An Analysis of the Competitiveness of Asia’s Major Airports

Yonghwa Park Director·Senior Research Fellow Department of Aviation Research

The Korea Transport Institute (KOTI) 2311, Daehwa-dong, Ilsan-gu, Goyang-city, Gyeonggi-do, Korea

Tel. +82-(0)31-910-3096 Fax. +82-(0)31-910-3227 E-mail: yhpark@koti.re.kr

ABSTRACT Asia is one of the fastest growing regions within the global air transport market. Since the 1990s, air passenger and cargo traffic volumes in this region have increased dramatically, and potential demand is enormous. In response to such trends, many Asian countries have opened new airports or expanded existing facilities in the hopes of laying claim to the region’s main international hub airport. This paper presents an analysis of the competitive status of Incheon International Airport (ICN) as well as seven major airports in the East Asia region: Tokyo Narita (NRT), Osaka Kansai (KIX), New Hong Kong (HKG), Shanghai Pudong (PVG), Taipei Chiang Kai Shek (TPE), Singapore Changi (SIN), and Kuala Lumpur Sepang (KUL). The analysis assesses these airports based on five factors: service, demand, managerial, facility, and spatial qualities. In order to analyze the competitive status of the selected airports, this study has applied a multi-decision criteria approach and is based on a comparison of qualitative data.

The competitive status of the selected airports will be represented in terms of a competitive indicator, which will be most best competitive airport, as determined by its having the minimum score. In addition, the competitive status of each airport can be categorized as one of the following levels: most, more, less, or least competitive. Keywords: Incheon International Airport, airport competitiveness, multi-decision criteria,

competitive factors 1. INTRODUCTION During the last two decades, air transportation demand in Asia has grown at a faster rate than any other region of the world. Moreover, potential air transportation demand in this region is also enormous, because of its high population density, strong economic growth, improving political stability and widespread open-skies. In particular, the Chinese air market is the fastest growing, with its enormous population and territorial size, as well as the highest economic growth rate worldwide. According to Airbus Industrie (2000),

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Chinese air traffic demand, in terms of revenue passenger-kilometers (RPK), is forecast to grow by an average of 8.1 percent per annum over the next two decades. The annual growth rate for Asian countries has been forecast at 7.7 percent per annum during the same period. These growth rates obviously outpace the predicted average growth rate for the world, which is 4.9 percent per annum. According to the Airports Council International (ACI, 2002), in 2001, five of the 30 busiest airports in the world – Tokyo Haneda (HND), Hong Kong (HKG), Bangkok (BKK), Singapore (SIN), and Tokyo Narita (NRT) – were in the Asian region, in terms of numbers of passengers handled. In terms of aircraft operations, however, no Asian airport was ranked within the top 30 worldwide. These finding suggest more passengers are being moved with fewer aircraft movements. In this case, it is clear that air traffic on short-haul routes has been, and will remain, dominant, although traffic on short-haul routes will grow at a faster rate than that of long-haul routes (Park, 1997: 292). In 2000, the average number of aircraft seats on Asian carriers was estimated at 242 seats, versus an average of 179 seats in the aircraft of other regions (Airbus, 2000). Indeed, compared to the average wide-bodied aircraft proportion of all IATA members, 25.9 percent, Asian airline fleets boast the highest proportion of wide-bodied aircraft. (2001). Many Asian countries have undertaken expansion and construction projects on their existing airport’s facilities and new airports to accommodate sharply increasing air demand. In Korea, the main gateway airport in the Seoul Metropolitan Area was Gimpo International Airport (GMP). Gimpo Airport handled approximately 34.7 million passengers per annum, plus 2.3 million tons of cargo. Since 1995, Gimpo had been overloaded, resulting in congestion and delays during peak hours. In response to these problems, a new airport, Incheon International Airport, was being planned and constructed on Youngjong Island, 55 kilometers west of center of Seoul, beginning in 1989. It opened in March 2001 as a potential main hub airport in Northeast Asia. Since its opening, the airport’s operations have been fairly successful, and most of its related systems have been working properly. However, the level of services and operational skills will have to be enhanced if it is to become a regional hub airport among major airports in Northeast Asia. Since opening for service, the new major airports in Northeast and Southeast Asia have been faced with fierce competition to become the main regional hub airport – in terms of quantitative and qualitative service indices – both now and into the future. Because it is very difficult to analyze the relative competitive status of these airports as a result of their different operational systems, financial accounting methods, air traffic volume, geographical conditions, this paper has attempted to apply a simplified approach to the analysis of their relative competitive status. 2. DEFINITION OF COMPETITIVENESS Michel Porter introduced the concept of competitive advantage. His initial effort concentrated on competition and strategy within an industry. Porter (1980) addressed the importance of competitive advantage in a single business because he understood that it

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determined the level of that industry’s profitability. According to Porter, the state of competition in an industry depends on five basic forces, which are diagrammed in Figure 1, and that the collective strength of these forces determines the ultimate profit potential of an industry (Porter 1998:21). Hence, competitive advantage can be defined as the ability to jockey for position against various competitors.

