APP下载

Evaluation on Urban Tourism Competitiveness of Beibu Gulf Rim Based on the Entropy Approach

2019-11-20YafenHUANG1HuaQUAN

Asian Agricultural Research 2019年10期

Yafen HUANG1,2, Hua QUAN

1. School of Life Science and Technology, Lingnan Normal University, Zhanjiang 524000, China; 2. Macao University of Science and Technology, Macao 999078, China; 3. School of Tourism & Event Management, Shanghai University of Internal Business and Economics, Shanghai 200000, China

Abstract The tourism competitiveness of the cities around the Beibu Gulf was studied using the entropy method from the perspective of the 14 indexes of tourism current competitiveness, tourism support competitiveness and tourism potential competitiveness. The results show that the cities of Sanya, Nanning and Haikou have the stronger competitiveness; the cities of Zhanjiang, Beihai, Chongzuo and Maoming have moderate competitiveness; and the cities of Yulin, Qinzhou, Yangjiang and Fangchenggang have the weak competitiveness. According to the analysis of the index weight and comprehensive ranking, almost each aspect contributes to the city competitiveness. The index weights of the tourism current competitiveness, tourism support competitiveness and tourism potential competitiveness are 0.272 22, 0.393 8 and 0.333 98, respectively. Based on the niche theory, the article put forward the strategies: dislocation development, balanced development and regional tourism competition and cooperation development.

Key words Entropy approach, Urban agglomeration in Beibu Gulf Rim, Urban tourism competitiveness, Niche theory

1 Introduction

From the development to the maturity stage of the city, urban agglomeration is the highest structural organization form. It is a large, multi- core, multi- level urban cluster formed by a number of cities. It is a consortium of metropolitan areas. The Beibu Gulf urban agglomeration is a state- level urban agglomeration approved by the State Council on January 20th, 2017. The region in China includes 11 cities: Zhanjiang, Maoming and Yangjiang of Guangdong Province, Nanning, Qinzhou, Beihai, Fangchenggang, Yulin and Chongzuo of Guangxi Province, and Haikou and Sanya of Hainan Province. The Beibu Gulf urban agglomeration is an important part of the China- ASEAN (Association of South East Asian Nations) Free Trade Area. With the development of China’s foreign trade, the Beibu Gulf urban agglomeration has important policy support background in economy, trade and tourism.

As an important part of China’s national economy, tourism has an important significance for the improvement of urban competitiveness, and has a positive effect on urban communication and urban innovation. Urban tourism competitiveness refers to the competitive advantage of a city in terms of tourism. It is not only the result of tourism development, but also the internal driving force of tourism development. Urban tourism development is divided into three stages: natural growth stage, competitive growth stage and win- win growth stage[1]. After years of development, urban tourism is currently in a competitive growth stage. Therefore, each city is enhancing tourism competitiveness, and it is particularly important to evaluate and analyze the city’s tourism competitiveness.

This research is trying to evaluate the urban tourism competitiveness of Beibu Gulf Rim, and put forward some strategies to promote the urban tourism development.

2 Literature review

Previous literature has studied a lot about the tourism competitiveness. Early studies focus on the attributes of the tourism competitiveness[2], later on, the research on the categories about the competitive and the evaluation system become popular. In the system, there are a lot of problems. Firstly, there are mainly two theories for the system, Ricardian comparative advantage theory and Porterian competitive advantage framework. Because of the different theories, the concepts of tourism competitiveness are different[3-4]. In the first theory, the competitiveness is relative, but in the second one, the competitiveness is based on the owned resources. Secondly, different scholars have proposed different evaluating systems[5-6]. Mendola and Volo evaluated several systems and put forward that the system should be comparable, standardized and high- quality[5]. Because of the differences in the standard and conception, Navais, Ruhanen and Arcodia studied the destination competitiveness from the phenomenographic aspect[7]. Ivanov and Webster pointed that in the study of tourism competitiveness that the globalization should be considered[8]. The evaluation subjects could be countries[9-12], regions, and even certain destinations[13-17]. And in the adaption of methodology, recent efforts have been geared towards the use of composite indicators to measure competitiveness, as these better reflect its multifaceted and holistic nature[18], such as the techniques of TOPSIS[19], PRPMETHEE[7]and IPA[20]. Most of these methodologies propose either an individual assessment or a comparative ranking system[19, 21].

