Culinary science and hospitality research (한국조리학회지)

Culinary Society of Korean Academy (한국조리학회)
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A Study of Comparison between Cruise Tours in China and U.S.A through Big Data Analytics

  • Shuting, Tao (School of Hospitality & Tourism Management, Kyungsung University) ;
  • Kim, Hak-Seon (School of Hospitality & Tourism Management, Kyungsung University)
  • Received : 2017.09.07
  • Accepted : 2017.09.26
  • Published : 2017.09.30
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Abstract

The purpose of this study was to compare the cruise tours between China and U.S.A. through the semantic network analysis of big data by collecting online data with SCTM (Smart crawling & Text mining), a data collecting and processing program. The data analysis period was from January $1^{st}$, 2015 to August $15^{th}$, 2017, meanwhile, "cruise tour, china", "cruise tour, usa" were conducted to be as keywords to collet related data and packaged Netdraw along with UCINET 6.0 were utilized for data analysis. Currently, Chinese cruisers concern on the cruising destinations while American cruisers pay more attention on the onboard experience and cruising expenditure. After performing CONCOR (convergence of iterated correlation) analysis, for Chinese cruise tour, there were three clusters created with domestic destinations, international destinations and hospitality tourism. As for American cruise tour, four groups have been segmented with cruise expenditure, onboard experience, cruise brand and destinations. Since the cruise tourism of America was greatly developed, this study also was supposed to provide significant and social network-oriented suggestions for Chinese cruise tourism.

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References

  1. Arney, C. (2009). Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Mathematics and Computer Education, 43(3), 271.
  2. Barabasi, A. L., & Watts, D. J. (2002). Six degrees: the Science of a connected age.
  3. Behrendt, S., Richter, A. & Trier, M. (2014). Mixed methods analysis of enterprise social networks. Computer Networks, 75(24), 560-577. https://doi.org/10.1016/j.comnet.2014.08.025
  4. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).
  5. Chin, C. B. (2008). Cruising in the global economy: Profits, pleasure and work at sea. Ashgate Publishing, Ltd..
  6. Choi, H. Y., Kwak, G. H., & Kim, H. S. (2007). A positioning study of national food: In perspective of Korean, American, Chinese food tourists. Culinary Science & Hospitality Research, 23(2), 86-94. https://doi.org/10.20878/cshr.2017.23.2.009
  7. Collins English Dictionary (2010). Collins english dictionary. Bishop Briggs, Glasgow: HarperCollins.
  8. Dowling, R. K. (2006). Looking ahead: The future of cruising. Cruise ship tourism, 414.
  9. Esposito, F., Ferilli, S., Basile, T. M., & Di Mauro, N. (2012). Social networks and statistical relational learning: A survey. International Journal of Social Network Mining, 1(2), 185-208. https://doi.org/10.1504/IJSNM.2012.051057
  10. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321-326. https://doi.org/10.5465/amj.2014.4002
  11. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457 (7232), 1012-1014. https://doi.org/10.1038/nature07634
  12. Guarino, N., Masolo, C., & Vetere, G. (1999). Ontoseek: Content-based access to the web. IEEE Intelligent Systems and their Applications, 14(3), 70-80.
  13. Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687. https://doi.org/10.1109/ACCESS.2014.2332453
  14. Kim, H. S. (2017a). A semantic network analysis of big data regarding the food exhibition on convention center. Culinary Science & Hospitality Research, 23(3), 257-270. https://doi.org/10.20878/cshr.2017.23.3.024
  15. Kim, H. S. (2017b). An exploratory study on the semantic network analysis of food tourism through the big data. Culinary Science & Hospitality Research, 23(4), 22-23. https://doi.org/10.20878/cshr.2017.23.4.003
  16. Kim, H. Y., (2012). Analysis of dynamic change of the novel land and character network. The Journal of the Korea Contents Association, 12 (11), 519-526. https://doi.org/10.5392/JKCA.2012.12.11.519
  17. Klein, R. A. (2006). 24 Turning water into money: The economics of the cruise industry. Cruise ship tourism, 261.
  18. Klein, R. A., (2011). Responsible cruise tourism: Issues of cruise tourism and sustainability. Journal of Hospitality and Tourism Management, 18(1), 107-116. https://doi.org/10.1375/jhtm.18.1.107
  19. Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Sage.
  20. Ministry of Maritime Affairs & Fisheries (2014). The second marine tourism promotion basic plan. Sejong: Ministry of Maritime Affairs and Fisheries.
  21. Ministry of Maritime Affairs & Fisheries (2015). Cruise industry activation measures. Sejong: Ministry of Maritime Affairs and Fisheries.
  22. Lusch, R., Liu, Y., & Chen, Y. (2010). The phase transition of markets and organizations: the new intelligence and entrepreneurial frontier.
  23. Palen, L., Vieweg, S., Liu, S. B., & Hughes, A. L. (2009). Crisis in a networked world: Features of computer-mediated communication in the April 16, 2007, Virginia Tech event. Social Science Computer Review, 27(4), 467-480. https://doi.org/10.1177/0894439309332302
  24. Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc.
  25. Shim, H. J., Kim, Y. C., Shon, H. Y., & Lim, J. Y. (2011). An exploratory usage pattern research of smartphone and social media users through semantic network analysis: Gender and age differences in perception and evaluation of usage pattern. Korean Journal of Broadcasting, 25(4), 28-138.
  26. Sun, X. D., & Feng, X. G. (2012). China cruise tourism industry: Research status and prospects. Journal of Tourism, (2), 101-112.
  27. Testa, M. R., & Sullivan, K. (2002). Customer satisfaction, quality in cruise industry. Hospitality Review, 20(2), 1.
  28. Trnka, A. (2014). Big data analysis. European Journal of Science and Theology, 10(1), 143-148.
  29. Van Atteveldt, W. H. (2008). Semantic network analysis: Techniques for extracting, representing, and querying media content.
  30. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. https://doi.org/10.1111/jbl.12010
  31. Wang, Y. (2015). The rise of the ‘shareholding state’: financialization of economic management in China. Socio-Economic Review, 13(3), 603-625. https://doi.org/10.1093/ser/mwv016
  32. Weeden, C., Lester, J. A., & Thyne, M. (2011). Cruise tourism: Emerging issues and implications for a maturing industry. Journal of Hospitality and Tourism Management, 18(1), 26-29. https://doi.org/10.1375/jhtm.18.1.26

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