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1 Original Paper Volume 3 Issue 8 April 2016 International Journal of Informative & Futuristic Research A Study Of Competitiveness Of Airports Using Paper ID IJIFR/V3/ E8/ 049 Page No Subject Area Economics Keywords Competitiveness,, Heuristic Algorithm, Linguistic Variables, Euclidean Distance Dr. Igy George Assistant Professor, Department of Economics, Mar Athanasius College, Kothamangalam Abstract This study is about the competitiveness of three international airports in Kerala by using fuzzy linguistic approach. Competitiveness is analysed based on five factors, which were relevant from the point of view of Kerala. The factors are Airport facilities, Airport Accessibility, Airport Expansion Potential, Airport Charges and Geographical factors. In order to analyse airport competitiveness, two linguistic variables; importance of each factor and competitiveness of each airport; are used. To solve the fuzzy relations of the two linguistic variables, heuristic algorithm methodology by Sanchez is adopted. The most competitive airport is measured by calculating the relative Euclidean distance of each airport from the most optimum compatibility function. The result highlights the relevance of Greenfield airports under Kerala context. 1. INRODUCTION Kerala the southernmost tiny state, leading the other states in infrastructure penetration is now having three operative international airports. The factors that influence the growth of air traffic in Kerala and make it a fertile ground for aviation industry are the favorable climatic conditions, high density of population, increasing number of expatriates, boom in the service sector etc. Moreover, the present generation is giving more importance to speed and competency. More people have used air traffic during the year and the combined passenger movement of the three international airports in Kerala registered a growth of 9.89 percent (AAI Report 2015) over the previous year. With the catchment area of the three international airports overlapping and people giving more importance to the quality of the facilities and services at the airports, there is a need to maintain the airports under the international standards to attract more passengers. Available online through Published On: 28 th April,

2 Kerala, a comparatively smaller state is already having three international airports and all the three airports are performing exceedingly well. Because of the multiplicity of operators including private parties and the possibility of oligopolistic practices and stiff competition, the airports in Kerala are in the process of upgrading their facilities. Competitiveness of an airport is reflected in the various facilities attached to the airport. The international airports, which operated differently, are maintaining some common features. Differences in operation are only at the micro level. The main concerns of the international airports are understanding, maintaining and improving the quality of service at the airports (Yeh and Kuo, 2003). Chou, (2009) stated that main concerns of international airports are evaluation and improvement of the quality of service provided to the passengers. The situation in Kerala is unique compared to other international airports in India where the minimum distance between two international airports is more than 500 kms. In this scenario, an analysis of the competitiveness of the three international airports in Kerala by conducting a study of major factors determining competitiveness seems appropriate. Thus, the thrust area of this paper is to analyse the competitiveness of the three international airports in Kerala. The International airports under study are Calicut, Cochin and Trivandrum. This paper is organized under five sections. The first section provides an introduction. Section 2 presents the methodology, Section 3 analyses the Competitiveness by using, Section 4 identify the most competitive airport and Section 5 presents the result and conclusion. 2. METHODOLOGY Methodology is divided into two parts: Methodology for the selection of factors determining competitiveness and methodology for data analysis & Methodology for the selection of factors determining the competitiveness of each airport. Based on the literature review, eight factors influencing the airport competitiveness are selected for preliminary scrutiny. Based on the interviews with the experts, the factors, which are relevant in determining the competitiveness of airports in the circumstances prevailing in Kerala, were identified and five factors were selected. The factors selected are; Airport facilities (APF), Airport Accessibility (APA), Airport Expansion Potential (AEP), Airport Charges (APC) and Geographical factors (APG). Methodology for Analysis In order to analyse airport competitiveness, two linguistic variables X and Y are used, where X denotes importance of each factor and Y the competitiveness of each airport. Assume that the primary values of X = importance of factors, and new variable values using hedges are as follows: R 1 (x) = {most important (A 1 )} = x 4 R 2 (x) = {more important (A 2 )} = x 2 R 3 (x) = {important (A 3 )} = x 2988