From the end of the 1980s, researchers started to consider the city tourism competitiveness from multi- dimensional perspective[22], but now there isn’t generally acknowledged concept and connotation of the urban tourism competitiveness, and it is vague in academic expression. The foreign researchers focus on two aspects about the urban tourism competitiveness: evaluation model and evaluation methods[21]. Cibinskiene pointed the conceptual model of the urban tourism competitiveness, including internal and external environment[23]. Cinbinskiene and Snieskiene studied the urban tourism competitiveness with quantitative and qualitative method[24]. Pike and Mason evaluated the destination competitiveness through brand position[25]. Santos, Ferreira and Costa studied the factors influencing the competitiveness in the mature tourism destination[26]. Globalization[8], sustainability[27]and potential of management networks[28]are mentioned in the competitiveness.

Domestic scholars have conducted deep research on the urban tourism competitiveness, and the trend is more quantitative, diversified and integrated[29]. The main research focus on two aspects: research content and scholar argue about the evaluation indicators of tourism competitiveness. Guo evaluated the regional tourism competitiveness from tourism competitive strength, tourism competitive potential and future development of tourism competitiveness[30]. Su Weizhongetal.[31]constructed the evaluation indicators from tourism competitive performance, tourism competitive potential, tourism environment support and tourism comprehensive competitiveness. Ding Leietal.[32]put forward indicators from hard and soft competitiveness. Chen Xiao & Li Yuezheng[33]evaluated the urban tourism competitiveness from tourism environment support, tourism performance and tourism development potential using the quantitative method. Dong Yuncai & Wu Jun[34]constructed the urban tourism competitive evaluation indicators from tourism current competitiveness, tourism environment support competitiveness and tourism potential competitiveness. The research subjects of urban tourism competitiveness could be single cities, cities within a province[6, 35], and even cities within a region[36-38]. The theories are dominated by ecologic niche theory[39], competitive factor and spatial pattern[40]. The research methods are mainly quantitative methods, such as principal component analysis[41], clustering methodology[42], analytic hierarchy process[43], entropy approach[44]and system model[45].

According to the literature review, it could be concluded that there are rare studies about the Beibu Gulf Rim, and there are a lot of evaluation systems. On the basis of the former studies, the article evaluated the urban tourism competitiveness with entropy approach. Based on the niche theory, the article put forwards the strategies: dislocation development, balanced development and regional tourism competition and cooperation development.

3 Research method

3.1 Data sourceThe data is selected from China City Statistical Yearbook 2017, China Tourism Spot Development Report 2017, 2017 statistical bulletin of each province on national economic and social development, and various local tourism administration networks.

3.2 Evaluation index system and descriptionThis article comprehensively considers the accessibility of evaluation data. In terms of principles of scientific, measurable and operatable, the research constructs three levels. Level I refers to the urban tourism competitiveness index. Level II has three components, and Level III consists of the available indicators. Table 1 is the evaluating system.

Table 1 The evaluation system[31]

Level ILevel IILevel IIIUTCTCCDomestic tourists (x1)∥106 person-timesInternational tourists (x2)∥106 person-timesDomestic income (x3)∥109 yuanTotal tourism income (x4)∥109 yuanTSCPer capita green area (x5)∥km2Green area coverage in built-up areas (x6)∥%Added value of the tertiary industry as a share of GDP (x7)∥%Per capita GDP (x8)∥yuanNumber of scenic spots above 3A (x9)Number of tourist agency (x10)Number of star-hotels (x11)TPCNumber of students in higher education schools per 10 000 people (x12)Annual passenger traffic (x13)∥106 person-timesTourism revenue as a share of GDP (x14)∥%

Note: UTC, urban tourism competitiveness; TCC, tourism current competitiveness; TSC, tourism support competitiveness; TPC, tourism potential competitiveness.

