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공급사슬 리스크 연구 동향과 이슈: 키워드 네트워크 분석

Research Trends and Issues in Supply Chain Risk Management: A Keyword Network Analysis

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참고문헌

키워드

주제분류경영학

초록

오늘날 기업에서 공급사슬 리스크 관리(Supply Chain Risk Management)는 세계경제의 저성장, 정치사회의 불안, 환경과 규제의 문제로 인해 비즈니스 생태계의 불확실성이 증가되고 기술의 발전 및 산업의 글로벌화로 인해 경쟁우위를 확보하고 지속가능한 성장을 하는데 있어 매우 중요한 사안으로 부각되고 있다. 이에 최근 들어 수요, 공급, 정보 및 운영을 포함한 다양한 유형의 공급사슬 리스크에 대한 연구가 더욱 활발하게 진행되고 있다. 따라서 본 연구는 대표적 학술 데이터베이스인 Scopus와 Thompson Reuters의 최근 5년 평균 Impact Factor를 이용하여 2000-2017년간 경영 분야의 9개의 국제저명학술지에서 총 1,429편의 연구문헌을 추출한 후, 네트워크 분석 툴인 NetMiner를 사용하여 키워드 네트워크 분석(Keyword Network Analysis)을 실시하였다. 구체적으로 체계적인 문헌조사 분석을 위하여 전체 키워드 네트워크의 컴포넌트 분석과 구조 분석을 통하여 공급사슬 리스크 연구의 키워드 네트워크를 구성하고 네트워크의 구조적 특성을 파악하였다. 또한 연결, 매개, 근접 중심성 분석을 통하여 주요 이슈를 분석하였고, 클러스터링 분석을 통하여 그룹별 세부 연구주제들을 파악하였다. 나아가 선호 연결 분석을 통하여 향후 연구 동향을 살펴보았다. 본 연구는 보다 총체적인 키워드 네트워크 분석 기법을 사용하여 주요 이슈와 동향을 파악했다는 점에서 그 학문적, 실무적 의의가 있다고 하겠다.

Abstract

Given the increasing uncertainties in the business ecosystem arising from low global economic growth, political and social turmoil, and environmental and regulatory concerns, along with technological advances and industrial globalization, supply chain risk management has become imperative for firms today to achieve competitive advantage and sustainable growth. In recent years, extensive research has been conducted on the management of various supply chain risks, including those related to demand, supply, information, and operations. Using NetMiner, a network analysis tool, this study performed a keyword network analysis of 1,429 articles published during 2000–2017, from nine premier overseas business journals included in the Scopus database and selected based on the recent five-year average impact factor from Thomson Reuters. Specifically, for a systematic literature review, we conducted component and structure analyses to construct a keyword network and identify its structural characteristics. Additionally, we performed degree, betweenness, and closeness centrality analyses to discover major issues, and clustering analysis to extract detailed research topics by groups. Moreover, we carried out preferential attachment analysis to explore future research trends. The academic and practical significance of this study lies in revealing major research issues and trends in supply chain risk management through a more holistic keyword network analysis.

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