Evaluating the competitiveness of the aerotropolises in East Asia

Journal of Air Transport Management 32 (2013) 24e31

Contents lists available at SciVerse ScienceDirect

Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman

Evaluating the competitiveness of the aerotropolises in East Asia Gi-Tae Yeo a, *, Ying Wang a,1, Chien-Chang Chou b, 2 a b

Graduate School of Logistics, Incheon National University, Incheon 406-772, South Korea Department of Shipping Technology, National Kaohsiung Marine University, Kaohsiung 80543, Taiwan

a b s t r a c t Keywords: Competitiveness of an aerotropolis (COA) East Asia Fuzzy MCDM Sensitivity analysis

In the twenty-first century, the development of the aerotropolis is recognized as one of the major contributors to the local and national economy in terms of stimulating new investment, fostering employment and creating new business opportunities. It is important to acknowledge the competitiveness of an aerotropolis (COA) which clearly suggest the advantages and disadvantages of such a development. An evaluation of COA criteria, regarded as a multiple criteria decision making (MCDM) problem, cannot easily quantify the weight of the criteria and rating of each alternative owing to the uncertainties and imprecision in the real world. In this respect, this paper attempts to evaluate the COAs in East Asia, where air transport is sharply increasing, using an integrated fuzzy MCDM methodology. To assess the changes in rank of COAs, a sensitivity analysis is adopted.
2013 Elsevier Ltd. All rights reserved.

1. Introduction In the past, airports generated revenue mainly from aviation, specifically the transfer of passengers and cargo. However, the globalization and liberalization trends in the air market have fostered competition among hub airports. To maintain competitiveness and create more revenue, non-aviation revenue is becoming airports’ mainstay (Kratzsch and Sieg, 2011). The development of an airport business that involves its local community stimulates the emergence of an aerotropolis. The creation of an aerotropolis can increase air transport demand and develop the airport’s surrounding industries. This is becoming a tendency in airport development strategy to stimulate new investment, foster employment, and create new business opportunities (Stenvert and Penfold, 2007). As examples of international business, airportrelated companies prefer to locate their business in the optimal aerotropolis because this provides the best competitiveness for their business. In this respect, it is important for airport-related stakeholders to clearly recognize the advantages and disadvantages of their specific aerotropolis through the process of evaluating the COA. However, clear empirical studies on this issue are lacking. This paper uses the well-established quantitative analysis

* Corresponding author. Tel.: þ82 32 8358196; fax: þ82 32 8350703. E-mail addresses: [email protected] (G.-T. Yeo), [email protected] (Y. Wang), [email protected] (C.-C. Chou). 1 Tel.: þ82 32 8354590; fax: þ82 32 8350703. 2 Tel.: þ88 67 8100888 5106; fax: þ88 67 5714350. 0969-6997/$ e see front matter
2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jairtraman.2013.06.004

known as the integrated fuzzy MCDM methodology in an effort to evaluate the competitiveness of several aerotropolises in East Asia. A number of implications for aerotropolises can be established in the following ways: (1) by representing aerotropolis competitiveness indexes, (2) by evaluating a real case of the COA in East Asia, (3) by undertaking a sensitivity analysis in order to find out the changes in the rankings of COAs. This study provides fresh insights to stakeholders in the airport industry. 2. Literature review An aerotropolis can be developed based on a hub airport, and the hub airport and the bigger aerotropolis have reciprocal benefits with each other. The convenient service and speedy market connectivity that are provided by the airport will add value to the aerotropolis’s businesses and industries, and in turn, the more businesses and industries clustered around the airport, the greater will be the additional passengers and cargoes generated (Kasarda, 2006). In terms of the competitiveness of a hub airport, various influential criteria have been considered, such as geographic location (Jayalath and Bandara, 2001; Lirn, 2010), airport surface access quality (Janic and Reggiani, 2002; Kim and Park, 2012; Liou et al., 2011; Keumi and Murakami, 2012), airport charges (Berechman and Wit, 1996; Gardiner et al., 2005), the airline service network (Barros et al., 2007; Chou et al., 2011; Sasaki et al., 1999), infrastructure (Ohashi et al., 2005; Yuen, 2008), and so on. Regarding the aerotropolis, the airport’s surrounding industries should be taken into account. Menou et al. (2010) considered that the competitiveness that aerotropolises supply to industries is

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

influenced by both qualitative and quantitative criteria: (1) the cost and availability of utilities, (2) the air, rail, highway, and waterway systems, (3) the proximity to suppliers and markets for rapid access (time-based competition), perishable products, and transport costs, (4) the size of the markets, (5) the cost of land (construction or rental) and operations, and (6) the distances (or costs) between sites. Industrial diversification such as shopping centers and hotels, leisure and tourism facilities, retail, and the logistics industry (Pagliari, 2005; Rendeiro and Cejas, 2006; Wang and Hong, 2011) can increase the functions of the aerotropolis and create additional revenue. In the meantime, information technology (IT) strategies can help to improve performance and increase sales for businesses and ensure the high quality and efficiency of services (Dirk, 2009; Nucciarelli and Gastaldi, 2009). Wang et al. (2011) introduced the following competitiveness criteria for the aerotropolis: industrial diversification, aggressive construction, trade liberation, rationalization of regulations, environmental convenience, operation globalization, and business management. Skouloudis et al. (2012) found that the indicators should include economic (economic indicators), environmental (environmental indicators), and social factors (labor practices and decent work, human rights, society, and product responsibility). Stevens et al. (2010) produced a model of the factors integral to the competitiveness evaluation of aerotropolises, specifying economic development, land use, infrastructure, and governance. Futuremore, Baker and Freestone (2010) indicated that land development and cost are critical problems faced by stakeholders. Cooperative land use planning would assist airport master planning to interconnect with broader local, city, regional, and national plans (Freestone et al., 2011; Walker and Baker, 2010; Walker and Stevens, 2008). The above-mentioned criteria influencing the COA are summarized in Table 1. Based on these COA criteria, a questionnaire was designed by factor analysis, as shown in Table 4.