3.3 Entropy approachThe entropy method is a mathematical method used to determine the degree of dispersion of an indicator. The greater the degree of dispersion, the greater the impact of the indicator on the overall evaluation. The entropy value can be used to determine the degree of dispersion of an indicator. In information theory, entropy is a measure of uncertainty. The larger the amount of information, the smaller the uncertainty and the smaller the entropy; the smaller the amount of information, the greater the uncertainty and the greater the entropy. According to the characteristics of entropy, we can judge the randomness and degree of disorder of an event by calculating the entropy value. The entropy value can also be used to judge the degree of dispersion of an index. The greater the degree of dispersion of the index, the greater the impact on the comprehensive evaluation of the index.

The entropy method is an objective weighting method. According to the degree of variation of each index, the information entropy is used to calculate the weight of each index, which provides a basis for comprehensive evaluation of multiple indicators. The competitiveness of urban tourism is in line with the conditions of this method. The specific steps are as follows:

(i)xij(i=1, 2,…,n;j=1, 2,…,m) means the score of one city under each indicator.nis the number of cities, andmis the number of indicators.

(ii) Normalization of indicators. Since the positive indicator and the negative indicator value represent different meanings (the higher the positive indicator value and the lower the negative indicator value, the better). Therefore, for the high and low indicators, we use different algorithms for data standardization. The specific method is as follows.

Positive indicator:

(1)

Negative indicator:

(2)

Measurement of weight:

(3)

Calculation of entropy:

(4)

Calculation of information entropy redundancy:

dj=1-ej

Calculation of weight of each indicator:

(5)

Calculation of composite score for each city:

(6)

4 Calculation process of entropy approach

4.1 Homogeneous processing of original data and heterogeneous indexesIn this paper, the relevant statistical data for 2017 are shown in Table 2. Because of the different meanings of the data and the lack of dimensions that can be calculated uniformly, the original data must be homogenized by heterogeneous indexes, and the formulas (1) and (2) must be used synthetically. The results are shown in Table 3.

Table 2 Basic data in 2017

IndexYLCHZBHFCHGNNQZHMMYJZHJHKSYx13 989.882 539.483 069.802 016.3511 001.082 564.301 091.872 079.064 305.902 427.661 761.69x213.6740.3114.5417.6659.136.884.457.0244.5318.1969.28x3415.49234.67364.51164.831 109.80252.68314.24264.73414.16225.36370.35x4419.62244.93406.09169.101 127.51254.55328.33267.61421.42265.99406.17x510.4310.639.9013.5612.1010.5115.7013.0812.8111.5013.02x633.6437.8837.8830.4043.2537.6043.2543.0240.4237.2042.95x740.5036.1630.1031.1651.3534.3544.1041.7842.6477.2867.30x823 46843 67874 37879 35154 41931 87547 44355 55338 74461 58389 370x91514141037167681015x10321361911867193645246255x1131631513552131x1223.75214.85295.2977.85499.1050.6051.2557.36184.88677.35833.69x13979.00109.002 313.00846.189 244.531 532.106 515.001 500.009 793.009 600.002 452.29x1424.6926.9933.0222.8027.3719.4311.2219.0014.9219.1376.74

Note: YL, Yulin; CHZ, Chongzuo; BH, Beihai; FCHG, Fangchenggang; NN, Nanning; QZH, Qinzhou; MM, Maoming; YJ, Yangjiang; ZHJ, Zhanjiang; HK, Haikou; SY, Sanya. The same as follows.

