal point. The x-axisindicates values of satisfactions of physical environment; they-axis indicates values of satisfactionsof interaction and outcome.



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Table 3. The 12-criterion VIKOR results.

Criteria/features (ci) c1 c2 c3 c4 c5 c6 c7 c8 c9

Importance weights (wi) 0.097(4)a

0.049(12)

0.049(11)

0.103(2)

0.087(6)

0.095(5)

0.074(9)

0.064(10)

0.083(7)

Negative performance values (fi ) 1 1 1 1 1 1 1 1 1Perceived performance values (fi) 4.198

(6)b4.000

(9)3.997(10)

4.133(7)

3.855(12)

3.937(11)

4.485(1)

4.243(4)

4.297(3)

Aspired performance values (fi ) 5 5 5 5 5 5 5 5 5Unweighted unimproved distances/

unweighted gaps

((f

i fi)/(f

i f

i ))

0.200(7)c

0.250(4)

0.251(3)

0.217(6)

0.286d

(1)0.266

(2)0.129(12)

0.189(9)

0.176(10)

Weighted unimproved distances/weighted gaps((wi(f

i fi)/(f

i f

i )))

0.019(5)c

0.012(10)

0.012(9)

0.022(4)

0.025(3)

0.025(2)

0.010(12)

0.012(11)

0.015(8)

Aggregate gap (G(v= 0)) Aggregate gap (G(v= 0.5)) Aggregate gap (G(v= 1))

aThe ranking of importance values.bThe ranking of performance values.cThe ranking of gaps.dThe value that is also the value of measure D (degree of regret).eThe value that is also the value of measure C(degree of majority utility).f

The values of measure G, calculated by Equation (4), in different v values.


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departure (c10) are high may reside in the requirement of the highest level of on-time

departure for air passengers. This finding indicates that airport managers might have

different decisions regarding improvement when they consider passenger preferences

and satisfaction simultaneously. Using Equations (2) and (3) of Section 3, we obtained

the values of measure C (degree of majority utility) and measure D (degree of regret),

which is also displayed in Table 3. In order to understand how the aggregate gap (G) is

affected by v (0 v 1), this study, respectively, adopts v= 0, v= 0.5 and v= 1 to

compare these index values (G) in Table 3. When the strategy of maximum group

utility is adopted and the individual regret ignored, v= 1 can be selected for the calcu-

lation, whereas when the individual regret is considered and the strategy of maximum

group utility ignored, v= 0 can be selected. Generally speaking, if airport managers

decide to simultaneously address the strategy of maximum group utility and minimum

individual regret, then v= 0.5 should be selected. This selection is decided based on the

preferences of the airport managers.

4.3. Gap reduction analysis

Subsequently, we mapped the values of unimproved distance (x-axis) and importance (y-

axis) of Taoyuan International Airport on the two-axis improvement strategy map, shown

as Figure 6. The mean (0.0833) of the important values of the 12 criteria was used to split

between high importance and low importance; the top scale (0.1666) was twice as many as

the mean. The mean (0.2131) of the 12 unweighted relative distances was used to split

between large unimproved distance and small unimproved distance; the top scale

(0.4262) was twice as many as the mean. Since improving a feature with higher

importance and larger unimproved distance should have more opportunities to lead the

perceptive alternative closer to the best possible point, features in the grey area ofFigure 6 are identified as opportunities and features in the white area are satiated needs.

Figure 6 illustrates the importance unimproved distance map of Taoyuan Inter-

national Airport. In Figure 6, several findings are apparent, as follows: (1) the four

features, internal direction line arrangement (c4), external surrounding circulation

Figure 6. The importanceunimproved distance map of Taoyuan International Airport.

Notes: c1= sanitary condition; c2= environment beauty; c3= facilities allocation; c4= directionline arrangement; c5= surrounding circulation planning; c6= public transportation convenience;c7= receptionist attitude; c8= security inspection; c9= check-in and baggage delivery;c10= on-timedeparture; c11= broadcasting system and c12= flight information board.



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planning (c5), convenience of public transportation (c6) and on-time departure of flights

(c10), are mapped in Quadrant I. Thus, these four features should be regarded as improve-

ment items with the highest priority because they have more potential to rapidly reduce the

customer gap. (2) Environment beauty and cleanliness (c2) and facilities allocation and

space design (c3) are mapped in Quadrant IV, so these two features are regarded as lower

priority improvement items because they contained large rooms for improvement while

being relatively unimportant to air passengers. (3) In Quadrant II, sanitary condition of

lavatory (c1) and accuracy of flight information board (c12) are two important features

for passengers to evaluate airport service. Although the two features are categorised to

those with relative smaller unimproved distances, managers may adopt the strategy of

finding any possible methods of improving these to reduce gaps. (4) The remaining

features (c7, c8, c9 and c11) mapped in Quadrant III with relative lower importance and

smaller unimproved distances are those with satiated passenger needs; airport managers

could employ the strategy of maintaining these features.

Consequently, airport managers can arrange appropriate strategies according to

passenger preferences and unimproved gaps of present services in order to reduce thecustomer gap and to achieve passenger expectations. Therefore, this research proposes

the multi-criteria evaluation model as a suitable and effective method for evaluating

and reducing the gaps of the improvement of airport passenger services.

5. Conclusions

This paper employed the AHP and VIKOR methods to overcome the shortages of the tra-

ditional IPA. This paper also used an empirical sample of Taoyuan International Airport

to evaluate airport passenger services by the multi-criteria evaluation model combined

three methods: AHP, VIKOR and IPA. The application of both the AHP and the VIKORmethods to the empirical data was employed to analyse passenger preferences and passenger

satisfactions and also to illustrate the customer gap of airport passenger services. The IPA

technique was then applied to diagnose managerial strategies for reducing the customer

gap between passenger perceptions and expectations. The improvement strategy map of

gap reduction was conducted to categorise various features of airport passenger services.

The major contribution of this paper lies in the development of an integrated model,

which incorporates diversified issues for evaluating airport passenger services. Through

this evaluation model, airport managers can decide which features should be further

improved in order to achieve air passengers aspired levels. Our empirical case study

demonstrated the effectiveness and feasibility of the proposed model. This proposed

model successfully integrates AHP, VIKOR and IPA methods; it can simultaneously

deal with air passenger preferences and satisfactions and help the airport managers to

confidently create improvement strategies. In addition, this model can be used to solve

different kinds of problems by modifying the constructs of the hierarchy trees and

finding the appropriate improvement strategy. However, since the empirical results

were from the analysis in the airport of Taiwan and culture is a significant influence

in marketing management, the results might not be generalised broadly. For further

research, one could consider the relations among criteria or features of service quality.

Acknowledgement

The authors would like to thank the National Science Council of Taiwan for financially

supporting this research under Grant NSC97-2410-H-008-029.



17/18

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