Figure 1. Forces Governing Competition in an Industry (Porter, 1998:22) Porter (1998:160) explained that the only meaningful concept regarding competitive advantage at the national level is productivity. The principal goal of a nation is to produce a high and rising standard of living for its citizens. The ability to do so depends on the productivity with which a nation’s labor and capital are employed. Productivity depends on both the quality and features of products and the efficiency with which they are produced. It is also the primary determinant of a nation’s long-term standard of living. How do we measure the competitive advantage of an airport? The answer is not clear and simple. Referring to Michel Porter’s definition, it can be defined as the relative strength of five of the airport’s core factors: level of services provided to users, spatial advantages relative to neighbor airports, quality of facilities, traffic volume, and managerial efficiency. The competitive advantage of an airport depends on five core factors, which are diagrammed in Figure 2. More detailed illustration of the five-core-factor is as follows:

Spatial factors: Refers to the level of development of the region surrounding the airport, influencing the economics of the airport’s vicinity, such as international trade zones, logistics and convention centers, airport-related industrial complexes and other facilities.

The Industry

Jockeying for position among current competitors

Threat of new entrants

Threat of substitute products or services

Bargaining power of suppliers

Bargaining power of customers

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Facility factors: Related to the level of airport facilities and expandability of the facilities at the existing airports in order to increase capacity. Demand factors: Represents the level of origin-destination (O-D) demand and

sufficient transit and transfer traffic volumes for hub-and-spoke network at an airport. Both air passenger and cargo demand are considered. Service factors: Mainly consists of level of services to users, type of airport

operations, and level of airport charges. Managerial factors: Refers to economical considerations such as airport

operating costs, productivity, revenue structure, revenue scales per provision space, etc.

Figure 2. The Structure of Airport Competitive Advantage 3. METHODOLOGY 3.1 Previous Studies In general, it is difficult to find research literature regarding the competitive analysis of ports. There have not been many studies conducted on the analysis of airport competitive status. Various studies have been carried out by academicians and practitioners however, with regard to seaports. As airports and seaports share many facilities and functions, it is surely valuable to review studies on seaport competitive studies to apply to airports.

Airport Competitiveness

Demand Factors

Facility Factors

Managerial Factors

Service Factors

Spatial Factors

-airport vicinity development

-environmental and

economic conditions

-level of services

-operating conditions

-operating systems

-costs and revenues

-productivity

-level of facility

-expansion

-physical conditions

-O-D demand

-hub & spoke network

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Basically, there are two main methodologies for evaluating the international competitiveness of a container port. The first method is the traditional productivity analysis approach, which considers the facilities’ efficiency, selectivity, and land availability based on general capacities such as handling ability, storage ability, and terminal size. The other method is multi-attribute utility analysis, which considers such elements as the reasons for selecting particular container ports and determining factors of competitive strength. Analyses of seaport competitiveness have concentrated on the international competitive status of container ports. According to studies by Willingale (1984), Slack (1985) and Murphy (1987), the determining factors for international container ports’ competitive strength were the port selectivity of shippers, carriers and forwarders. In particular, they considered service factors to be more important than cost factors. Suykens (1987) analysed port productive efficiency for the main European ports at Antwerp, Hamburg, Bremen and Rotterdam. Fleming (1997) applied a throughput data analysis method to 25 ports worldwide. Tongzon (2001) measured the port productive efficiency for 16 Australian and other major container ports using data envelopment analysis (DEA). Other analyses were carried out by Monie (1987), Talley (1988), Jun (1993) and Sachish (1996). A competitiveness analysis of airports was undertaken by Doganis and Graham (1987), who applied the work load unit (WLU) 1 to compare selected European airports’ performance using various indicators. Graham (1998) revised the first airport performance measurement in 1987. Assailly (1989) carried out an analysis of French airports based on their productivity. Park (1997) applied a fuzzy linguistic approach to analyze major Asian airports’ competitiveness. He selected nine major airports in the Northeast and Southeast Asian regions. His analysis assessed eight main factors: the airport’s geographical characteristics, airport ground access system, environmental effects, the business and operating conditions of airlines, development of the airport vicinity, availability of planning implementation, socio-economic effects, and airport user charges. According to the results, the most competitive airports in Asia were the New Hong Kong International Airport (HKG), Singapore Changi International Airport (SIN), and Seoul Incheon International Airport (ICN). At that time, several selected airports were under construction, so this evaluation did not apply quantitative methods, because of limitations in providing practical performance data. Recently, the Air Transport Research Society (2002) conducted a study on airport performance benchmarking. Seventy-six airports were selected, as the objective of the benchmarking analyses was to measure and compare the performance of three aspects of airport operations: productivity and efficiency, unit cost, and financial results. The report also examined relationships between various performance measures and airport

