관광산업의 디지털 전환 이슈 도출과 지원 정책 평가에 관한 연구
A Study on Derivation of Digital Transformation Issues and Evaluation of Support Policy in Tourism Industry
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초록
이 연구는 텍스트 마이닝 기법을 사용하여 COVID-19 이후 학술 문헌과 언론 보도에서 논의되었던 관광산업의 디지털 전환 주제들을 추출하였다. 이러한 주제들을 기반으로, 국내의 관광산업을 위한 디지털 전환 지원 정책을 검토하고, 기존 정책의 개선점을 제안하였다. 연구를 위해 온라인 학술 데이터베이스와 언론사 웹사이트에서 제공하는 영문으로 작성된 논문 초록과 뉴스 기사 5,018건을 수집하였다. 수집된 데이터는 전처리 과정을 거쳐, 딥러닝 기반의 BERTopic 모델을 사용하여 논문의 초록과 뉴스 기사에서 주제와 핵심 키워드를 도출했다. 이 연구로 파악된 관광산업의 디지털전환 관련 이슈들과 이를 바탕으로 한 구체적인 지원 정책은 국내 관광 업계의 디지털 전환 촉진 및 정부 지원기관의 정책 마련과 실행에 유용한 참고 자료가 될 것이다. 또한, 이는 산업의 다양한 요구사항을 더 넓게 반영하여 디지털 지원 정책의 효과성을 높이는 데 기여할 것이다.
Abstract
This study utilized text mining techniques to extract themes related to the digital transformation of the tourism industry discussed in academic literature and press reports after COVID-19. Based on these themes, it reviewed digital transformation support policies for the domestic tourism industry and suggested improvements to existing policies. For the research, 5,018 English-written paper abstracts and news articles were collected from online academic databases and news website portals. The collected data underwent a preprocessing process and then themes and key keywords were derived from the paper abstracts and news articles using a deep learning-based BERTopic model. The issues related to the digital transformation of the tourism industry identified through this research and the specific support policies based on them will serve as useful reference materials for promoting digital transformation in the domestic tourism sector and for policy formulation and implementation by government support agencies. Additionally, this will contribute to enhancing the effectiveness of digital support policies by more broadly reflecting the diverse requirements of the industry.
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