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A Study on the Revitalization of Tourism Industry through Big Data Analysis
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Jungmi Lee (Business School of Hanyang University)
Meina Liu (Business School of Hanyang University)
Gyoo Gun Lim (School of Business, Hanyang University)
Vol. 24, No. 2, Page: 149 ~ 169
10.13088/jiis.2018.24.2.149
Keywords
Big Data, Artificial Neural Network, Decision Tree, Logistic Regression Analysis, R Programming
Abstract
Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field.
However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low.
In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above.
As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived.
As a result of data analysis, it was found that seven major variables influencing ‘overall satisfaction’ were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction.
In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher β value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher β value than other variables.
From this analysis, we found that ‘food satisfaction’ and ‘sightseeing spot attraction’ variables were the common factors to influence three dependent variables that are mentioned above(‘Overall satisfaction’, ‘Revisit intention’ and ‘Recommendation’), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.
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한국관광 실태조사 빅 데이터 분석을 통한 관광산업 활성화 방안 연구
이정미 (한양대학교 일반대학원 경영대학 비즈니스인포매틱스)
류미나 (한양대학교 일반대학원 경영대학)
임규건 (한양대학교 경영대학)
Keywords
빅데이터, 인공신경망, 의사결정나무, 로지스틱스 회귀분석, R 프로그래밍
Abstract
본 연구에서는 한국문화관광연구원에서 조사된 “2013년~2015년 외래 관광객 실태조사”의 약 36,000개 데이터에 대한 빅 데이터 분석을 통해 관광산업 활성화 방안을 도출해 보고자 한다. 이를 위해서 외래 관광객들의‘전반적 만족도’, ‘재방문 의사’, ‘추천의사’ 변수에 가장 많은 영향을 끼치는 요인을 분석하고 해당 요인들의 각각에 대한 영향력에 대해 파악 하였다. 본 연구에서는 SPSS IBM Modeler 16.0의 의사결정나무(C5.0, CART, CHAID, QUEST), 인공신경망, 로지스틱 회귀분석의 데이터마이닝 기법을 이용하여 종속변수에 가장 큰 영향을미치는 상위 변수 7개씩을 각각 도출하였고, 추가적으로 각 독립변수들의 영향력을 심도 있게 파악하기 위하여R프로그래밍을 활용하여 SPSS IBM Modeler 16.0을 통해 도출된 각 독립변수들의 영향력을 파악하였다. 데이터분석 결과 '전반적 만족도'에 가장 영향을 미치는 상위 변수 7개는 관광지매력도, 음식만족도, 숙박만족도, 교통수단만족도, 안내서비스만족도, 방문관광지수, 국가로 나타났으며 가장 큰 영향력을 미친 변수는 음식만족도와관광지매력도로 분석되었다. ‘재방문 의사’에 가장 영향을 미치는 상위 변수 7개로는 국가, 여행 동기, 활동, 음식만족도, 제일 좋았던 활동, 관광안내서비스만족도, 관광지매력도로 나타났으며 그중 가장 큰 영향력을 미친변수는 음식만족도와 여행 동기로 분석되었다. 마지막으로 ‘추천의사’에 영향을 미치는 상위 변수 7개로는 국가, 관광지매력도, 방문관광지수, 음식만족도, 활동, 관광안내서비스만족도, 비용으로 나타났으며 가장 큰 영향력을 미친 변수는 국가, 관광지매력도, 음식만족도로 분석되었다. 따라서 세 변수에 공통적으로 영향을 끼치는요인은 음식만족도, 관광지매력도로 분석되었으며 해당 요인들이 공통적으로 한국여행에 대한 전반적 만족도와 재방문 의사, 추천의사에 미치는 영향이 크다는 것을 확인할 수 있었다. 본 연구는 외래 관광객들의 한국관광에 대한 활성화 방안을 “외래 관광객 실태조사” 빅 데이터 분석을 통해 규명함으로써 한국 관광 데이터 분석의 활용과 관광 정책 수립의 기초자료로 활용될 수 있을 것으로 기대되며 향후 기업 및 국가차원에서 한국 관광발전에 기여할 수 있는 활성화 방안을 마련하는 자료로 사용될 수 있을 것으로 기대한다.
Cite this article
JIIS Style
Lee, J., M. Liu, and G. G. Lim, "A Study on the Revitalization of Tourism Industry through Big Data Analysis", Journal of Intelligence and Information Systems, Vol. 24, No. 2 (2018), 149~169.

IEEE Style
Jungmi Lee, Meina Liu, and Gyoo Gun Lim, "A Study on the Revitalization of Tourism Industry through Big Data Analysis", Journal of Intelligence and Information Systems, vol. 24, no. 2, pp. 149~169, 2018.

ACM Style
Lee, J., Liu, M., and Lim, G. G., 2018. A Study on the Revitalization of Tourism Industry through Big Data Analysis. Journal of Intelligence and Information Systems. 24, 2, 149--169.
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@article{Lee:JIIS:2018:730,
author = {Lee, Jungmi and Liu, Meina and Lim, Gyoo Gun},
title = {A Study on the Revitalization of Tourism Industry through Big Data Analysis},
journal = {Journal of Intelligence and Information Systems},
issue_date = {June 2018},
volume = {24},
number = {2},
month = Jun,
year = {2018},
issn = {2288-4866},
pages = {149--169},
url = {http://dx.doi.org/10.13088/jiis.2018.24.2.149 },
doi = {10.13088/jiis.2018.24.2.149},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Big Data, Artificial Neural Network, Decision Tree, Logistic Regression Analysis and R Programming
},
}
%0 Journal Article
%1 730
%A Jungmi Lee
%A Meina Liu
%A Gyoo Gun Lim
%T A Study on the Revitalization of Tourism Industry through Big Data Analysis
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 24
%N 2
%P 149-169
%D 2018
%R 10.13088/jiis.2018.24.2.149
%I Korea Intelligent Information System Society