1 Work Load Unit is equivalent to one airport terminal passenger or 100 Kg of freight or mail. This

maintains the original 10:1 relationship used within the airline industry. While such a ratio was logical for airlines in that it was based on weight, which was critical for aircraft payloads, its relevance to airports is questionable.

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characteristics in order to better understand the observed differences in airport performance. 3.2 Methodology In general, the airports represented varying sizes, ownership structures, financial accounting methods, and geographic conditions. It should be noted that not all of the selected airports had complete data required for every aspect of the analysis. Therefore, this paper adopts the five-core-factor analysis method, referred to here as the simple multi-decision model. Analysis of airport competitiveness consisted of the following five steps: (1) selection of airport, (2) determination of competitive factors, (3) evaluation of the component factors of each the five-core-factor group using the scoring and ranking method and the assessing index method, (4) aggregative evaluation for all component factors which are subjected to a five-core-factor group and considering these group factors’ weighted values, and (5) computation of final airports’ competitiveness. The analysis steps are shown in Figure 3.

Figure 3. Framework for Airport Competitive Strength Analysis Airport Selection The first step in analyzing the competitive strength of an airport is the selection of target airports. This paper tried to focused on major airports in the Northeast and Southeast Asian regions, mainly Korea, China, Japan, Taiwan, Singapore, and Malaysia. Only

Results of airport competitive strength

analysis results

Multi-Decision Model

Airport Selection

Determining factors

Evaluating component factors - scoring & ranking method - assessing index method

Five-core-factor group’s weighted

values

Aggregative evaluation for all

component factors

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airports in operation were considered; airports under construction were not considered. Figure 4 shows these airports’ locations.

Korea (1) Seoul Incheon International Airport (ICN) Japan (2) Tokyo Narita International Airport (NRT) Japan (3) Osaka Kansai International Airport (KIX) China (4) New Hong Kong International Airport (HKG) China (5) Shanghai Pudong International Airport (PVG) Taiwan (6) Taipei Chiang Kai Shek International Airport (TPE) Singapore (7) Changi International Airport (SIN) Malaysia (8) Kuala Lumpur International Airport (KUL)

Figure 4. Location of the Northeast and Southeast Asian Airports Determining Factors for Competitiveness In Section 2, airport competitive strength was defined by the five core factors. Now there is a need to determine the component factors of each of the five core factors. As mentioned above, these component factors can be hardly determined to analyze airport competitive strength, as almost every airport has different operational characteristics, particularly with regard to international relations. Therefore, in this paper, the component factors of each five-core-factor group were simplified and applied to all selected airports. The five-core-factor had their won weighted values in terms of the importance for airport competitiveness, but the component factors of each core-factor had not considered. The component factors of each core-factor group are as follows:

Changi

New Hong Kong Chiang Kai Shek

Narita Kansai

Incheon

Kuala Lumpur, Sepang

Pudong

Asia’s Major Airports

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}{ kFF = , k = 1, 2, … , 5, where, 1F = service factor, 2F = demand factor, 3F = managerial factor,

4F = facility factor, and 5F = spatial factor.

Service factor (F1) and its component factors (f1i)

F1 = { f1i }, f11 = service performance measurements, f12 = terminal space (square meters) per passenger, f13 = level of airport charges, and f14 = airport operational time.

Demand factor (F2) and its component factors (f2i)

F2 = { f2i }, f21 = number of airlines and flight frequency, f22 = hub-and-spoke network condition, and f23 = level of induced force of demand.

