Data Mining in Tourism Data Analysis: Inbound Visitors to Japan
big data analysis, decision trees, quantitative destination marketing, data mining, Japan
The increasing power of technology puts new, advanced statistical tools at the disposal of researchers. This is one of the first research articles to use a data mining tool—namely, decision trees—to analyze the behavior of inbound tourists for the purpose of effective future destination marketing in Japan. The research results of approximately 4,000 observations show that the main motivation for visitors' future return is not driven by experiences had during their most current visit but rather by experiences anticipated in the future, such as visiting hot springs or immersing themselves in beautiful natural settings. The data mining method largely excludes the possibility of the intrusion of researcher subjectivity and is conducive to useful discoveries of certain visitor patterns in large data sets, providing governments and destination marketing organizations with additional tools to better formulate effective destination marketing strategies.
Number of Pages
Journal of Travel Research
Rosen College of Hospitality Management
Shapoval, Valeriya; Wang, Morgan C.; Hara, Tadayuki; and Shioya, Hideo, "Data Mining in Tourism Data Analysis: Inbound Visitors to Japan" (2018). Faculty Scholarship and Creative Works. 730.