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August & September  2019, 12(4&5): 771-782. doi: 10.3934/dcdss.2019051

Tourism destination competitiveness evaluation in Sichuan province using TOPSIS model based on information entropy weights

Tao Gu , Peiyu Ren , Maozhu Jin , and  Hua Wang

Business School, Sichuan University, Chengdu, Sichuan 610064, China

* Corresponding author: Maozhu Jin, E-mail: jinmaozhu@scu.edu.cn

Received  June 2017 Revised  December 2017 Published  November 2018

This study applied the combined methods of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Information Entropy Weights to evaluate the tourism destination competitiveness (TDC) of 13 cities in Sichuan Province. In the empirical study, IEW was used to determine the subjective weights of four aspects and 26 evaluation indexes, which have the influence on TDC. In addition, applying the essential ideas of TOPSIS, chosen alternative should have the shortest geometric from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS), to conduct a comprehensive evaluation and sort-based analysis. In the end, the essay arranged the TDC of 13 cities in Sichuan Province from high to low, then produced policy recommendations. The results represent that IEW & TOPSIS were an efficient and effective way to evaluate TDC.

Keywords: Information entropy weight, technique for order preference by similarity to ideal solution, tourism destination competitiveness, Sichuan Province.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.
Citation: Tao Gu, Peiyu Ren, Maozhu Jin, Hua Wang. Tourism destination competitiveness evaluation in Sichuan province using TOPSIS model based on information entropy weights. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 771-782. doi: 10.3934/dcdss.2019051
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show all references

References:
[1]

J. M. Amigó and Á. Giménez, Applications of the min-max symbols of multimodal maps, Applied Mathematics and Nonlinear Sciences, 1 (2016), 87-98.   Google Scholar

[2]

F. Balibrea, On problems of Topological Dynamics in non-autonomous discrete systems, Applied Mathematics and Nonlinear Sciences, 1 (2016), 391-404.   Google Scholar

[3]

D. Buhalis, Marketing the competitive destination of the future, Tourism Management, 21 (2000), 97-116.   Google Scholar

[4]

R. Couch, Destination competitiveness: ExploringFoundations for a long term research program, Proceedings of the Administrative Sciences Association of Canada 1994. Annual Conference 1994. Google Scholar

[5]

A. M. d'Hauteserre, Lessons in managed destination competitiveness: The case of foxwoods casino resort, Tourism Management, 21 (2000), 23-32.   Google Scholar

[6]

L. Dwyer, T. H. P. Forsyth and P. Rao, The price competitiveness of travel and tourism: A comparison of 19 destinations, Tourism Management, 21 (2000), 9-22.   Google Scholar

[7]

M. J. Enright and J. Newton, Determinants ofTourism destination competitiveness in asia pacific: Comprehensiveness and universality, Journal of TravelResearch, 2005. Google Scholar

[8]

N. Gooroochurn and G. Sugiyarto, Competitiveness indicators in the travel and tourism industry, Tourism Economics, 11 (2005), 25-46.   Google Scholar

[9]

N. Gooroochurn and G. Sugiyarto, Competitiveness indicators in the travel and tourism industry, Tourism Economics, 11 (2005), 25-46.   Google Scholar

[10]

S. S. Hassan, Determinants of market competitiveness in an environmentally sustainable tourism industry, Journal of Travel Research, 38 (2000), 239-245.   Google Scholar

[11]

C. L. Hwang and K. Yoon, Multiple Attribute Decision Making: Method and Application, Springer-Verlag, Berlin-New York, 1981.  Google Scholar

[12]

S. S. Kim, Y. Z. Guo and J. Agrusa, Preference and positioning analyses of overseas destinations by mainland chinese outbound pleasure tourists, Journal of Travel Research, 44 (2005), 212-220.   Google Scholar

[13]

M. Kozak and M. Rimmington, Measuring tourist destination competitiveness: Conceptual considerations and empirical findings, Hospitality Management, 18 (1999), 273-283.   Google Scholar

[14]

M. Kozak and M. Rimmington, Measuring tourist destination competitiveness: Conceptual considerations and empirical findings, Hospitality Management, 18 (1999), 273-283.   Google Scholar

[15]

C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal, 27 (1948), 379-423,623-656.  doi: 10.1002/j.1538-7305.1948.tb01338.x.  Google Scholar

[16]

D. Larry and K. Chulwon, Destination Competitiveness: Determinants and Indicators, Current Issues in Tourism, 2003. Google Scholar

[17]

K.-L. Wang and C.-S. Wu, A Study of Competitiveness of International Tourism in the South East Asian Region, Eleventh Annual East Asian Seminar on Economics: Trade in Services, June 22-24, 2000, Seoul, Korea. Google Scholar

[18]