25

3. Methodology In the literature review, studies regarding the COA were thoroughly reviewed and every significant factor was identified. However, it can be concluded that quantitative methodologies have certain limitations in solving dynamic and complicated problems such as the COA. The fuzzy evaluation method is regarded as a particularly suitable methodology for dealing with data with the characteristics of uncertainty, ambiguity, non-observability, and scarcity. The other difficulties observed in the review, i.e., extracting influential factors and formulating meaningful strategies, can also be solved within the boundaries of fuzzy evaluation. 3.1. An integrated fuzzy MCDM methodology In general, determining the COA on the basis of two or more criteria is an MCDM problem. In many situations, the values of the qualitative criteria are often imprecisely defined by the decision makers (Chou, 2010). Since there are many uncertainties, vaguenesses, and imprecisions in the real world when dealing with decisions of multiple criteria, it is not easy to quantify the weight of the criteria and rating of each alternative (Lai et al., 2010). To deal with such problems, fuzzy set theory, which uses natural language that can express the experts’ thinking and preferences, should be employed. An integrated fuzzy MCDM methodology that consists of factor analysis, a fuzzy analytic hierarchical process (fuzzy AHP), and the technique for order of preference by similarity to ideal solution (TOPSIS) are established to support the analytical framework. 3.2. Fuzzy AHP The fuzzy AHP method is employed to determine the relative importance of the criteria. An integrated fuzzy set theory and AHP,

Table 1 The definition of criteria that influence the COA. Criteria

Definition

Authors (year)

Airport access modes

Multi-modal transportation such as trains, shuttle buses, taxis, and trams around the airport to accelerate the inter-modal transfer of goods and people. Availability of space in the transfer area to increase flight selection and air routes and facilitate a transfer hub. Global information and communication technology networks should be shaped around the aerotropolis so that users receive information in time. Developing/expanding the scale of the free trade zone and providing a highquality international free trading environment. Whether the aerotropolis has a superior geographic location to attract passengers, cargo and companies. Whether a large hinterland is located near the airport to expand the scope of the aerotropolis and the cost of renting the land. The level of internationalization of the airport required for a successful aerotropolis. Whether experts are present who have ability and experience related to aerotropolis operation and management. Shopping centers and hotels around the airport providing people with rest and shopping destinations. Whether retail venues are located in the aerotropolis to create nonaeronautical revenue. A combination of local tourism features and excellent local resources near or in the aerotropolis. Public facilities for a comfortable resident function (e.g., medical resources, education, transportation, etc.) and commercial business areas. Relevant tax incentives for the industry park setting, land rent, tariffs, and other measures to encourage industrial clusters.

Janic and Reggiani (2002), Liou et al. (2011), Kim and Park (2012), and Keumi and Murakami (2012) Sasaki et al. (1999), Barros et al. (2007), and Chou et al. (2011) Dirk (2009) and Nucciarelli and Gastaldi (2009) Wang et al. (2011) and Skouloudis et al. (2012)

Flight hub and transfer Network strategies and application of IT Free trade zone Geographic location Land use and cost International environment Human resources and consultation Shopping centers and hotels Retail industry Leisure and tourism Residential and business areas Government’s support and management Loose regulation policy Environmental sustainability

The creation of relevant laws and regulations to improve the effective development of the aerotropolis. Reduction of airplane noise and industry pollution, an increase in forestation.

Jayalath and Bandara (2001) and Lirn (2010) Menou et al. (2010), Stevens et al. (2010), and Baker and Freestone (2010) Stevens et al. (2010) and Wang et al. (2011) Gardiner et al. (2005) and Skouloudis et al. (2012) Pagliari (2005), Rendeiro and Cejas (2006), and Wang and Hong (2011) Pagliari (2005), Rendeiro and Cejas (2006), and Wang and Hong (2011) Pagliari (2005), Rendeiro and Cejas (2006), and Wang and Hong (2011) Wang et al. (2011) and Skouloudis et al. (2012) Walker and Stevens (2008), Stevens et al. (2010), Walker and Baker (2010), and Freestone et al. (2011) Walker and Stevens (2008), Walker and Baker (2010), and Wang et al. (2011) Stevens et al. (2010), Wang et al. (2011), and Skouloudis et al. (2012)

26

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

the so-called fuzzy AHP method, has been widely used to solve the problem of MCDM (Lo and Tzeng, 2011). The method overcomes the subjectivity of decision makers, and linguistic variables truly reflect the judgments of experts on the weights of criteria (Bozbura and Beskese, 2007). The outlines of calculation procedure by fuzzy AHP can be summarized as follows:

Table 3 Linguistic variables for the preference of each alternative.

Step 1. Construct pair-wise comparison matrices among all the criteria in the dimensions of the hierarchy system. Assign linguistic terms to the pair-wise comparisons by asking which is the more important of each of two criteria, such as

2

1 6~ 21 ~ ¼ 6a A 4 « ~n1 a

~12 a 1 « ~n2 a

3 2 ~1n ~12 / a 1 a 6 1=a ~2n 7 ~ / a 1 12 7 ¼ 6 4 « 1 « 5 « ~1n 1=a ~2n / 1 1=a

3 ~1n / a ~2n 7 / a 7 1 « 5 / 1 (1)

where

8~ ~ ~ ~ ~ < 1; 3; 5; 7; 9; ~ij ¼ 1; a : 1 1 1 1 1 ~ ~ ;5 ~ ; ~ ;7 ;9 1 ;3

ij i ¼ j ij

Step 2. Use the geometric mean technique to define the fuzzy geometric mean and fuzzy weights of each criterion by Buckley (1985) as follows:

