관광지 여행상품의 구성요소와 지각된 가치에 대한 사회연결망분석

Abstract

As the economy grows and the appetite for travel increases, the number of travelers continues to increase gradually. Even with the growth of travelers, quality enhancement of the domestic travel industry is insufficient. The purpose of this study is to find the components of travel products and the value perceived by travelers and to vitalize the tourism industry by enhancing the quality of travel products. Big data was collected from major domestic portal sites such as Naver and Daum, focusing on the key word, "Busan travel" The social network analysis indicated the components of Busan travel products, cognitive values, and the clusters of these factors. The results were summarized as follows. First, travel to Busan was identified as 'Haeundae', 'activity', 'tourist site', 'travel service', 'transportation', 'accommodation', 'food', 'shopping' and so on. Second, tourism activity such as 'eating', 'riding', and tourist attraction such as 'good', 'recommending', 'many', 'none' were identified as the main attributes in Busan travel products. Third, the perceived value formed in the Busan trip showed positive and emotional value factors. Fourth, the cluster formed for the travel to Busan provided four clusters including 'before traveling to Busan', 'package-related', 'food-related', and 'experience-related.' These most influential attributes and clusters as the components of Busan travel products and perceived value could be seen critical aspects to enhance the quality overall. The results of this study provided a method of making travel products and using them as marketing tools by investigating the cognitive values ​​of Busan travel products and tourists. This study was able to measure the factors and cognitive values ​​measured in big data, instead of using the survey method. This study contributed to the body of knowledge utilizing a fairly new methodology, social network analysis, by analyzing the components of travel products and the cognitive value of travelers based on previous research results. Keywords: Busan, Traveler, Travel Product, Perceived Value, Tourist Destination, Big Data, Text Mining, Social Network Analysis