스마트미디어저널 (Smart Media Journal)

한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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텍스트마이닝을 활용한 빅데이터 기반의 디지털 트랜스포메이션 연구동향 파악

Identifying Research Trends in Big data-driven Digital Transformation Using Text Mining

  • 김민준 (국립금오공과대학교 산업공학부)
  • 투고 : 2022.10.21
  • 심사 : 2022.11.30
  • 발행 : 2022.11.30
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초록

빅데이터 기반의 디지털 트랜스포메이션은 데이터 및 데이터 관련 기술을 통해 기업의 성과 향상, 조직 변화, 사회 공헌 등의 목적 달성을 위해 수행하는 혁신적 프로세스를 의미한다. 성공적인 빅데이터 기반의 디지털 트랜스포메이션을 위해서는 관련 연구 현황, 주요 연구토픽, 주요 연구토픽 간의 관계를 이해하는 것이 필수적이다. 그러나 여러 연구들의 서로 다른 관점 및 이들 간 연계 가능성에 대해 이해하려는 노력은 아직 미진하다. 본 논문은 텍스트마이닝을 활용하여 관련 연구동향을 분석하고, 여러 연구의 다양한 관점을 통합적으로 이해하기 위한 기반 마련을 시도해보았다. Web of Science Core Collection에서 추출한 439편의 논문을 분석하여, 10개의 주요 연구토픽을 도출하였고, 이들 간의 관계를 분석하였다. 본 연구의 결과가 빅데이터 기반의 디지털 트랜스포메이션에 대한 통합적인 이해를 촉진하고, 성공을 위한 방향성 모색에 기여할 것으로 기대한다.

A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.

키워드

과제정보

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2022R1G1A1008312).

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