~i1 *a ~i2 */*a ~in Þ1=n ; ~r i ¼ ða

~ i ¼ ~r i *ð~r 1 þ ~r 2 þ / þ ~r n Þ1 w

(2)

~in is the fuzzy comparison value of criterion i to criterion n, where a thus, ~ri is the geometric mean of fuzzy comparison value of crite~i is the fuzzy weight of the ith criterion, rion i to each criterion, a which can be indicated by a triangular fuzzy number (TFN), ~1 ; a ~2 ; a ~3 Þ. Here a ~1 ; a ~2 and a ~3 stand for the lower, middle ~ i ¼ ða a and upper values of the fuzzy weight of the ith criterion. In this paper, a fuzzy-level linguistic term defined by Mon et al. (1994) is employed to determine the weights of each criterion, as shown in Table 2. Furthermore, linguistic variables are used as a way to measure the performance value of the alternative for each criterion as shown in Table 3. The result of the fuzzy synthetic decision reached by each criterion and alternative is a fuzzy number. Therefore, it is necessary to employ a non-fuzzy ranking method for fuzzy numbers for the comparison of each criterion and alternative. The procedure of defuzzification is to locate the Best Non-fuzzy Performance value ~i ¼ (BNP) (Hsieh et al., 2004). The BNP value of the fuzzy number a ~2 ; a ~3 Þ can be calculated by the equation below: ~1 ; a ða

BNPi ¼ ½ða3  a1 Þ þ ða2  a1 Þ=3 þ a1

(3)

Triangular fuzzy scale

Triangular fuzzy reciprocal scale

Equally important (EI) Weakly important (WI) Strongly important (SI) Very strongly important (VSI) Absolutely important (AI)

(1, (1, (3, (5, (7,

(1/3, (1/5, (1/7, (1/9, (1/9,

1, 3, 5, 7, 9,

3) 5) 7) 9) 9)

1, 1) 1/3, 1) 1/5, 1/3) 1/7, 1/5) 1/9, 1/7)

(0, (1, (3, (5, (7, (9,

1, 3) 3, 5) 5, 7) 7, 9) 9, 10) 10, 10)

To help find the optimal solution that has the shortest distance from the positive ideal solution and the farthest from the negative ideal solution (Wang and Elhag, 2006), fuzzy TOPSIS is applied. Instead of crisp values, fuzzy TOPSIS uses linguistic terms in the evaluation process to deduce the information loss by decisionmakers (Buyukozkan and Cifci, 2012). The step of fuzzy TOPSIS, which was introduced by Onut and Soner (2008), is summarized as below. Step 1. Choose the linguistic values ð~ xij ; i ¼ 1; 2; .; n; j ¼ 1; 2; .; mÞ for alternatives concerning the criteria. The fuzzy linguistic rating ð~ xij Þ keeps the ranges of normalized triangular fuzzy numbers that belong to [0, 1], hence, there is no need for normalization. Step 2. by

Compute the weighted normalized fuzzy-decision matrix

  ~v ¼ ~vij n*j ;

i ¼ 1; 2; .; n; j ¼ 1; 2; .; m

~vij ¼ ~ xij *wi

(4) (5)

Step 3. Determine the positive-ideal (FPIS, A*) and negative-ideal (FNIS, A) solutions from the equations below:

A* ¼

o n     maxj vij ; i˛Ub ; maxj vij ; i˛Uc v*1 ; .; v*i ¼ (6)

A ¼

n

 v 1 ; .; vi

o

¼



   minj vij ; i˛Ub ; maxj vij ; i˛Uc (7)

Ub are the sets of benefit criteria and Uc are the sets of cost criteria. Step 4. Calculate the distances of each alternative from the ideal solution and the negative-ideal solution:

D*i ¼

m  X ~ ;V ~ ; d V ij i

i ¼ 1; 2; .; n

(8)

j ¼ 1; 2; .; m

(9)

j¼1

D i ¼

Linguistic scale

Fuzzy score

Poor (P) Medium poor (MP) Fair (F) Medium good (MG) Good (G) Very good (VG)

3.3. Fuzzy TOPSIS

According to the above equations, the value of each criterion and alternative for center of area can be obtained.

Table 2 Linguistic variables for the importance weight for each criterion.

Linguistic scale

m  X ~ ;V ~ ; d V ij i j¼1

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i  h ~ ¼ ~; b d a ð1=3Þ ða1  b1 Þ2 þ ða2  b2 Þ2 þ ða3  b3 Þ2 (10) ~ are two triangular fuzzy numbers, which is shown by the ~ and b a triplet (a1,a2,a3) and (b1,b2,b3).

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

Step 5. Determine the relative closeness of each alternative to the ideal solution. The relative closeness of the alternative Ai in relation to A* is characterized as below:

 *  FCi ¼ D i = Di þ Di ;

i ¼ 1; 2; .; n

(11)

4. A case study In this section, the proposed integrated fuzzy MCDM approach is illustrated using the COA evaluation problem faced by the aerotropolises in East Asia. Based on the research of Kasarda (2011), there are five aerotropolis in East Asia: Beijing, Hong Kong, Incheon, Shanghai, and Taoyuan. The geographical locations of these aerotropolises are shown in Fig. 1. 4.1. Survey data A questionnaire was designed based on the previous literature. Fifteen criteria were employed in this questionnaire to select the proper criteria, build the taxonomy, and examine the reliability and validity of these criteria using the factor analysis method. Factor analysis is a method of summarizing the information from data collected through a quantitative method (Child, 2006; Liou and Tzeng, 2007). A list of 150 members of the Korea International Logistics Association (KILA) was used for the mailing list. These individuals are engaged in aerotropolis-related work, e.g., they are aerotropolis managers, operators, and aviation and logistics servicers. The questionnaires were sent to each company from June 21 to September 30, 2012. A follow-up request was sent two weeks after the initial mailing. To confirm the reliability of the questionnaire, only respondents who had at least five years of experience in this area were considered. A totally of 60 usable replies were received. Five-point Likert scales from 1 ¼ very unimportant to 5 ¼ very important were used in the questionnaire to obtain information on COA criteria. Using the collected data, a number of statistical procedures are carried out by using SPSS version 18.0. To find the underlying

