• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of the Korean association of regional geographers
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• v.21 no.2
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• pp.286-303
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• 2015

This paper aims at analyzing the role of Korean ethnic network in the settlement process of Korean immigrants, and elucidating their utilization measures, through the case Study of Korean Immigrants in LA. The main results of this study are as follows. Firstly, the majority of respondents used Korean ethnic networks and Korean town in the immigration process. According to the results of ANOVA test, Korean ethnic network are more importantly considered by the groups such as elderly, livers in Korean town, and people who the first settlement was LA. Secondly, respondents more frequently use Korean ethnic network in the activities such as church, shopping and business. Especially, the groups such as short immigration term, elderly, poor English ability are more frequently use Korean ethnic network. Thirdly, Viewing the cognition of respondents about the necessity of Korean ethnic networks, respondents want to use them for strengthening the ties of domination society. Finally, utilization of Korean ethnic churches, Korean ethnic business organizations and Korean ethnic web-site portal are proposed for activating Korean ethnic networks.