Managerial factor (F3) and its component factors (f3i)

F3 = { f3i }, f31 = sales per unit throughput, f32 = ratio of aeronautical vs. non-aeronautical revenues, f33 = net profit per unit throughput, and f34 = type of airport operation.

Facility factor (F4) and its component factors (f4i)

F4 = { f4i }, f41 = availability of expansion, and f42 = category of air navigation facility. Spatial factor (F5) and its component factors (f5i)

F5 = { f5i }, f51 = environmental effects on vicinity society, f52 = accessibility to airport, and f53 = airport regional development.

Multi-Decision Criteria Model This study adopted a multi-decision criteria model to analyze airport competitive strength. The model consisted of three steps: (1) evaluating each component factor of a core-factor group using the scoring or ranking method, (2) aggregative evaluation of each core-factor group, and (3) consideration of weighted values for each core-factor group. The steps are as follows:

Evaluation of each component factor )( ilkk MC ×≡ ,

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where, k represents one of the five-core-factor group, l is number of selected airports, and i is number of component factors of k core-factor group.

Aggregation of the core-factor group (

kC~ )

ilk MC ×≡ ~~ ,

Consideration of weighted values for each core-factor group (Wk)

klkk MWCD ×≡⋅= ~~ ,

where, D is airport competitive strength, Wk is the weighted value for k core-factor group.

The weighted value for each core-factor group was established through a survey of airport experts. Of 38 airport experts who responded, 13 (35%) were employees of the airport authority or government, 11 (29%) were from airlines, 7 were academicians (18%), and 7 were researchers (18%). From these experts’ responses, the weighted value for each core-factor group was defined. According to the results, shown in Table 1, the most important factor for airport competitiveness is the demand factor, followed by the service factor, spatial and facility factors, and managerial factor, in that order.

Table 1. Degree of Importance of Each of the Five Core Factors

Core Factors for Airport

Competitiveness

Degree of Importance or

Weighting Value

Demand Factor

Service Factor

Spatial Factor

Facility Factor

Managerial Factor

1.000

0.741

0.453

0.453

0.438

4. ANALYSIS OF COMPETITIVE STRENGTH 4.1 Service Competitive Strength (F1) Service Performance Measurements ( f11) Direct measurement of the selected airports’ service performance was not possible. Instead, existing service measuring results were adapted to this analysis. Business Traveler Asia-Pacific (BTAP) had released the results of an October 2001 passenger survey regarding the provision of services to passengers at major airports worldwide. Singapore Changi Airport (SIN) was ranked at the top in categories such as duty free

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shop, immigration services, baggage handling, customs, and transit and transfer services. Ranked second was the New Hong Kong Airport (HKG), which showed outstanding results in almost every category. Table 2 shows the results of the service performance measurement (f11).

Table 2. Results of Service Performance Measurement (f11) Evaluation

Ranking 1 2 3 4 5 6 7 8

Airport SIN HKG KUL ICN NRT TPE KIX PVG

Terminal Spaces per Passenger (f12) This component factor is based on physical characteristics. The size of terminal is based on current figures, although the data on passenger numbers are from 1999. Data on the competitive strength of terminal physical structures are listed in Table 3.

Table 3. Terminal Spaces per Passenger (f12)

Ranking Airport No. of Pax. (thousand)

Pax. Terminal (thousand m2)

No. of Pax. per Unit Space

1 KUL 15,172 527 28.8

2 ICN 19,303 496 38.9

3 SIN 26,065 634 41.1

4 NRT 25,566 622 41.3

5 PVG 12,346 280 44.1

6 TPE 17,044 378 45.1

7 HKG 29,772 515 57.7

8 KIX 19,880 301 65.9

Note: Shanghai Pudong (PVG) appeared passenger volume in 2000. Level of Airport Charges (f13) Among the airport charges to users, the aircraft landing fee is normally the most representative indicator. In this analysis, a large aircraft, the B747-400, 352 tons, was chosen as the analysis standard. According to the results, Japanese airports showed a very

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high level of airport fees, while Kuala Lumpur, Changi, and Incheon Airport were relatively low. These results are shown in Figure 5.