27

constructs associated with 15 criteria, principal component analysis with a varimax rotation is utilized and four common criteria (basic infrastructure, convenient operation, diversified industry, green management) are obtained, as shown in Table 4. The validity of the data is tested by using the KaisereMeyereOlkin measure of sampling. According to Hair et al. (1987), a value of 0.840 is suitable and indicates that the number of the criteria and the sample size are appropriate. To test the reliability of the criteria, Cronbach’s alphas are calculated. The value (0.823, 0.780, 0.808, and 0.680) indicates satisfactory internal consistency reliability (Yeo et al., 2008). 4.2. The weights calculation of the evaluation criteria The weights of the COA criteria and the importance of the five alternative aerotropolises were respectively obtained from decision makers’ responses according to the linguistic variables given in Tables 2 and 3. To calculate the weight of the COA, five in-depth interviews were carried out with senior airport executives, who had knowledge in this area, careers that had spanned over 20 years, and clearly knew about these five alternative aerotropolises. All five interviewees were asked to indicate their perception and knowledge of aerotropolises according to the procedures of the research methodology. According to the interviews with these experts, the pair-wise comparison matrices of criteria can be obtained by doing the following: (1) By applying the fuzzy numbers defined in Table 2, the pair-wise comparison matrices of criteria of the linguistic scales can be transferred to the corresponding fuzzy numbers. (2) By computing the criteria of pair-wise comparison matrix using the geometric mean method, the final matrices can be constructed. (3) By using Equation (2) to obtain the fuzzy weights of the criteria, as shown here:

~r 1 ¼ ð1:294; 1:749; 2:736Þ ~r 3 ¼ ð0:579; 0:881; 1:339Þ

Fig. 1. The geographical locations of five aerotropolises in East Asia.

~r 2 ¼ ð0:776; 1:316; 1:726Þ ~r 4 ¼ ð0:375; 0:493; 0:725Þ:

28

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

Table 4 Factor analysis results after varimax rotation. Criteria

Sub-criteria

Basic infrastructure (C1)

Common factors

Airport access modes Flight and transfer hub Network strategies and application of IT Free trade zone Geographic location Land use and cost International environment Human resources and consultation Shopping centers and hotels Retailing industry Leisure and tourism Residential and business areas Government’s support and management Loose regulation policy Environmental sustainability

Convenient operation (C2)

Diversified industry (C3)

Green management (C4)

KaisereMeyereOlkin Cronbach’s alpha Accumulated variance

1

2

3

4

0.793 0.757 0.684 0.669 0.231 0.146 0.169 0.436 0.146 0.325 0.235 0.162 0.159 0.168 0.248 0.840 0.823 24.35

0.044 0.282 0.004 0.126 0.784 0.744 0.673 0.668 0.037 0.019 0.189 0.209 0.089 0.198 0.075

0.184 0.212 0.271 0.181 0.186 0.011 0.103 0.287 0.854 0.814 0.784 0.698 0.071 0.094 0.017

0.013 0.085 0.350 0.288 0.003 0.058 0.257 0.035 0.104 0.079 0.285 0.323 0.869 0.814 0.765

0.780 43.69

0.808 59.52

0.680 76.95

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

The weight of each criterion can be obtained as follows:

~ 1 ¼ ð0:198; 0:394; 0:905Þ w ~ 3 ¼ ð0:089; 0:198; 0:443Þ w

~ 2 ¼ ð0:119; 0:296; 0:571Þ w ~ 4 ¼ ð0:057; 0:111; 0:240Þ w

(4) To take the BNP value of the weight of the criteria, Equation (3) is used; for example, for C1:

BNPw1 ¼ ½ðcw1  aw1 Þ þ ðbw1  aw1 Þ=3 þ aw1 ¼ ½ð0:905  0:198Þ þ ð0:394  0:198Þ=3 þ 0:198 ¼ 0:49 The total fuzzy AHP results are shown in Table 5. Under the four main criteria, the most two important items are “basic infrastructure” (0.499) and “convenience operation” (0.329), whereas the least important is “green management” (0.136). These results indicate that when evaluating the COA, “basic infrastructure” including developed multi-modal transportation, diverse flight selection, etc. is the first criterion worth considering because a good

transport network is necessary to move the customers’ products to the required destination around the world in a timely fashion. As for the four sub-criteria in the main criterion “basic infrastructure”, “flight and transfer hub” (0.297) and “airport access modes” (0.194) are the most important. This may reflect that the time and agility are the most important key factors. A flight hub is the foundation stone for aerotropolis development; it includes the terminal routes, living functions around the airport, transportation networks, good facilities, and service at the airport. Convenient facilities can support and help airport-related companies to operate their business more flexibly. Multi-modal forms of transportation such as trains, shuttle buses, taxis and trams around the airport can accelerate the inter-modal transfer ability of goods and people. Dedicated airport expressway links and airport express trains will also connect the airport to major regional business and residential areas efficiently. “Geographic location” (0.227) and “land use and cost” (0.187) are the two most important criteria under the main criterion “convenience operation”. The approachability of the airport’s geographical location can shorten airway distance to the other

Table 5 Weights of evaluation criteria from decision makers. Criteria

Local weights

Overall weights

BNP

Basic infrastructure (C1) Flight and transfer hub (C11) Airport access modes (C12) Network strategies and application of IT (C13) Free trade zone (C14)

(0.198,0.394,0.905) (0.124,0.314,0.822) (0.081,0.210,0.535) (0.362,0.612,1.034) (0.117,0.258,0.601)

