데이터 마이닝에 기반한 관광지 연관성 분석 : 외래관광객의 방한 동기별 공간 특성 비교 = Analyzing tourist movement patternsusing data mining

다국어 초록 (Multilingual Abstract)

Despite the numerous research endeavors aimed at examining tourists’destinations choice behavior by their motivations, it remains difficult for practitioners to utilize the results of association rule mining methods in tourism management. The purpose of this research were to explore and investigate tourist movement patterns of leisure, business and shopping travellers. This paper also aims to be a methodological contribution to the field of spatial tourism behavior research by presenting an empirical study on the mining of association rules in tourist attraction visits. Using the dataset of 2013 international visitor survey collected by the Ministry of Culture, and Tourism in Korea, the data mining method was applied to over 10000 inbound tourists. We were able to identify interesting associations rules among three tourist groups and discover that three groups have a substantially different tendency of visiting destinations. First, according to the result of leisure travelers, who visited Korea for relaxation on their vacation, the N tower in Seoul particularly played a bridging role of other destinations in Seoul. Second, a geographical proximity between destinations was observed in business travelers. In addition, business travelers tended to visit destinations, where they could experience Korean traditional culture by visiting Insa-dong, and a Korean traditional village(Namsan Hanok Maeul) and also sought to visit a location such as Itaewon, where they could enjoy a multi-cultural atmosphere, that was not discovered in other groups. Thirdly, the result of shopping tourists revealed that they were likely to visit relatively few destinations compared to leisure and business travelers with a high record of their support ratio.
An extensive literature review and interpretation were followed by an exploratory analysis, analyzing the discovered associations rules each group and additionally visualizing them. This paper presents both academic and practical implications on dominantly different movement patterns of tourists based on their motivations. The result will help tourism private organizations develop better tour packages and more appropriate tourism products aligned to the characteristics of the tourists. The empirical results will be also useful in assisting tourism managers to make an appropriate decision and implement more efficient strategies.

목차 (Table of Contents)

• 제1장 서론 9
• 제1절 연구 배경 및 필요성 9
• 제2절 연구 목적 13
• 제3절 연구 흐름도 14
• 제2장 이론적 배경 15
• 제1절 관광동기 이론 15
• 2.1.1. Push-Pull 동기 이론 15
• 2.1.2. 관광 동기 집단별 특성 18
• (1) 휴가레저 관광객 18
• (2) 비즈니스 관광객 20
• (3) 쇼핑 관광객 24
• 제2절 관광지 네트워크 27
• 2.2.1. 네트워크 개념 및 특성 27
• 2.2.2. 관광 목적지 네트워크 30
• 제 3절 데이터 마이닝 33
• 2.3.1. 데이터 마이닝 정의 33
• 2.3.2. 빅데이터 개념 및 특성 36
• 2.3.3. 데이터 마이닝 적용 분야 38
• 제3장 연구 대상 및 방법론 42
• 제1절 데이터 마이닝 수행 절차 42
• 제2절 연구 방법론 44
• 3.2.1. 연관성 규칙 개념 및 특성 44
• 3.2.2. 연관성 규칙 분석 수행절차 48
• 제3절 연구 대상 및 범위 52
• 제4장 실증 분석 결과 58
• 제1절 휴가레저 관광객 58
• 제2절 비즈니스 관광객 66
• 제3절 쇼핑 관광객 70
• 제5장 결론 74
• 제1절 연구 결과 요약 74
• 제2절 이론적 시사점 76
• 5.2.1. 데이터 마이닝 연구 방법론 적용 76
• 5.2.2. 융복합 관광연구 77
• 5.2.3. 관광 네트워크 문헌 확장 79
• 제3절 실무적 시사점 80
• 5.3.1. 교차 판매 및 시장 세분화 80
• 5.3.2. 관광객 교통 시스템 효율성 82
• 5.3.3. 관광지 개발정책 효과성 83
• 제4절 한계점 84