3 R 4 (x) = {less important (A 4 )} = x.5 R 5 (x) = {unimportant (A 5 )} = 1-x R 6 (x) = {least important (A 6 )} = 1-x 2 Primary values of Y = Competitiveness of airports; and new variable values using hedges are defined such as: R 1 (y) = {indeed superior (B 1 )} = y 6 R 2 (y) = {more superior (B 2 )} = y 3 R 3 (y) = {superior (B 3 )} = y 1.5 R 4 (y) = {average (B 4 )} = y R 5 (y) = {inferior (B 5 )} = 1-y 1.5 R 6 (y) = {more inferior (B 6 )} = 1-y 3 R 7 (y) = {most inferior (B 7 )} = 1-y 6 To solve the fuzzy relation of two linguistic variable X and Y, heuristic algorithm methodology is used. In this analysis, the methodology used by Sanchez (1976) for solving the basic linguistic equation is adopted. x i = r ij ₒ y j (1) where x i is the value of importance of a competitive factor i, y j is the value of competitiveness of an airport j r ij is a fuzzy relation of factor i and airport j Since xi = r ij ₒ y j, we get r ij = x T i y j (2) where x T i is the transpose of x i is a compositional operator 1 if R i (x) Rj (y) (3) = R j (y) if R i (x) > R j (y) The intersection of these fuzzy relation r ij for j=1, 2, 3 are obtained by fuzzy intersection as R j = i r ij = Min (r ij ) (4) Hence, a fuzzy relation as a rule of compositional inference can describe the competitiveness of each airport Rjmax (y). Competitiveness of each airport is calculated using the fuzzy relation R j max (y) = max R j (y) (5) The relative Euclidean distance defines in terms of a metric distance of A from any of the nearest crisp sets. The study adopted the procedure followed by Park (1997) in analysing the competitiveness of major airports in Asia using Fuzzy theory. Each airport s competitiveness is measured by applying this distance. The airport having the shortest distance is the most competitive one. The relative Euclidean distance (δ j ) is defined as: δ j = 1 [ [ ] ] 2989

4 for j = 1, 2, 3 where n is the number of elements in the universe of discourse, R * (y) is the ideal compatibility function in terms of the linguistic variable of indeed superior for an airport j. R jmax (y) is the maximum compatibility function for the competitiveness of the airport j 3. ANALYSIS OF THE COMPETITIVENESS OF AIRPORTS BY USING FUZZY LINGUISTIC APPROACH Fuzzy linguistic variable approach is applied to measure the competitiveness of three international airports in Kerala. Two fuzzy linguistic variables importance (X) associated with each of the competitive factors and competitiveness (Y) of each airport is calculated by analyzing the collected secondary data. The values of competitiveness of each airport are divided into six linguistic criteria, B 1, B 2, B 3, B 4, B 5, and B6. In this analysis, R(x) and R(y) are defined as a semantic rule for associating a meaning with each variable name. The primary values of the two variables X and Y are defined on the universe of discourse [0, 1].The competitiveness are calculated in a step-by-step process as follows. Step 1: The competitiveness of each airport is analysed by setting up the degree of importance of the influencing factors and linguistically assessing it by selecting the values of importance (X). In order to assess the relative degree of importance of five factors, the data obtained through personal interview with the experts in the airport field was used. The criteria used for assessing the linguistic importance of each factors is given table 1. Table 1: Criteria for the linguistic assessment of the importance of each factor Factors Relative Degree of Linguistic value of Importance Importance Airport Facilities (APF) 1.00 A1 Airport Accessibility (APA) 0.95 A1 Airport Expansion Potential (AEP) 0.90 A1 Airport Charges (APC) 0.65 A4 Airport Geographical Factors (APG) 0.54 A5 Source: Survey Data In table 1, importance of the five factors is divided in to six categories from most important to unimportant. Based on the criteria given in table 1, the importance of each of the influencing factors to the airport competitiveness was assessed linguistically by selecting values of the variable X= importance, which is given in table 2. Table 2: Linguistic assessment of the importance of each factor MOST IMPORTANT (A1 ) = [ 0.90,1.00] MORE IMPORTANT (A2) = [ 0.80,0.89] IMPORTANT (A3) = [0.70, 0.79] LESS IMPORTANT (A4) = [0.60, 0.69] LEAST IMPORTANT (A5) = [0.50, 0.59] UNIMPORTANT (A6) = [0.00, 0.49] 2990