(0.025,0.124,0.744) (0.016,0.083,0.484) (0.021,0.068,0.248) (0.014,0.077,0.343)

0.499 0.297 0.194 0.112 0.144

Ranking 1 3 9 6

Convenient operation (C2) Geographic location (C21) Land use and cost (C22) International environment (C23) Human resources and consultation (C24)

(0.119,0.296,0.571) (0.167,0.424,0.938) (0.076,0.203,0.516 (0.045,0.106,0.313) (0.072,0.155,0.371)

(0.020,0.126,0.535) (0.015,0.080,0.466) (0.009,0.042,0.284) (0.009,0.046,0.212)

0.329 0.227 0.187 0.111 0.089

2 4 10 12

Diversified industry (C3) Shopping centers and hotels (C31) Retailing industry (C32) Leisure and tourism (C33) Residential and business areas (C34)

(0.089,0.198,0.443) (0.230,0.388,0.655) (0.150,0.311,0.764) (0.069,0.154,0.309) (0.078,0.163,0.397)

(0.013,0.043,0.157) (0.013,0.062,0.338) (0.006,0.031,0.137) (0.009,0.048,0.227)

0.243 0.071 0.138 0.058 0.095

14 7 15 11

Green management (C4) Government’s support and management (C41) Loose regulation policy (C42) Environmental sustainability (C43)

(0.057,0.111,0.240) (0.056,0.167,0.427) (0.142,0.317,0.687) (0.099,0.218,0.421)

(0.011,0.066,0.386) (0.013,0.063,0.304) (0.009,0.043,0.186)

0.136 0.154 0.127 0.079

5 8 13

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

international airports and lead-time for logistics companies. Whether the logistics company located in the aerotropolis has enough land available for its operations is also important. The cost generally involves land rent cost, labor cost, transport cost, etc. The lower cost will lessen the monetary cost of the company and improve the competitiveness; normally, the cost element is the most sensitive factor for business companies (Zeng and Rossetti, 2003). The liberalization and privatization degree of hub airports in East Asia is not as high as in the West (Max, 2007), and in this region, the ownership of airports and the planning and management of airports is mainly dominated by the government. It can be taken as given that in market-based development, coordinating land-use decisions among owners is difficult, and, disputes are inevitable. As a powerful agency, government is well able to settle the disputes by negotiation with stakeholders (Kasarda, 2009). As for COA, “government’s support and management” (0.154) is also important. Before locating in an aerotropolis, guidance and policy help from the local government is crucial, whereas the less important subcriterion is “environmental sustainability”. Though green management is becoming a trend in airport development, business companies’ aim is to obtain maximized profit but rarely giving consideration to environmental problems. In the total ranking of 15 sub-criteria, “flight and transfer hub” (0.297) and “geographic location” (0.227), “airport access modes” (0.194) and “land use and cost” (0.187) are the most important criteria when evaluating the COA. In contrast, “leisure and tourism” (0.058), “shopping centers and hotels” (0.071) and “environmental sustainability” (0.079) are the least important sub-criteria. 4.3. Estimating the performance matrix In this section, the COA in East Asia will be evaluated; the performance matrix will be estimated based on the performance criteria. (1) Getting the average fuzzy numbers of alternatives under each evaluation criterion. Using the Beijing aerotropolis under the sub-criteria “flight and transfer hub” as an example, the average fuzzy performance values can be obtained in Equation (4) as follows:

" ~ x11 ¼

5 X

!, a1 x11

5;

i¼1

5 X

!, a2 x11

i¼1

5;

5 X

!, # a3 x11

5

i¼1

¼ ð4:6; 6:6; 8:2Þ In the same way, all the average fuzzy performance values can be obtained. (2) Obtaining the fuzzy performance value. Next, because the evaluation of alternatives is given under each sub-criteria, each average value of the alternatives under the subcriteria should be multiplied by the weights of each sub-criteria xij *wi . using Equation (5) as ~vij ¼ ~

~v11 ¼ ~ x11 *w1 ¼ ½ð4:6*0:025Þ; ð6:6*0:124Þ; ð8:2*0:744Þ ¼ ð0:113; 0:816; 6:009Þ Following the same process, all the fuzzy decision values of the alternatives can be obtained. To accurately determine the weights of the alternatives under each sub-criterion, BNP values are calculated by Equation (3) shown in Table 6.

29

The result shows that the Beijing aerotropolis is good at C12 e “airport access modes”. Being connected by aerolanes, aerotrains and ring roads, the flexibility of the transportation in an aerotropolis will bring greater convenience for operations of local businesses. On the other hand, the Beijing aerotropolis is weak in C24 e “human resources and consultation”. In recent years, China has been developing at a very fast pace, and to meet the needs of passengers and cargo shipments, hardware capacities such as basic infrastructure are under active construction. In contrast, such soft capacities as implementing human resources in planning aerotropolis development and consulting with industries that want to invest or be located in the aeroteopolis are deficient. The Hong Kong aerotropolis is good at almost all the requirements except that of C34 e “residential and business areas”. Hong Kong is a small island with limited usable land for the development of an aeroteopolis, especially in residential and business areas because of the vast land areas required for residential construction. The Incheon aerotropolis is performing great in the role of C11 e “flight and transfer hub”. According to the data, the cargo transfer rate for August 2012 in the Incheon aerotropolis is 45.7%, which means it can offer various flight selections for cargo transfer to improve the flexibility of the operation. However, as concerns C21 e “geographic location”, Incheon is weaker than Beijing because of the limited passenger resources. Actually, Shanghai fulfills the qualifications required of an aerotropolis. True, in most sub-criteria, Shanghai is insufficient. But it is good at C13 e “network strategies and application of IT”. In the twenty-first century, IT is important for logistics companies to share their information with customers to reach the aim of visibility. And at the same time, airline companies use the communications faculties of IT to transport the cargo more promptly and accurately. Taking advantage of C14 e “free trade zone”, the Taoyuan aerotropolis is famous in East Asia. The free trade zone in an aerotropolis not only brings non-aeronautical revenue but creates employment. In contrast, Taoyuan is not good at “government’s support and management”. The significance of aerotropolis construction is still not well known among governments.

