Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.1.111

A Study on the Characteristics of Tourism Flow of Independent Tourists from China to South Korea Based on Tourists' Digital Footprint  

Wang, Chun-Yan (Faculty of Tourism Management, Jilin Engineering Normal University)
Jang, Phil-sik (Dept. of Air Transport and Logistics, Sehan University)
Kim, Hyung-Ho (Dept. of Air Transport and Logistics, Sehan University)
Publication Information
Journal of Digital Convergence / v.18, no.1, 2020 , pp. 111-119 More about this Journal
Abstract
This study takes Chinese independent tourists to South Korea as the research object, mines the data of tourists' digital footprints from online travel notes, and analyzes the characteristics of the tourism flow of Chinese independent tourists to South Korea by using the method of quantitative statistics and social network analysis(SNA). The results show that Seoul, Jeju Island, Busan and Daegu are the important tourist destinations for Chinese independent tourists entering South Korea. In addition, Qingdao, Tianjin, Shenyang, Hong Kong, Foshan and Macao are crucial hubs for Chinese independent tourists to visit South Korea. In future studies, the number of sample data should be increased. The time span of data collection should be extended for studying the annual variation characteristics of tourism flow and the trend of tourism hot spots.
Keywords
SNA; Chinese Independent Tourist; Tourism Flow; Digital Footprint; Degree Centrality;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 A. V. Williams & W. Zelinsky. (1970). On some patterns in international tourism flows. Economic Geography, 46(4), 549-567. https://www.jstor.org/stable/142940   DOI
2 T. Hong, T. Ma & T. C. Huan. (2015). Network behavior as driving forces for tourism flows. Journal of Business Research, 68(1), 146-156. DOI : 10.1016/j.jbusres.2014.04.006   DOI
3 Y. F. Li & H. Cao. (2018). Prediction for Tourism Flow based on LSTM Neural Network. Procedia Computer Science, 129, 277-283. DOI : 10.1016/j.procs.2018.03.076   DOI
4 G. De Vita. (2014). The long-run impact of exchange rate regimes on international tourism flows. Tourism Management , 45, 226-233. DOI : 10.1016/j.tourman.2014.05.001   DOI
5 R. Cellini & T. Cuccia. (2012). Museum and monument attendance and tourism flow: a time series analysis approach. Applied Economics, 45(24), 3473-3482. DOI : 10.1080/00036846.2012.716150   DOI
6 Y. H. Hwang, U. Gretzel & D. R. Fesenmaier. (2006). Multicity trip patterns: Tourists to the United States. Annals of Tourism Research, 33(4), 1057-1078. DOI : 10.1016/j.annals.2006.04.004   DOI
7 N. Scott, C. Cooper & R. Baggio. (2008). Destination networks: Four Australian cases. Annals of Tourism Research, 35(1), 169-188. DOI : 10.1016/j.annals.2007.07.004   DOI
8 J. I. L. Miguens & J. F. F. Mendes. (2008). Travel and tourism: Into a complex network. Physica A Statistical Mechanics & Its Applications, 387(12), 2963-2971. DOI : 10.1016/j.physa.2008.01.058   DOI
9 F. Girardin, F. Dal Fiore, C. Ratti, & J. Blat. (2008). Leveraging explicitly disclosed location information to understand tourist dynamics: A case study. Location Based Services , 2(1), 41-56. DOI : 10.1080/17489720802261138   DOI
10 H. Kim & S. Stepchenkova. (2015). Effect of tourist photographs on attitudes towards destination: manifest and latent content. Tourism Management, 49, 29-41. DOI : 10.1016/j.tourman.2015.02.004   DOI
11 S. Choi, X. Y. Lehto & A. M. Morrison. (2007). Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management, 28(1), 118-129. DOI : 10.1016/j.tourman.2006.03.002   DOI
12 J. C. Garcia-Palomares, J. Gutierrez & C. Minguez. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. DOI : 10.1016/j.apgeog.2015.08.002   DOI
13 M. H. Salas-Olmedo, B. Moya-Gomez, J. C. Garcia-Palomares & J. Gutierrez. (2018). Tourists' digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. DOI : 10.1016/j.tourman.2017.11.001   DOI
14 B. B. Delgado, H. M. Ma, J. G. Oh & G. T. Yeo. (2018). The Study on the research trend about Europe ports: focus on Baltic Sea using Keyword network. Journal of Digital Convergence, 16(2), 139-149. DOI : 10.14400/JDC.2018.16.2.139   DOI
15 C. Y. Wang, P. S. Jang & H. H. Kim. (2019). A Study on the Characteristics of the Seasonal Travel Path of Individual Chinese Travellers in Korea. Korea Convergence Society , 10(7), 23-31. DOI : 10.15207/JKCS.2019.10.7.023
16 S. C. Song, T. H. Nguyen, S. H. Park & G. T. Yeo. (2018). Research Trends Analysis on Port Hinterland Using SNA Method. Journal of Digital Convergence, 16(11), 17-27. DOI : 10.14400/JDC.2018.16.11.017   DOI
17 H. Y. Shih. (2006). Network characteristics of drive tourism destinations: An application of network analysis in tourism. Tourism Management, 27(5), 1029-1039. DOI : 10.1016/j.tourman.2005.08.002   DOI