5 Assessment of the relative degree of importance and the corresponding linguistic values of the five factors based on the inputs from the interview with the experts and on the criteria given in table 1, are done and the values are given in table 2. The linguistic values of A 1, A 1, A 1, A 4 and A 5 are assigned to the five factors as shown in table 2. Hence, the three factors, APF, APA and AEP are linguistically assessed as the most important with respect to the study of competitiveness of the international airports in Kerala. The values related to the competitiveness of each airport with respect to the five factors are assessed after analyzing the secondary data collected from various sources. The values of competitiveness of each airport are divided into six linguistic criteria as given in table 3. Table 3: Criteria for the linguistic assessment of competitiveness of each airport Criteria HIGHLY SUPERIOR (B 1 ) MORE SUPERIOR (B 2 ) SUPERIOR (B 3 ) AVERAGE (B 4 ) BELOW AVERAGE (B 5) INFERIOR (B 6 ) Where U is the set of universe of discourse [0,1] = {0.0,0.1,0.2,0.3,0.4,0.5, 0.6,0.7,0.8,0.9,1.0} Each airport is graded as given in table 4 based on the results got after analyzing the secondary data collected in respect of the three international airports in Kerala, and this grade value is converted in to linguistic assessment of the competitiveness using the criteria given in table -3and the results are given in table-4. Table- 4 Calculated values of degree of competitiveness of three airports VALUES OF Y Factors Calicut Cochin Trivandrum Airport Facilities (APF) Airport Accessibility (APA) Airport Expansion Potential (AEP) Airport Charges (APC) Airport Geographical Factors (APG)

6 Factors Table 5: Linguistic assessment of the competitiveness of each airport Airport Facilities (APF) A 1 B 4 B 1 B 2 Airport Accessibility (APA) A 1 B 5 B 2 B 1 Airport Expansion Potential (AEP) A 1 B 6 B 1 B 4 Airport Charges (APC) A 4 B 2 B 1 B 1 Airport Geographical Factors (APG) A 5 B 3 B 1 B 2 Linguistic variables assigned to the various factors related to the three international airports in Kerala given in table 5 shows that airport facilities which is assigned an importance of A 1 is having varying competitiveness values. Calicut international airport for which the calculated degree of competitiveness is 0.61 was assigned a linguistic value of B 4 for the airport facilities. Cochin and Trivandrum airports were assigned B 1 and B 2 respectively. In the case of the factor airport accessibility, linguistic values assigned to the Calicut, Cochin and Trivandrum airports are B 5, B 2 and B 1 respectively. The factor, airport expansion potential had the lowest competitiveness value for Calicut airport followed by Trivandrum and Cochin. The linguistic value assigned to these airports is B 6, B 1 and B 4 respectively. The factors namely airport charges and geographical characteristics are assessed as less important and least important and the linguistic value of competitiveness assigned to these factors for Calicut airport are B 2 and B 3 respectively. Step 2: In this step, the compatibility functions of fuzzy linguistic variables, X and Y are used to calculate the values of linguistic assessment r ij, in equation (3) in the methodology, in respect of each airport and each influencing factor. There are five influencing factors for each of the three selected airports. Based on this, 15 fuzzy relations representing the five factors for each airport are obtained. For instance the fuzzy relation r 12, which represents the competitiveness of factor airport facilities (i=1) for Cochin International airport (j=2), is shown below. Airport facilities are assessed as most important (A 1 ) in terms of importance and the competitiveness of CIAL with respect to this factor is assessed as highly superior (B 1 ). r 12 = 1 Importance > Competitiveness Calicut Cochin Trivandrum = [ ]

7 In the same manner, 15 fuzzy relations are calculated for the three airports in respect of the five factors of competitiveness. Step 3: Based on the 15 fuzzy relations obtained in step 2, the intersection (R j ) of fuzzy relations in equation (4) in the methodology, associated with each of the three selected airports across the five influencing factors to the competitiveness, are calculated. Step 4: From the three matrices R1, R2 and R3 maximum competitiveness of each of the three airports were extracted by using the equation (5) in the methodology. The maximum competitiveness of each airport with regard to the five influencing factors so calculated are given in table- 6 Table 6: Maximum competitiveness of three international airports Airport Fuzzy Relation Values Of Maximum Competitiveness [ Calicut R 1(Max) (Y) ] [ Cochin R 2(Max) (Y) ] [ Trivandrum R 3(Max) (Y) ] Source: Research data 4. IDENTIFICATION OF THE MOST COMPETITIVE AIRPORT IN KERALA From table 6, the maximum competitiveness of each airport is obtained and to find out the most competitive airport, the relative Euclidean distance is calculated. The relative Euclidean distance is defined in terms of a metric distance of A from any of the nearest crisp sets. This distance is used to measure each airport s competitiveness. The airport having the shortest distance is the most competitive one. With reference to the present study, the concept relative Euclidean distance (δ j ) briefly can be explained as the average of the square root of the sum of the squares of the difference between the ideal compatibility function and the maximum compatibility function for the competitiveness of the airport and it can be defined as: 1 δ j = [ [ ] ] for j = 1, 2, 3 where n is the number of elements in the universe of discourse R * (y) is the ideal compatibility function in terms of the linguistic variable of highly superior for an airport j R jmax (y) is the maximum compatibility function for the competitiveness of the airport j. After calculating the maximum competitiveness of each of the three selected airports in step 4, the relative Euclidean distance (δ j ) was calculated to measure each airport s competitiveness. From the values of δ j, the most competitive airport is selected based on 2993