4.4. Ranking the alternatives In this section, the total ranking of five alternative aerotropolises will be evaluated based on the performance matrix by the fuzzy TOPSIS method. The calculation process is shown below. (1) Finding the fuzzy positive and negative ideal solution (FPIS & FNIS) by Equations (6) and (7). Under each sub-criteria, finding the lowest and highest value of fuzzy numbers among the five alternatives marked A* and A. For example, since the maximum fuzzy value listed in the first column of Table 6 is (0.152,1.014,7.140), the FPIS for the criterion C11 is (0.152,1.014,7.140), and the FNIS for the same criterion takes the minimum fuzzy value listed in the first column of Table 6, which is (0.084,0.668,5.504). (2) Getting the distances of each alternative from the ideal solution D*i and the negative-ideal solution D i by using Equations (8)e (10) shown on the left in Table 7. (3) Calculating the relative closeness of each alternative to the ideal solution by Equation (11), shown on the left in Table 7. The value for the first alternative, Beijing aerotropolis, is calculated as follows:

 *  FCi ¼ D ¼ 2:292=ð3:106 þ 2:292Þ ¼ 0:425 i = Di þ Di

30

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

Table 6 Fuzzy decision value of alternative aerotropolises. Alternatives

C11

BNP

C12

BNP

C13

BNP

Beijing Hong Kong Incheon Shanghai Taoyuan

(0.113,0.816,6.099) (0.152,1.014,7.140) (0.133,0.891,6.396) (0.084,0.668,5.504) (0.093,0.717,5.801)

2.343 2.769 2.473 2.085 2.204

(0.106,0.711,4.744) (0.118,0.744,4.744) (0.099,0.678,4.647) (0.061,0.479,3.776) (0.074,0.546,4.066)

1.854 1.869 1.808 1.439 1.562

(0.041,0.274,2.382) (0.067,0.374,2.779) (0.056,0.333,2.665) (0.045,0.291,2.495) (0.041,0.274,2.382)

0.899 1.073 1.018 0.944 0.899

Alternatives

C14

BNP

C21

BNP

C22

BNP

Beijing Hong Kong Incheon Shanghai Taoyuan

(0.070,0.529,3.824) (0.106,0.705,4.571) (0.076,0.561,4.104) (0.082,0.577,4.104) (0.088,0.625,4.384)

1.474 1.794 1.580 1.588 1.699

(0.115,0.979,5.030) (0.163,1.206,5.351) (0.107,0.929,4.816) (0.083,0.779,4.281) (0.115,0.979,5.030)

2.042 2.240 1.951 1.714 2.042

(0.051,0.435,3.319) (0.065,0.514,3.551) (0.060,0.487,3.474) (0.065,0.514,3.551) (0.065,0.514,3.628)

1.269 1.377 1.341 1.377 1.402

Alternatives

C23

BNP

C24

BNP

C31

BNP

Beijing Hong Kong Incheon Shanghai Taoyuan

(0.081,0.597,3.224) (0.109,0.720,3.430) (0.092,0.643,3.293) (0.047,0.413,2.538) (0.053,0.444,2.675)

1.301 1.419 1.343 1.000 1.057

(0.029,0.249,1.567) (0.070,0.442,2.118) (0.056,0.396,2.075) (0.039,0.304,1.821) (0.049,0.359,1.991)

0.615 0.877 0.843 0.722 0.800

(0.061,0.415,2.221) (0.072,0.444,2.221) (0.065,0.425,2.221) (0.046,0.338,1.995) (0.057,0.396,2.176)

0.899 0.912 0.904 0.793 0.876

Alternatives

C32

BNP

C33

BNP

C34

BNP

Beijing Hong Kong Incheon Shanghai Taoyuan

(0.053,0.390,2.495) (0.113,0.630,3.043) (0.088,0.554,2.982) (0.058,0.416,2.617) (0.078,0.516,2.921)

0.979 1.262 1.208 1.030 1.172

(0.061,0.407,2.773) (0.088,0.518,3.246) (0.093,0.542,3.314) (0.061,0.407,2.840) (0.050,0.358,2.570)

1.080 1.284 1.316 1.103 0.993

(0.044,0.302,1.601) (0.033,0.251,1.452) (0.047,0.311,1.601) (0.047,0.320,1.676) (0.051,0.337,1.750)

0.649 0.579 0.653 0.681 0.713

Alternatives

C41

BNP

C42

BNP

C43

BNP

Beijing Hong Kong Incheon Shanghai Taoyuan

(0.023,0.178,1.040) (0.028,0.202,1.149) (0.026,0.190,1.122) (0.023,0.178,1.040) (0.023,0.178,1.067)

0.414 0.460 0.446 0.414 0.423

(0.096,0.449,2.132) (0.096,0.449,2.083) (0.112,0.490,2.132) (0.104,0.476,2.132) (0.087,0.422,1.934)

0.892 0.876 0.911 0.904 0.814

(0.055,0.267,1.287) (0.061,0.284,1.350) (0.055,0.267,1.287) (0.050,0.250,1.225) (0.055,0.267,1.287)

0.537 0.565 0.537 0.508 0.537

Table 7 Final computation results and the sensitivity analysis results. Alternatives

Beijing Hong Kong Incheon Shanghai Taoyuan

Final ranking of aerotropolises in competitiveness evaluation

Decline in the preference level of “airport access modes” in Beijing

Rise in the preference level of “government’s support and management” in Taoyuan

Di

D i

FCi

Ranking

Di

D i

FCi

Ranking

Di

D i

FCi

Ranking

3.106 0.304 1.636 3.379 3.127

2.292 4.074 3.710 1.007 2.241

0.425 0.931 0.694 0.230 0.417

3 1 2 5 4

3.441 0.304 1.636 3.379 3.127

1.944 4.074 3.710 1.007 2.241

0.361 0.931 0.694 0.230 0.417

4 1 2 5 3

3.139 0.342 1.675 3.414 3.005

2.205 3.986 3.627 0.921 2.241

0.413 0.921 0.684 0.212 0.427

4 1 2 5 3

D*i means the distances of each alternative from the ideal solution Di. Di means the distances of each alternative from the negative-ideal solution Di.. FCi is the final ranking of the alternatives arranged in descending order according to their relative closeness value.