Y. Yoon, Development of a Structural Model for Tourism Destination Competitiveness from Stakeholders Perspectives, 2002. Google Scholar

Figure 1.  weight of indexes in TDC
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Table 1.  Data of tourism resources in Sichuan Province
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Table 2.  Data of tourism capacity in Sichuan Province
CityNumber of travel agency (units)Number of star-reated hotels (units)Civilian-owned value added of tertiary industry (%)
Chengdu462128109.4
Guangyuan1420108.6
Luzhou2723109.2
Leshan7533109.9
Mianyang6130110.0
Nanchong4926110.6
Aba3419110.5
Yibin2715110.1
Dazhou2710110.4
Ya'an1221112.3
Ganzi209107.2
Suining1322119.2
Zigong218109.4
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CityNumber of travel agency (units)Number of star-reated hotels (units)Civilian-owned value added of tertiary industry (%)
Chengdu462128109.4
Guangyuan1420108.6
Luzhou2723109.2
Leshan7533109.9
Mianyang6130110.0
Nanchong4926110.6
Aba3419110.5
Yibin2715110.1
Dazhou2710110.4
Ya'an1221112.3
Ganzi209107.2
Suining1322119.2
Zigong218109.4
Table 3.  Data of tourism industrial strength in Sichuan Province
CityNumber of domestic tourist arrivals (10000 person times)Domestic tourist income (100 million yuan)Number of international tourist arrivals (10000 preson times)International tourist income (UAD 10000)
Chengdu18423.021616.95197.885768.11
Guangyuan2769.43158.690.1840.96
Luzhou2539.512539.510.2257.31
Leshan3342.14383.912.794656.25
Mianyang2821.12277.130.74212.25
Nanchong3076.5253.280.2571.07
Aba2861.34240.7815.442852.81
Yibin2822.24256.020.1763.21
Dazhou1351.0190.360.2999.29
Ya'an1658.91108.60.37102.92
Ganzi792.6679.036.251799.29
Suining2432.79201.090.72148.95
Zigong2106200.380.1652.5
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CityNumber of domestic tourist arrivals (10000 person times)Domestic tourist income (100 million yuan)Number of international tourist arrivals (10000 preson times)International tourist income (UAD 10000)
Chengdu18423.021616.95197.885768.11
Guangyuan2769.43158.690.1840.96
Luzhou2539.512539.510.2257.31
Leshan3342.14383.912.794656.25
Mianyang2821.12277.130.74212.25
Nanchong3076.5253.280.2571.07
Aba2861.34240.7815.442852.81
Yibin2822.24256.020.1763.21
Dazhou1351.0190.360.2999.29
Ya'an1658.91108.60.37102.92
Ganzi792.6679.036.251799.29
Suining2432.79201.090.72148.95
Zigong2106200.380.1652.5
Table 4.  Data of economic support ability
CityGDP per capita (yuan)Total investment in fix asset (100 million yuan)Passenger-kilometers of highways (10000 passenger-km)Possession of civil motor vehicles (10000 units)Total lengh of highways (km)
Chengdu700196620.371231084312.822789
Guangyuan22117561.7418354814.019520
Luzhou296551181.0364148221.013516
Leshan37125863.9124743222.311658
Mianyang335581080.3733684837.819887
Nanchong226391244.5446694227.722446
Aba27043383.3425459910.413218
Yibin323181130.2633082719.218301
Dazhou244111176.2024136518.119510
Ya'an30052471.4010668911.86286
Ganzi18096465.721429987.429584
Suining24691913.6820142713.38805
Zigong39145597.6118336413.86456
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CityGDP per capita (yuan)Total investment in fix asset (100 million yuan)Passenger-kilometers of highways (10000 passenger-km)Possession of civil motor vehicles (10000 units)Total lengh of highways (km)
Chengdu700196620.371231084312.822789
Guangyuan22117561.7418354814.019520
Luzhou296551181.0364148221.013516
Leshan37125863.9124743222.311658
Mianyang335581080.3733684837.819887
Nanchong226391244.5446694227.722446
Aba27043383.3425459910.413218
Yibin323181130.2633082719.218301
Dazhou244111176.2024136518.119510
Ya'an30052471.4010668911.86286
Ganzi18096465.721429987.429584
Suining24691913.6820142713.38805
Zigong39145597.6118336413.86456
Table 5.  Data of eco-environment support ability in Sichuan Province