0 2000 4000 6000 8000 10000

Landing Fees ( Large Ai rc raft, B747-400)

KUL

SIN

ICN

HKG

TPE

SHA

KIX

NRT

Airports

Figure 5. Comparison of Aircraft Landing Fee Levels (f13)

Airport Operational Time (f14) Nowadays, most major airports have to operate around the clock to meet surplus demand and provide various air services to customers. However, many airports in the world cannot operate throughout the day, because of environmental problems. Most notably, Tokyo Narita Airport (NRT) and Taipei Chiang Kai Shek Airport (TPE) have a curfew from 23:00 to 06:00 each day. Aggregative Evaluation of Service Competitive Strength (C1) The results of the aggregative evaluation of the service factor group are shown in Table 4. The analysis method of component factors applied the scoring and ranking rule. The most and least competitive airports receive a score or rank of “1” to “8,” respectively. This means that an airport with a rank of 1 is the most competitive airport. Kuala Lumpur Airport (KUL) was the most competitive airport in terms of service factors such as service performance measurements, terminal spaces, level of airport charges, and airport operating time. The next was Singapore Changi Airport (SIN), followed by Incheon, Hong Kong, Pudong, and Kansai Airports. The least competitive airports were Narita and Chiang Kai Shek.

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Table 4. Aggregative Evaluation of Service Factor (C1)

Ranking Airport f11 f12 f13 f14 C1 1 KUL 3 1 1 1 6 2 SIN 1 3 2 1 7 3 ICN 4 2 3 1 10 4 HKG 2 7 4 1 14 5 PVG 8 5 6 1 20 6 KIX 7 8 7 1 23 7 NRT 5 4 8 7 24 7 TPE 6 6 5 7 24

4.2 Demand Competitive Strength (F2) Number of Airlines and Flight Frequency (f21) At an airport, the number of operational airlines and their flight frequencies can be represented as a level of airport grade. An airport has various and dense air networks, which can be possible a hub-and-spoke network operations. In order to analyze the airport competitive strength based on demand, this study set up a demand competitive indicator (DI), as follows: cwa NFNDI ++= , where, Na is the number of operational airlines at airport, Fw is the flight frequencies per week, and Nc is the number of air route connecting cities. Using this indicator, the demand competitive strength can be calculated (OAG, 2001). Table 5 shows results of the demand competitive analysis considering the number of airlines and flight frequency (f21).

Table 5. Evaluation of the Number of Airlines and Flight Frequency (f21)

Ranking Airport No. of Airlines Frequency

per Week Connecting

Cities Evaluation

Index 1 NRT 48 6,504 309 6,861 2 HKG 56 6,139 298 6,493 3 SIN 57 5,282 268 5,607 4 TPE 34 5,072 255 5,361 5 KUL 36 3,558 222 3,816 6 KIX 49 3,104 199 3,352 7 ICN 46 2,959 189 3,194 8 PVG 25 1,151 139 1,315

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Hub-and-Spoke Network Condition (f22) In order to build up an efficient hub-and-spoke network, it is not only well-developed long-haul routes, but also high density of feeder routes in neighboring areas. Hence, the feeder routes’ framework can be the main element of a hub-and-spoke network. The evaluation of network condition depends on how feeder routes are established. To answer this question, this study set up a competitive index, which considered the number of air routes connecting cities and weekly flight frequencies within a 2,000-mile range from an airport. Moreover, the rate of them out of total connecting cities and flight frequencies must be considered. Therefore, the index of hub-and-spoke network condition for demand competitive strength (DJ) can be defined as follows: ][][ h

cgc

bw

aw rNNrFFDJ +++= ,

where, a

wF is the ranking of flight frequency per week within a 2,000-mile air route range, bwrF is the frequency rate ranking within a 2,000-mile air route range out of total

frequency, gcN is the ranking of the number of air route connecting cities within a 2,000-

mile distance, and hcrN is the number rate ranking within 2,000 miles distance out of

total connecting cities. The result of evaluation for hub-and-spoke network condition for demand competitive strength is shown in Table 6.

Table 6. Results of Evaluation for Hub-and-spoke Network Conditions (f22) Year: 2001

Frequencies per Week Route Connecting Cities Rank Airport

No. Rank Rate Rank No. Rank Rate Rank Index(DJ)

1 TPE 2,106 1 41.5 2 69 3 27.1 5 11

2 ICN 1,103 6 37.3 4 72 2 38.1 1 13

3 HKG 1,983 2 32.3 6 78 1 26.2 6 15

4 KUL 1,321 4 37.1 5 67 4 30.2 3 16

5 KIX 1,208 5 38.9 3 59 6 29.6 4 18

5 PVG 516 8 44.8 1 47 7 33.8 2 18

7 SIN 1,585 3 30.0 7 61 5 22.8 7 22

8 NRT 633 7 9.7 8 33 8 10.7 8 31

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Level of Induced Force of Demand (f23) In order to analyze the level of induced force of air transport demand, the degree of city development, inter and intra-city networks, and the size of the airport umbrella cities can be considered. This means that the potential developmental ability of an airport umbrella city can be estimated by its population and the size of GDP in the airport umbrella metropolitan areas. In this study, this concept was applied to evaluate the level of induced force of demand. The results are shown in Table 7. The largest force outcome was in Tokyo, with Hong Kong ranking second.