The alternatives are arranged in descending order according to their relative closeness value and the final ranking of the alternatives is shown on the left in Table 7. Finally, Hong Kong aerotropolis, with the highest relative closeness to the ideal solution of 0.931, is selected as the most competitive aerotropolis in East Asia, followed by Incheon, Beijing, Taoyuan, and Shanghai. The outcomes of the proposed methodology and the ranks of the alternatives illustrate the tendencies of the aerotropolis competitiveness evaluation process. The proposed approach can originally be utilized as a decision aid for the industries located in the aerotropolis; moreover, it provides both motivation and contributions to operators and managers related to the aerotropolis’s critical administrative issues. 4.5. Sensitivity analysis A sensitivity analysis is performed in this section. The objective of a sensitivity analysis of an MCDM problem is to find out when the input data are changed into new values, how the ranking of the alternatives will change (Triantaphyllou et al., 1998).

By changing the experts’ preferences for COA criteria into linguistic variables, as shown in Table 3, changes in the rankings of alternatives can be obtained. The changes in Table 7 show that the rankings of Beijing and Taoyuan are swapped in only two: a decline in the level of “airport access modes” in the Beijing aerotropolis or a rise in the level of the “government’s support and management” in Taoyuan. This indicates that “airport access modes” represent the crucial competitive criterion for the Beijing aerotropolis, and continuous improvement in the airport access modes will help Beijing aerotropolis to maintain its competitiveness. In addition, the government and operators of Taoyuan should enhance the administration’s ability and awareness concerning the significance of aerotropolis development. The results of the sensitivity analysis are significant and useful in the evaluation of the COA. 5. Conclusions and future work Airports do not develop in isolation, but rather do so along with their neighboring areas. It is necessary and significant to

G.-T. Yeo et al. / Journal of Air Transport Management 32 (2013) 24e31

conducting a COA analysis to provide useful information in regard to the airport industries. With the use of the data for the input variables and the calculation process suggested by the adopted fuzzy MCDM methodology, we were able to obtain the COAs and the competitiveness rankings of aerotropolises in East Asia. The most important criteria of COA were shown to be the “flight and transfer hub”, “geographic location”, “airport access modes” and “land use and cost”. The Hong Kong aerotropolis was ranked as the most competitive, followed by Incheon, Beijing, Taoyuan, and Shanghai. The sensitivity analysis indicated that Beijing aerotropolis is competitive in terms of taking advantage of its perfect airport access modes; in order to enhance the competitiveness of the Taoyuan aerotropolis, the government and operators should strengthen the administration’s ability and awareness concerning the significance of aerotropolis development. This paper represents a first step in exploring the COA. Some important additional studies can follow from this paper and some future research directions are as follows: (1) incorporating uncertainties into the analysis: regulatory variation, potential risk under the changeable political and economic environments, security of critical infrastructure, etc.; (2) developing an integrated quantitative and qualitative analysis model to obtain more accurate results. Acknowledgments The authors thank the anonymous reviewers of this paper for the time and effort they put into reviewing the paper to improve its quality. This work was supported by the Incheon National University (International Cooperative) Research Grant in 2012. References Baker, D.C., Freestone, R., 2010. The Airport City: a New Business Model for Airport Development. In: Critical Issues in Air Transport Economics and Business, pp. 150e164. Barros, A.G.D., Somasundaraswaran, A.K., Wirasinghe, S.C., 2007. Evaluation of level of service for transfer passengers at airports. Journal of Air Transport Management 13, 293e298. Berechman, J., Wit, J.D., 1996. An analysis of the effects of European aviation deregulation on an airline’s network structure and choice of a primary West European hub airport. Journal of Transport Economics and Policy 30 (3), 251e274. Bozbura, F.T., Beskese, A., 2007. Prioritization of organizational capital measurement indicators using Fuzzy AHP. International Journal of Approximate Reasoning 44, 124e147. Buckley, J.J., 1985. Fuzzy hierarchical analysis. Fuzzy Sets System 17 (1), 233e247. Buyukozkan, G., Cifci, G., 2012. A combined Fuzzy AHP and Fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications 39, 2341e2354. Child, D., 2006. The Essentials of Factor Analysis, third ed. Continuum, London. Chou, C.C., Liu, L.J., Huang, S.F., Yih, J.M., Han, T.C., 2011. An evaluation of airline service quality using the Fuzzy weighted SERVQUAL method. Applied Soft Computing 11, 2117e2128. Chou, C.C., 2010. Application of FMCDM model to selecting the hub location in the marine transportation: a case study in Southeastern Asia. Mathematical and Computer Modelling 51, 791e801. Dirk, K., 2009. The Role of Information Technology in the Airport Business: A Retailweighted Resource Management Approach for Capacity-constrained Airports (Ph.D. thesis). School of Engineering, Cranfield University. Freestone, R., Baker, D., Stevens, N., 2011. Managing airport land development under regulatory uncertainty. Research in Transportation Business & Management 1, 101e108. Gardiner, J., Ison, S., Humphreys, I., 2005. Factors influencing cargo airline’s choice of airport: an international survey. Journal of Air Transport Management 11 (6), 393e399. Hair, J.F., Anderson, R.E., Tatham, R.L., 1987. Multivariate Data Analysis, second ed. Macmillan, New York. Hsieh, T.Y., Lu, S.T., Tzeng, G.H., 2004. Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management 22 (7), 573e584. Janic, M., Reggiani, A., 2002. An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research 2 (2), 113e141.