CityAir quality indexRate of forest coverage (%) Public green area per capita (sq.m)
Chengdu8.9538.413.5
Guangyuan5.9955.311.2
Luzhou6.05509.01
Leshan755.479.03
Mianyang6.7952.49.63
Nanchong639.811.6
Aba2.9824.955.5
Yibin6.5844.213.08
Dazhou7.6641.511.7
Ya'an6.2663.113.08
Ganzi3.8333.049
Suining4.939.016.8
Zigong7.6933.711.3
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CityAir quality indexRate of forest coverage (%) Public green area per capita (sq.m)
Chengdu8.9538.413.5
Guangyuan5.9955.311.2
Luzhou6.05509.01
Leshan755.479.03
Mianyang6.7952.49.63
Nanchong639.811.6
Aba2.9824.955.5
Yibin6.5844.213.08
Dazhou7.6641.511.7
Ya'an6.2663.113.08
Ganzi3.8333.049
Suining4.939.016.8
Zigong7.6933.711.3
Table 6.  Ranking of tourism resources in Sichuan province and the value of $ d^+ $, $ d^- $, $ c_i $
$ d^+ $ $ d^- $ $ C_i $Rank
1Chengdu0.5508140.6694860.5486242
2Guangyuan0.6217540.4220170.4043205
3Luzhou0.8508050.3311030.2801439
4Leshan0.6636120.4374010.3972727
5Mianyang0.5918470.5781720.4941563
6Nanchong0.7692320.5206040.4036206
7Aba0.4775720.6979940.5937521
8Yibin0.8342140.2937770.26044310
9Dazhou0.8616760.2973500.25655211
10Ya'an0.8344640.3508780.2960148
11Ganzhi0.7205490.5171630.4178384
12Suining0.9119160.1547740.14509713
13Zigong0.8981650.2510980.21848612
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$ d^+ $ $ d^- $ $ C_i $Rank
1Chengdu0.5508140.6694860.5486242
2Guangyuan0.6217540.4220170.4043205
3Luzhou0.8508050.3311030.2801439
4Leshan0.6636120.4374010.3972727
5Mianyang0.5918470.5781720.4941563
6Nanchong0.7692320.5206040.4036206
7Aba0.4775720.6979940.5937521
8Yibin0.8342140.2937770.26044310
9Dazhou0.8616760.2973500.25655211
10Ya'an0.8344640.3508780.2960148
11Ganzhi0.7205490.5171630.4178384
12Suining0.9119160.1547740.14509713
13Zigong0.8981650.2510980.21848612
Table 7.  The ranking of $ d^+ $, $ d^- $, $ c_i\sum $ and $ c_i $ of Sichuan province
d+d-CiRank
Chengdu0.5486220.6759210.8210310.8775310.6890320.063540.963470.938131
Guangyuan0.4043250.08201120.0521380.2308480.6676730.747360.434340.367556
Luzhou0.2801490.11224100.3915920.2613350.53443100.665400.401010.376043
Leshan0.3972770.1884340.1008930.1873590.6209960.710110.424630.374214
Mianyang0.4941630.1727150.0657160.2900140.6183470.679700.494560.421172
Nanchong0.4036260.1727060.0676750.3142330.5529890.696640.415200.373445
Aba0.5937510.1477070.0758040.15504110.00000130.809580.466070.365367
Yibin0.26044100.1246090.0629370.2607560.6648440.772530.372810.325509
Dazhou0.25655110.1302980.01438130.2486270.6443250.795960.3602166160.3115610
Ya'an0.2960180.2057630.02281110.08964120.7775310.797060.4353911420.353278
Ganzhi0.4178440.01219130.01869120.3398420.28915110.792620.3415127830.30112139911
Suining0.14510130.3864020.0486690.08901130.28881120.857870.2838523110.24861731213
Zigong0.21849120.09030110.04191100.15590100.5599380.842061310.2962129090.26022983212
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d+d-CiRank
Chengdu0.5486220.6759210.8210310.8775310.6890320.063540.963470.938131
Guangyuan0.4043250.08201120.0521380.2308480.6676730.747360.434340.367556
Luzhou0.2801490.11224100.3915920.2613350.53443100.665400.401010.376043
Leshan0.3972770.1884340.1008930.1873590.6209960.710110.424630.374214
Mianyang0.4941630.1727150.0657160.2900140.6183470.679700.494560.421172
Nanchong0.4036260.1727060.0676750.3142330.5529890.696640.415200.373445
Aba0.5937510.1477070.0758040.15504110.00000130.809580.466070.365367
Yibin0.26044100.1246090.0629370.2607560.6648440.772530.372810.325509
Dazhou0.25655110.1302980.01438130.2486270.6443250.795960.3602166160.3115610
Ya'an0.2960180.2057630.02281110.08964120.7775310.797060.4353911420.353278
Ganzhi0.4178440.01219130.01869120.3398420.28915110.792620.3415127830.30112139911
Suining0.14510130.3864020.0486690.08901130.28881120.857870.2838523110.24861731213
Zigong0.21849120.09030110.04191100.15590100.5599380.842061310.2962129090.26022983212
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