Table 7. Evaluation of Induced Force of Demand (f23) Year: 1996

Population GDP Ranking Main City

Million Rank Billion $ Rank

EvaluationIndex

1 Tokyo 39.2 1 1,330.9 1 2

2 Hong Kong 28.0 3 167.1 4 7

2 Seoul 20.2 4 209.1 3 7

2 Osaka 16.8 5 610.6 2 7

5 Shanghai 37.3 2 47.4 7 9

6 Taipei 7.9 6 108.8 5 11

7 Singapore 5.1 7 69.5 6 13

8 Kuala Lumpur 4.2 8 14.8 8 16

Aggregative Evaluation of Demand Competitiveness (C2) The results of the aggregative evaluation of the demand factor group are shown in Table 8. Hong Kong Airport (HKG) is in a strong position in terms of air transport demand competitiveness. The next are Narita Airport (NRT), Incheon Airport (ICN) and Chiang Kai Shek (TPE). Shanghai Pudong Airport (PVG) is situated in the least competitive airport umbrella metropolitan area.

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Table 8. Aggregative Evaluation of Demand Factor (C2)

Ranking Airport f21 f22 f23 C2

1 HKG 2 3 2 7

2 NRT 1 8 1 10

3 ICN 7 2 2 11

3 TPE 4 1 6 11

5 KIX 6 5 2 13

6 KUL 5 4 8 17

6 SIN 3 7 7 17

8 PVG 8 6 5 19

4.3 Managerial Competitive Strength (F3) Sales per Unit Throughput (f31) Kansai Airport (KIX) had the largest sales per work load unit (WLU), at US$37.6, followed by Narita Airport (NRT), at US$24.9 sales per WLU; and Hong Kong Airport (HKG), at US$13.1 sales per WLU. Shanghai Pudong Airport (PVG) could not be evaluated because of the difficulty of obtaining relevant information. The results of the managerial competitive strength evaluation based on sales per unit throughput are shown in Table 9.

Table 9. Evaluation of Managerial Competitiveness for Sales (f31)

Ranking Airport WLU Total Sales (million US$) Sales per WLU

1 KIX 28,522,894 1,071.6 37.6

2 NRT 44,083,354 1,097.4 24.9

3 HKG 49,733,385 650.8 13.1

4 KUL 19,624,637 215.0 11.0

5 SIN 41,294,485 447.0 10.8

6 TPE 27,616,429 256.0 9.3

7 ICN 34,203,000 305.2 8.9

- PVG 17,266,596 N/A N/A

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Ratio of Revenues (f32) In general, airport revenues are divided into two categories: aeronautical and non-aeronautical. The more competitive airports are structured so that their non-aeronautical revenues are higher than their aeronautical revenues. This analysis assumed that the more competitive airports had a higher rate of non-aeronautical revenues than aeronautical. The results of this analysis are described in Table 10. The Kansai and Hong Kong Airports turned out to be the most competitive airports in terms of revenue structure.

Table 10. Evaluation of Managerial Competitiveness for Revenue Structure (f32)

Aeronautical Revenues Non-aeronautical Revenues Ranking Airport Unit

Amount % Amount %

1 HKG ‘000 HK$ 2,412,157 46.8 2,746,889 53.2

1 KIX 100 Million Yen 545 46.8 619 53.2

3 SIN ‘000 S$ 376,730 49.8 379,927 50.2

4 ICN Million Won 208,874 54.7 172,667 45.3

5 TPE Million NT$ 603 64.9 326 35.1

6 NRT 100 Million Yen 970 68.6 445 31.4

7 KUL ‘000 RM 576,502 70.6 240,414 29.4

8 PVG ‘000 Yuan 595,558 81.0 139,745 19.0

Net Profit per Unit Throughput (f33) Airport’s performance could be easily compared if they were to follow the same financial accounting rules, operational systems, and cost-revenue structures. Unfortunately, this is not the case. When attempting to compare horizontally the performance of two airports, we must understand their financial characteristics and other related rules and practices. This analysis used data from internal sources and from an airport yearbook. The results of the assessment are shown in Table 11. According to the results, only Singapore Changi and Kuala Lumpur Airport achieved net profits.