31

Jayalath, J.T.D., Bandara, J.M.S.J., 2001. Future of Colombo airport (CMB) as an airline hub. Journal of Air Transportation World Wide 6 (2), 117e128. Kasarda, J.D., 2011. 2011-Aerotropolis Status. http://www.Aerotro-polis.com. Kasarda, J.D., 2009. Governing the Aerotropolis. Global Airport Cities. http://www. aerotropolis.com/files/2009_04_GoverningTheAerotropolis.pdf. Kasarda, J.D., 2006. Rise of the Aerotropolis. Fast Company, pp. 76e85. http://www. Aerotropolis.com. Keumi, C., Murakami, H., 2012. The role of schedule delays on passengers’ choice of access modes: a case study of Japan’s international hub airports. Transportation Research Part E 48, 1023e1031. Kim, J.Y., Park, Y.H., 2012. Connectivity analysis of transshipments at a cargo hub airport. Journal of Air Transport Management 18, 12e15. Kratzsch, U., Sieg, G., 2011. Non-aviation revenues and their implications for airport regulation. Transportation Research Part E 47, 755e763. Lai, W.H., Chang, P.L., Chou, Y.C., 2010. Fuzzy MCDM approach to R&D project evaluation in Taiwan’s public sectors. Journal of Technology Management in China 5 (1), 84e101. Liou, J.J.H., Tang, C.H., Yeh, W.C., Tsai, C.Y., 2011. A decision rules approach for improvement of airport service quality. Expert Systems with Applications 38, 13723e13730. Liou, J.J.H., Tzeng, G.H., 2007. A non-additive model for evaluating airline service quality. Journal of Air Transport Management 13, 131e138. Lirn, T.C., 2010. Study of airlines’ cargo hub airport selection: an empirical study in Taiwan. Journal of the Eastern Asia Society for Transportation Studies 8, 2355e 2364. Lo, M.C., Tzeng, G.H., 2011. Fuzzy MCDM application for strategy evaluation. In: 2011 IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, pp. 1485e1490. Max, M.W., 2007. Aerotropolis: Airport Cities. http://www.aerotropolis.com/files/ 2007_01_Aerotropoli.pdf. Menou, A., Benallou, A., Lahdelma, R., Salminen, P., 2010. Decision support for centralizing cargo at a moroccan airport hub using stochastic multi-criteria acceptability analysis. European Journal of Operational Research 204, 621e629. Mon, D.L., Cheng, C.H., Lin, J.C., 1994. Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy Sets System 62 (2), 127e134. Nucciarelli, A., Gastaldi, M., 2009. Collaboration in the airport business through the development of an IT platform. International Journal of Production Economics 121, 562e573. Ohashi, H., Kim, T.S., Oum, T.H., Yu, C., 2005. Choice of air cargo transshipment airport: an application to air cargo traffic to/from Northeast Asia. Journal of Air Transport Management 11 (3), 149e159. Onut, S., Soner, S., 2008. Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management 28, 1552e1559. Pagliari, R., 2005. Developments in the supply of direct international air services from airports in Scotland. Journal of Air Transport Management 11, 249e257. Rendeiro, R., Cejas, M., 2006. Tourism service quality begins at the airport. Tourism Management 27, 874e877. Sasaki, M., Suzuki, A., Drezner, Z., 1999. On the selection of hub airports for an airline hub-and-spoke system. Computers & Operations Research 26 (14), 1411e 1422. Skouloudis, A., Evangelinos, K., Moraitis, S., 2012. Accountability and stakeholder engagement in the airport industry: an assessment of airports’ CSR reports. Journal of Air Transport Management 18, 16e20. Stevens, N., Baker, D., Freestone, R., 2010. Airports in their urban settings: towards a conceptual model of interfaces in the Australian context. Journal of Transport Geography 18, 276e284. Stenvert, R., Penfold, A., 2007. Container Port Strategy: Emerging Issues. Ocean Shipping Consultants. Triantaphyllou, E., Shu, B., Sanchez, S.N., Ray, T., 1998. Multi-criteria decision making: an operations research approach. Encyclopedia of Electrical and Electronics Engineering 15, 175e186. Walker, A.R., Baker, D.C., 2010. A panning support system for airport city development. In: 14th Air Transport Research Society (ATRS) Conference, Porto, Portugal, pp. 1e11. Walker, A.R., Stevens, N.J., 2008. Airport city developments in Australia: land use classification and analysis. In: 10th TRAIL Congress and Knowledge Market, Rotterdam, Netherlands. Wang, K.J., Hong, W.C., 2011. Competitive advantage analysis and strategy formulation of airport city development-the case of Taiwan. Transport Policy 18, 276e288. Wang, K.J., Hong, W.C.H., Chen, S.H., Jiang, J.T., 2011. Strategic development trend and key factors analysis of airport city in Taiwan. Journal of Transport Geography 19, 807e820. Wang, Y.M., Elhag, T.M.S., 2006. Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications 31, 309e319. Yeo, G.T., Roe, M., Dinwoodie, J., 2008. Evaluating the competitiveness of container ports in Korea and China. Transportation Research Part A 42, 910e921. Yuen, S.S.M., 2008. Selection criteria of air hub in Pearl River Delta (PRD) region: from shipper’s perspective. In: International Forum on Shipping, Ports and Airports (IFSPA 2008), Hong Kong. Zeng, A.Z., Rossetti, C., 2003. Developing a framework for evaluating the logistics costs in global sourcing processes: an implementation and insights. International Journal of Physical Distribution & Logistics Management 33 (9), 785e803.