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http://dx.doi.org/10.52255/smarttourism.2022.2.2.3

Can We Identify Trip Purpose from a Clickstream Data?  

Choe, Yeongbae (Department of Tourism Management, Gachon University)
Publication Information
Journal of Smart Tourism / v.2, no.2, 2022 , pp. 15-19 More about this Journal
Abstract
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.
Keywords
destination management organization; website; clickstream; business analytics; destination marketing; trip purpose;
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1 Stienmetz, J. L., & Fesenmaier, D. R. (2015). Estimating value in Baltimore, Maryland: An attractions network analysis. Tourism Management, 50, 238-252.   DOI
2 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.   DOI
3 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.   DOI
4 Luna-Nevarez, C., & Hyman, M. R. (2012). Common practices in destination website design. Journal of Destination Marketing and Management, 1(1-2), 94-106.   DOI
5 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.
6 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   DOI
7 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.   DOI
8 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.   DOI
9 Pike, S., & Page, S. J. (2014). Destination Marketing Organizations and destination marketing: A narrative analysis of the literature. Tourism Management, 41, 202-227.   DOI
10 Plaza, B. (2011). Google Analytics for measuring website performance. Tourism Management, 32(3), 477-481.   DOI
11 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.   DOI
12 Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2), 557-575.   DOI
13 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.   DOI
14 Gretzel, U. (2011). Intelligent systems in tourism. Annals of Tourism Research, 38(3), 757-779.   DOI
15 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.   DOI
16 Davenport, T. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Boston, MA: Harvard Business Review Press.
17 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.   DOI
18 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.
19 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.   DOI
20 Govers, R., Go, F. M., & Kumar, K. (2007). Promoting tourism destination image. Journal of Travel Research, 46(1), 15-23.   DOI
21 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.
22 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.
23 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
24 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.   DOI
25 Kim, J., & Fesenmaier, D. R. (2015). Measuring emotions in real time: Implications for tourism experience design. Journal of Travel Research, 54(4), 419-429.   DOI
26 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.   DOI
27 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.   DOI
28 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.
29 Gunter, U., & O nder, I. (2016). Forecasting city arrivals with Google Analytics. Annals of Tourism Research, 61, 199-212.   DOI
30 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.   DOI