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Table 11. Evaluation of Managerial Competitive Strength for Net Profit (f33)

Ranking Airport WLU Net Profit (Million US$)

Net Profit per WLU (US$)

1 SIN 41,294,485 205 5.0

2 KUL 19,624,637 70.6 3.6

3 TPE 27,616,429 0 0.0

4 NRT 44,083,354 - 9.4 - 0.2

5 HKG 49,733,385 - 50 - 1.0

6 ICN 34,203,000 - 230 - 6.7

7 KIX 28,522,894 - 214 - 7.5

- PVG 17,266,596 N/A N/A

Type of Airport Operation (f34) Depending on the type of airport operations, the productivity and efficiency of airport management will show distinctly different results. Public sector operational types achieve relatively low levels of productivity and efficiency based on historical information. However, Singapore Changi Airport has achieved the highest productivity and efficiency in the world even though it runs by governmental organization. So, Changi Airport can get the highest ranking, and the operational system of Kansai is a mix between private and public. Incheon Airport was transformed into a public corporation in 1998, in the hopes of achieving more efficient operations. The assessment of managerial factors in terms of airport operations are shown in Table 12.

Table 12. Evaluation of Managerial Factor for the Type of Airport Operation (f34)

Ranking Airport Type of Airport Operation

1 SIN Civil Aviation Authority

2 KIX Kansai International Airport Co., Ltd.

3 ICN Incheon International Airport Corporation

4 KUL Malaysia Airport Berhad

5 HKG Civil Aviation Department

5 NRT Narita Airport Authority

7 TPE Civil Aeronautics Administration

7 PVG Civil Aviation of China

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Aggregative Evaluation of Managerial Competitiveness (C3) The rational comparison of several airports’ managerial performance should be based on factors such as capital and labor productivity and efficiency. Because of the difficulty of obtaining basic resources, this assessment could not measure all important indicators. In spite of these limitations, several valuable indicators were considered. Thus, Changi, Kansai, and Hong Kong Airport were shown to have relatively high competitive strength levels. The results are shown in Table 13.

Table 13. Aggregative Evaluation of Demand Factor (C3)

Competitive Index Airport f 31 f 32 f 33 f34 C3

1 SIN 5 3 1 1 2.5

2 KIX 1 1 7 2 2.8

3 HKG 3 1 5 5 3.5

4 KUL 4 7 2 4 4.3

4 NRT 2 6 4 5 4.3

6 ICN 7 4 6 3 5.0

7 TPE 6 5 3 7 5.2

8 PVG N/A 8 N/A 7 7.5

4.4 Facility Competitive Strength (F4) The capacity for airport expansion is an important precondition for accommodating surplus air traffic demand. The development phases of large-scale airports are normally divided into at least 2 or 3 stages. In the earlier stage of construction, a sufficient expansion plan is a critical consideration. In evaluate the competitive strength of a facility, the availability of airport land and the capacity for passenger terminal expansion were focused on in this study. Hong Kong (HKG), Kuala Lumpur (KUL), Pudong (PVG), and Incheon Airport (ICN) were strong in these areas, but Chiang Kai Shek (TPE), Narita (NRT) and Kansai Airport (KIX) did not have enough potential expansion capacity. The level of air navigation facilities is considered a very important indicator in the improvement of aircraft operational safety. Almost all of the selected ports were found to possess high-level navigation systems. However, Pudong, Kuala Lumpur, and Chiang Kai Shek Airport are operated by Category-II, so these airports need to upgrade their air navigation facilities. The aggregative evaluation of facility competitive strength is illustrated in Table 14.

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Table 14. Aggregative Evaluation of Facility Factor (C4)

Competitive

Index Airport Availability of Expansion (f41)

Category of Air Navigation (f42)

C4

1 HKG 1 1 2

1 ICN 1 1 2

3 SIN 6 1 7

3 PVG 1 6 7

3 KUL 1 6 7

6 NRT 7 1 8

6 KIX 7 1 8

6 TPE 5 6 11

4.5 Spatial Competitive Strength (F5) Environmental Effects (f51) Since almost selected airports are located far away from city centers, noise levels and environmental impact should be better than ever. However, Tokyo Narita Airport (NRT) and Taipei Chiang Kai Shek Airport (TPE) crossed into their neighboring areas; thus, they are subject to curfews from 23:00 to 06:00 everyday. Airport Accessibility (f52) There is a need for a comprehensive evaluation of airport accessibility, based on level of transit fares, variety of transport modes, convenience, comfort, and other elements. These factors significantly affect user behavior when accessing an airport. Due to many difficulties involved in standardizing all selected airport access data, this analysis applied simple data, namely, the distance between an airport and the center of the main adjacent city. Figure 6 shows the distances from the city center to the selected airports.