8 shortest distance. Values of R*(y) and the calculated values of relative Euclidean distance are given in table 6. Relative Euclidean distance and the corresponding ranking of the three international airports in Kerala is given table 7 Table 7: Relative Euclidean Distance of three airports B δ j Calicut Cochin TVM In the table 7, B1 is the value of the ideal compatibility function in terms of the linguistic variable of highly superior for an airport j. Relative Euclidean distance is calculated using the maximum values of the three matrices R1, R2 and R3. Relative Euclidean distance Daten 0,1000 0,0800 Calicut, ,0600 0,0400 Trivandrum, ,0200 0,0000 Cochin, ,5 1 1,5 2 2,5 3 3,5 Figure 1: Relative Euclidean distance of three airports Table 8: Ranking of the three airports based on the relative Euclidean distance Rank Airport Relative Euclidean Distance 1 Cochin Trivandrum Calicut Table8 shows that CIAL is having the shortest relative Euclidean distance of followed by Trivandrum with a value of and Calicut with CONCLUSION CIAL is having minimum relative Euclidean distance in terms of the compatibility function for the competitiveness of the airport; it is ranked as the most competitive airport in Kerala. Since CIAL is the first Greenfield airport in the country under public private 2994

9 participation and the result of the study may be considered as a pointer towards the equity structure of future airports in India. 6. REFERENCES [1] Airbus Industries, Global Market Forecast ( ), Toulouse (2010) [2] Airport Authority of India Traffic figures [3] Airport Council International, Quarterly Bulletin, Q1 (2011) [4] Bellman, R.E, Zadeh, L.A., Decision -making in a fuzzy environment, Management Service, 17,pp (1970) [5] Chou,C.C. A Model for the evaluation of airport service quality, Transport 162, pp (2009) [6] Cochin International airport limited, (CIAL), Annual reports ( ) [7] David Gillen, Ashish Lall, Some competitive advantages for low cost carriers: Implications for airports, Journal of air Transport Management, 10 (1), pp41-50 (2003) [8] Garry S Becker (1965): A Theory of the Allocation of Time -The Economic Journal, 75 (299), pp (1965) [9] ICAO, Annexes to Convention on International Civil Aviation, Montreal (2006) [10] IMD world competitiveness year book, pp 111(2011) [11] Karwowski, Mital, Potential Applications of Fuzzy sets in Industrial Safety Engineering, Fuzzy Sets and systems, vol.19, pp (1986) [12] Kaufmann, A. Introduction to the theory of Fuzzy Sets; Vol.1, Academic Press, Newyork (1975) [13] Kerala Economic Review, Govt. of Kerala (2010) [14] Park, Y, Applications of a fuzzy linguistic approach to analyse Asian airports competitiveness, Transportation Planning and Technology, 20, pp (1997) [15] Park Y, An Analysis of competitive strength of Asian major airports, Journal of Air Transport Management, 9, pp (2003) [16] Porter, M.E., Competitive Strategy Techniques for analyzing Industries and Competitors, Free press, New York (1980) [17] Sanchez, E Resolution of Composite Fuzzy relation equations, Information and Control, 30, pp38-48 (1976) [18] Schmucker, Fuzzy Sets, Natural Language Computations and Risk Analysis, Computer Science Press, Rockville, Maryland (1983) [19] Senguttavan, P.S., Indian Airport Competitiveness and its efficiency, key strategy to develop an airport hub, AIRNETH-Iffad seminar (2011) [20] Senguttavan, P.S, Air Cargo: Engine for Economic Growth and Development a Case Study of Asian Region, METRANS 2006 [21] Tsaur, S.H, Chang, T.Y and Yen C.H, The Evaluation of airline service quality by fuzzy MCDM, Tourism Management, 23, (2002) [22] Yeh, C.H. and Kuo, Y.L.Evaluating passenger service of Asia-Pacific international airports, Transportation Research Part E, 39, (2003) [23] Zadeh, L.H, Fuzzy sets, Information and Control, 8, pp (1965) [24] Zimmerman, H.J., Fuzzy Set Theory and its applications, 2nd Edition, Kluwer Academic, Boston (1991) 2995