Table 3 Heterogeneous index homogenization processing value

IndexYLCHZBHFCHGNNQZHMMYJZHJHKSYx10.362 680.230 840.279 050.183 291.000 000.233 100.099 250.188 990.391 410.220 670.160 14 x20.197 320.581 840.209 870.254 910.853 490.099 310.064 230.101 330.642 750.262 561.000 00 x30.374 380.211 450.328 450.148 521.000 000.227 680.283 150.238 540.373 180.203 060.333 71 x40.372 170.217 230.360 170.149 981.000 000.225 760.291 200.237 350.373 760.235 910.360 24 x50.664 330.677 070.630 570.863 690.770 700.669 431.000 000.833 120.815 920.732 480.829 30 x60.777 800.875 840.875 840.702 891.000 000.869 361.000 000.994 680.934 570.860 120.993 06 x70.524 070.467 910.389 490.403 210.664 470.444 490.570 650.540 630.551 761.000 000.870 86 x80.262 590.488 730.832 250.887 890.608 920.356 660.530 860.621 610.433 520.689 081.000 00 x90.405 410.378 380.378 380.270 271.000 000.432 430.189 190.162 160.216 220.270 270.405 41 x100.125 490.050 980.239 220.035 290.462 750.262 750.074 510.141 180.176 470.964 711.000 00 x110.096 770.032 260.193 550.096 770.483 870.032 260.096 770.161 290.161 290.677 421.000 00 x120.028 490.257 710.354 200.093 380.598 660.060 690.061 470.068 800.221 760.812 471.000 00 x130.099 970.011 130.236 190.086 410.943 990.156 450.665 270.153 171.000 000.980 290.250 41 x140.321 740.351 710.430 280.297 110.356 660.253 190.146 210.247 590.194 420.249 281.000 00

4.2 Entropy value of calculated indexesAccording to formula (4), the entropy value of each index was calculated, and the results are shown in Table 4.

4.3 Determination of weightAccording to formulas (5) and (6), the weight of each index was determined (Table 5).

Table 4 Entropy values of the cities

IndexYLCHZBHFCHGNNQZHMMYJZHJHKSYx10.108 280.068 920.083 310.054 720.298 560.069 590.029 630.056 420.116 860.065 880.047 81x20.046 240.136 340.049 180.059 730.199 990.023 270.015 050.023 740.150 610.061 520.234 32x30.100 580.056 810.088 240.039 900.268 660.061 170.076 070.064 090.100 260.054 560.089 66x40.097 330.056 810.094 190.039 220.261 520.059 040.076 160.062 070.097 750.061 700.094 21x50.078 280.079 780.074 300.101 770.090 810.078 880.117 830.098 170.096 140.086 310.097 72x60.078 690.088 610.088 610.071 110.101 170.087 960.101 170.100 630.094 550.087 020.100 47x70.081 530.072 800.060 600.062 730.103 380.069 150.088 780.084 110.085 840.155 580.135 49x80.039 120.072 810.123 990.132 280.090 720.053 140.079 090.092 610.064 590.102 660.148 98x90.098 680.092 110.092 110.065 790.243 420.105 260.046 050.039 470.052 630.065 790.098 68x100.035 520.014 430.067 700.009 990.130 970.074 360.021 090.039 960.049 940.273 030.283 02x110.031 910.010 640.063 830.031 910.159 570.010 640.031 910.053 190.053 190.223 400.329 79x120.008 010.072 440.099 560.026 250.168 280.017 060.017 280.019 340.062 330.228 370.281 09x130.021 810.002 430.051 530.018 850.205 960.034 130.145 150.033 420.218 180.213 880.054 64x140.083 610.091 400.111 810.077 210.092 680.065 800.037 990.064 340.050 520.064 780.259 86

Table 5 The weight of each index

IndexWeightIndexEntropy weightTCC0.272 22x10.070 962 242x20.100 543 959x30.051 836 772x40.048 883 913TSC0.393 80x50.002 971 094x60.001 806 082x70.014 799 277x80.021 085 054x90.044 888 684x100.149 067 249x110.159 179 326TPC0.333 98x120.149 748 518x130.135 369 059x140.048 858 770

4.4 Comprehensive score of the citiesThe comprehensive score of the cities was calculated, and the results are shown in Table 6.