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KIX 40km

KUL 75km

HKG 28Km

TPE 31Km30KmSHA

ICN 52Km

NRT 66Km

SIN 20Km

0

25

50

75

100

0 500 1000 1500 2000

Land Site (ha)

Access D

ista

nce (

Km

)

Figure 6. Access Distance to the Selected Airports

Airport Regional Development (f53) Cases of airport regional development were found at several airports: Rinku Town with Kansai Airport; an air logistics center and industry park near Changi Airport; hotels and airport related facilities at Narita; F1 racing facilities near Kuala Lumpur Airport at Sepang. Aggregative Evaluation of Spatial Competitive Strength (C5) The result of aggregative evaluation of spatial competitive strength is represented in Table 15. Changi and Kansai Airport have good relationship with respective surrounding areas.

Table 15. Aggregative Evaluation of Spatial Factor (C5)

Competitive Index Airport f51 f52 f53 C5

1 SIN 6 1 1 8

1 KIX 1 5 2 8

3 HKG 1 2 6 9

4 PVG 1 3 8 12

5 ICN 1 6 7 14

5 KUL 1 8 5 14

7 TPE 7 4 4 15

8 NRT 7 7 3 17

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5. RESULTS Three steps of the multi-decision criteria model were utilized in this study. The first step was the evaluation of each component factor of a core factor group. The second step was an aggregative evaluation of each core factor group. The final step was consideration of weighted values for each core-factor group. These steps are as follows:

=≡ ×

4388578737864273131453461516331625613162

~~

54321 CCCCC

PVGTPENRTHKGKULICNKIXSIN

MC ilk

klkk MWCD ×≡⋅= ~~

]453.0453.0438.0000.1741.0[~ ⋅= kC

=

=

38.1805.1828.1549.1312.1257.1073.909.7

81.136.150.300.871.317.362.307.300.319.562.327.275.100.219.545.027.288.000.545.427.236.175.100.674.027.245.063.200.322.245.036.144.000.648.136.145.031.100.196.2

~~~~~54321 DCCCCC

PVGTPENRTKIXKULICNSIN

HKG

According to the results of this study, the New Hong Kong International Airport has the lowest score, 7.09, and followed by Singapore Changi (9.73), Incheon (10.57), Kuala Lumpur (12.12), in that order. The Taipei Chiang Kai Shek and Shanghai Pudong Airport have relatively higher score, it represents a weak competitive status.

Yonghwa Park 22

6. CONCLUSIONS The simple methodology proposed in this paper can provide a practical and applicable evaluation of the competitive strength of airports in the Northeast and Southeast Asian regions. The first finding of this paper is that even a simple evaluation model is difficult to apply because of problems with standardizing input data from various operational airports. Thus, there is a need to achieve better understanding of the relationship between various performance measures and airport characteristics. In this study, the five core factors were defined as a basic precondition to analyzing airport competitive strength. Airport experts assessed the relative importance of these factors. According to their responses, the demand factor was determined to be the most important factor, followed by the airport service factor. Other factors such as spatial, facility, and managerial factors were found to be of moderate importance. According to the results of this study, the most competitive airport in the Northeast and Southeast Asian regions is the New Hong Kong International Airport (HKG). Singapore Changi (SIN) and Seoul Incheon International Airport (ICN) were assessed as “more competitive” airports. Kuala Lumpur (KUL), Kansai (KIX), and Narita International Airport (NRT) were “less competitive” airports in the region. Finally, Taipei Chiang Kai Shek (TPE) and Shanghai Pudong International Airport (PVG) were ranked at the bottom of the sample. REFERENCES Airbus Industrie (2000), Global Market Forecast 2000-2019, Airbus Industrie, July. ACI (2002), http://www.airports.org, Airport International Council, updated in May 2002. Assailly, C. (1989), Airport Productivity, An Analytical Study, Institute of Air Transport,

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