Journal of Smart Tourism

  • 제2권2호
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  • Pages.15-19
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  • 2022
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  • 2765-2157(pISSN)
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  • 2765-7272(eISSN)
경희대학교 스마트관광연구소 (Smart Tourism Research Center, Kyung Hee University)
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Can We Identify Trip Purpose from a Clickstream Data?

  • 투고 : 2022.03.04
  • 심사 : 2022.05.05
  • 발행 : 2022.06.30
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초록

Destination marketing organizations (DMOs) utilize the official website for marketing and promotional purposes, while tourists often navigate through the official website to gather necessary information for their upcoming trips. With the advancement of business analytics, DMOs may need to exploit the clickstream data generated through their official website to develop more suitable and persuasive strategic marketing and promotional activities. As such, the primary objective of the current study is to show whether clickstream data can successfully identify the trip purposes of a particular user. Using a latent class analysis and multinomial logistic regression, this study found the meaningful and statistically significant variations in webpage visits among different trip purpose groups (e.g., weekend getaways, day-trippers, and other purposes). The findings of this study would provide a foundation for more data-centric destination marketing and management practice.

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과제정보

The authors wish to recognize the Northern Indiana Tourism Association for their financial support of the initial data collection effort.

참고문헌

  1. Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249-267. https://doi.org/10.1509/jmkr.40.3.249.19241
  2. Choi, S., Lehto, X. Y., & Morrison, A. M. (2007). Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management, 28(1), 118-129. https://doi.org/10.1016/j.tourman.2006.03.002
  3. Davenport, T. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Boston, MA: Harvard Business Review Press.
  4. Fesenmaier, D. R., & Xiang, Z. (2014). Tourism marketing from 1990-2010: Two decades and a new paradigm. In S. McCabe (Ed.), The handbook of tourism marketing (pp. 549-560). Burlington, MA: Routledge.
  5. Fuchs, M., Ho pken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations-A case from Sweden. Journal of Destination Marketing and Management, 3(4), 198-209. https://doi.org/10.1016/j.jdmm.2014.08.002
  6. Govers, R., Go, F. M., & Kumar, K. (2007). Promoting tourism destination image. Journal of Travel Research, 46(1), 15-23. https://doi.org/10.1177/0047287507302374
  7. Gretzel, U. (2011). Intelligent systems in tourism. Annals of Tourism Research, 38(3), 757-779. https://doi.org/10.1016/j.annals.2011.04.014
  8. Gretzel, U. (2022). The Smart DMO: A new step in the digital transformation of destination management organizations. European Journal of Tourism Research, 30, 3002-3002. https://doi.org/10.54055/ejtr.v30i.2589
  9. Gretzel, U., Fesenmaier, D. R., & O'Leary, J. T. (2006). The transformation of consumer behaviour. In D. Buhalis, C. Costa & F. Ford (Eds.), Tourism business frontiers: Consumers, products and industry (pp. 7-16). Burlington, MA: Routledge.
  10. Gunter, U., & O nder, I. (2016). Forecasting city arrivals with Google Analytics. Annals of Tourism Research, 61, 199-212. https://doi.org/10.1016/j.annals.2016.10.007
  11. Ho pken, W., Fuchs, M., Keil, D., & Lexhagen, M. (2011). The knowledge destination-A customer information-based destination management information system. In R. Law, M. Fuchs & F. Ricci (Eds.), Information and communication technologies in tourism (pp. 417-429). Vienna: Springer.
  12. Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. Proceeding of 28th Annual ACM SIGIR Conference. New York: ACM, 154-161. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.2481&rep=rep1&type=pdf
  13. Kim, H., & Fesenmaier, D. R. (2008). Persuasive design of destination web sites: An analysis of first impression. Journal of Travel Research, 47(1), 3-13. https://doi.org/10.1177/0047287507312405
  14. Kim, J., & Fesenmaier, D. R. (2015). Measuring emotions in real time: Implications for tourism experience design. Journal of Travel Research, 54(4), 419-429. https://doi.org/10.1177/0047287514550100
  15. Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS Procedure for Latent Class Analysis. Structural Equation Modeling, 14(4), 671-694. https://doi.org/10.1080/10705510701575602
  16. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52, 21-32.
  17. Li, X., & Wang, Y. (2010). Evaluating the effectiveness of destination marketing organisations' websites: Evidence from China. International Journal of Tourism Research, 12(5), 536-549. https://doi.org/10.1002/jtr.772
  18. Luna-Nevarez, C., & Hyman, M. R. (2012). Common practices in destination website design. Journal of Destination Marketing and Management, 1(1-2), 94-106. https://doi.org/10.1016/j.jdmm.2012.08.002
  19. Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt Publishing Company.
  20. Onder, I., & Berbekova, A. (2021). Web analytics: More than website performance evaluation? International Journal of Tourism Cities. Advance online publication. https://doi.org/10.1108/IJTC-03-2021-0039
  21. Park, S., & Kim, D. Y. (2017). Assessing language discrepancies between travelers and online travel recommendation systems: Application of the Jaccard distance score to web data mining. Technological Forecasting and Social Change, 123, 381-388. https://doi.org/10.1016/j.techfore.2017.03.031
  22. Pike, S., & Page, S. J. (2014). Destination Marketing Organizations and destination marketing: A narrative analysis of the literature. Tourism Management, 41, 202-227. https://doi.org/10.1016/j.tourman.2013.09.009
  23. Plaza, B. (2011). Google Analytics for measuring website performance. Tourism Management, 32(3), 477-481. https://doi.org/10.1016/j.tourman.2010.03.015
  24. Stienmetz, J. L., & Fesenmaier, D. R. (2015). Estimating value in Baltimore, Maryland: An attractions network analysis. Tourism Management, 50, 238-252. https://doi.org/10.1016/j.tourman.2015.01.031
  25. Stienmetz, J. L., & Fesenmaier, D. R. (2019). Destination value systems: Modeling visitor flow structure and economic impact. Journal of Travel Research, 58(8), 1249-1261. https://doi.org/10.1177/0047287518815985
  26. Stienmetz, J. L., Levy, S. E., & Boo, S. (2013). Factors influencing the usability of mobile destination management organization websites. Journal of Travel Research, 52(4), 453-464. https://doi.org/10.1177/0047287512467698
  27. Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2), 557-575. https://doi.org/10.1016/j.ejor.2004.04.022
  28. Yang, Y., Pan, B., & Song, H. (2014). Predicting hotel demand using destination marketing organization's web traffic data. Journal of Travel Research, 53(4), 433-447. https://doi.org/10.1177/0047287513500391
  29. Yuan, Y. L., Gretzel, U., & Fesenmaier, D. R. (2006). The role of information technology use in American convention and visitors bureaus. Tourism Management, 27(2), 326-341. https://doi.org/10.1016/j.tourman.2004.12.001
  30. Zach, F., Gretzel, U., & Xiang, Z. (2010). Innovation in the web marketing programs of American convention and visitor bureaus. Information Technology and Tourism, 12(1), 47-63. https://doi.org/10.3727/109830510X12747489979628