5 Calculation and suggestions

As far as the competitiveness index weight of urban agglomeration around Beibu Gulf is concerned, the weights of tourism current competitiveness, tourism support competitiveness and tourism potential competitiveness are 0.272 22, 0.393 8 and 0.333 98, respectively. This is demonstrated that the contribution rate of each index to the competitiveness of urban tourism is equal. Therefore, for the tourism competitiveness of a city, it should not only pay attention to the present development, but also pay attention to the sustainable development potential in the future.

For a city, its tourism competitiveness may not fully reflect its tourism competitiveness, such as Haikou. Haikou is the central city of Hainan Province, and the tourism resources in Hainan Province are very rich. In order to better admire the sea resources, many tourists just regard Haikou as a transit station and go to other cities for hydrophilic experience. Therefore, it is found that although Haikou has a strong overall urban tourism competitiveness, but its tourism reception number and tourism income and other indicators are in the moderate, but its passenger transport volume is very large.

Table 6 Rank of comprehensive score of the cities

CityTCCScoreRankTSCScoreRankTPCScoreRankOverallScoreRankSY0.036 2120.104 7310.062 1820.203 121NN0.068 0110.059 7530.057 6130.185 362HK0.016 7170.084 1020.066 3210.167 123ZHJ0.033 4130.021 3650.041 3440.096 114BH0.020 0460.028 2840.027 3550.075 665CHZ0.024 3240.010 99110.015 6470.050 956MM0.011 28110.013 8190.024 0960.049 187YL0.022 3050.017 2180.008 24110.047 758QZH0.013 3490.020 0460.010 3990.043 779YJ0.012 75100.019 8770.010 5680.043 1810FCHG0.013 8780.013 67100.010 25100.037 8011

In terms of the comprehensive score of urban tourism competitiveness, the Beibu Bay urban cluster can be divided into three categories: Sanya, Nanning and Haikou belong to the first category, and it has strongest tourism competitiveness; Zhanjiang, Beihai, Chongzuo and Maoming have medium competitiveness, and in particular, Zhanjiang has better stability of system indicators at this level, which is also the basis for Zhanjiang to become a sub- central city of Guangdong Province and bridgehead of ASEAN countries. However, the overall tourism competitiveness of Yulin, Qinzhou, Yangjiang and Fangchenggang is relatively weak. In this level of cities, Yulin’s tourism competitiveness is still relatively strong, but because of its weak support competitiveness and potential competitiveness, its comprehensive score is not high. So, in order to improve the overall competitiveness of urban tourism, we should not only have realistic tourism resources (the number of scenic spots and star hotels), but also improve the comprehensive supporting facilities of the city, including the coordinated development of education, environment and so on.

In order to realize the overall coordinated development of urban agglomeration around Beibu Gulf, this paper puts forward the following strategies.

(i) Strategies of misalignment management. Misplaced management refers to the management mode in which each city in the urban agglomeration is managed according to its own competitive advantages, according to the principles of niche separation, misplaced management, tacit interdependence and complement each other. The idea of misplaced management can be divided into two categories. One is the dislocation of the target customer base. Although the Beibu Gulf urban agglomeration is in the south of the motherland, the resource conditions are very similar, but also because of the geographical location, it can be divided into different markets in tourism marketing, such as Sanya, Haikou and Nanning cities, because of its convenient aviation conditions and the support of national policy, in addition to the publicity of the domestic market, we should increase the recruitment of international tourists and actively expand the international market. The other one is misplacement of propaganda. Hainan is the place of winter rest in our country. Tourists from home and abroad pour into Hainan one after another every winter. Under the existing traffic conditions, it is very easy to have traffic paralysis and affect the quality of tourism. Zhanjiang, Yangjiang, Maoming and other places, as transit stations in Guangdong and Hainan, can be used as the best choice for winter rest places in mainland China because of similar resource conditions and relatively convenient traffic conditions. Therefore, tourism related departments and tourism enterprises can highlight different selling points in different cities when carrying out tourism propaganda. The core idea of misplaced management is to avoid collision, reduce homogenization, complement each other and coordinate with each other.

(ii) Applying the principle of balanced development. The competitiveness of urban tourism is a complicated system, involving many indicators. Each index must be developed in a balanced manner, otherwise it will reduce the overall competitiveness of the city. This is just like tourists’ impression of the city coming from their overall experience in the city, and if one aspect of the experience is bad, it is bound to affect the overall tourism experience. For any city, we should not only start from the existing tourism resources and give full play to our advantages, but also strengthen our advantages from the aspects of tourism support, such as the improvement of hotel service quality, the improvement of scenic spot service quality and so on. For the cities with weak competitiveness, such as Yangjiang and Maoming, we should find the points with weak competitiveness to make targeted adjustments in order to improve the tourism competitiveness of the city.

(iii) Improving the tourism development of urban agglomeration around Beibu Gulf with regional tourism competition. Regional tourism is an overall image and a high degree of consumer identity. Because of the close geographical location, the urban agglomeration around Beibu Gulf has bred similar tourism resources, has the potential of alliance and market conditions, and can shape the tourism image of regional integration. At the same time, under the guidance of the national policy, among the many urban agglomerations, the Beibu Gulf urban agglomeration should seize the opportunity, enhance the overall competitiveness of the urban agglomeration, and guide the development of tourism with the concept of region. Regional tourism concurrence development, as a new economic concept, needs the support of the government in order to develop and perfect gradually. ① The urban agglomeration around Beibu Gulf involves three provinces and 11 municipal units. Local governments should dilute the concept of administrative division, play their own guiding role, coordinate with each other to formulate regional tourism development strategy, and provide external environment support for its development. ② Cities at different levels of development should have different development strategies. For example, Sanya, Nanning and Haikou, as the core areas of the urban agglomeration around Beibu Gulf, should constantly improve the attractiveness of tourism, overcome the obstacles of tourism development, make use of their own advantages to promote the tourism development of other regions, and give full play to their core radiation driving role. Compared with the first level of Sanya, Haikou and Nanning, the second level of cities is far from enough in terms of tourism popularity. For example, few northern tourists know these cities, such as Maoming and Zhanjiang, can open up the domestic tourist market, create the southernmost winter rest place in mainland China, and make use of the current low house prices and convenient transportation conditions to attract more domestic tourists; Beihai is already well known and should optimize its tourism products and improve the quality of scenic spots and hotels in order to maintain its existing market share. On the other hand, the marginal cities with weak competitiveness should adopt the characteristic strategy, dig deeply the resources with rich cultural connotation of the city, adapt to the local conditions, subdivide the market, strengthen the marketing for the subdivision market, enlarge and refine the market segmentation, grasp the regional economic integration, strengthen the cooperation with the central city and the second- level city, perfect the tourism infrastructure and reception facilities, and learn from the experience of the high- level urban tourism development step by step to the second level.

As an important national urban agglomeration in China, Beibu Gulf urban agglomeration covers more provinces and more cities, so it needs the coordination of the state and governments at all levels. Generally speaking, the tourism competitiveness of the urban agglomeration around Beibu Gulf is relatively high, but the level of tourism competitiveness of each city is uneven. Each city should give full play to its advantages, learn from each other and highlight its characteristics, so as to realize the linkage and coordinated development of the urban agglomeration.