Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Exploration of Emotional Labor Research Trends in Korea through Keyword Network Analysis

주제어 네트워크 분석(network analysis)을 통한 국내 감정노동의 연구동향 탐색

  • Lee, Namyeon (Department of IT Management, Hanshin University) ;
  • Kim, Joon-Hwan (Department of Paideia, Sungkyul University) ;
  • Mun, Hyung-Jin (Department of Information & Communication Engineering, Sungkyul University)
  • 이남연 (한신대학교 IT경영학과) ;
  • 김준환 (성결대학교 파이데이아학부) ;
  • 문형진 (성결대학교 정보통신공학부)
  • Received : 2019.01.08
  • Accepted : 2019.03.20
  • Published : 2019.03.28
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Abstract

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

본 연구는 최근 10년 동안(2009-2018) 국내 학술지에 발표된 감정노동(emotional labor) 관련 892편의 논문을 텍스트 마이닝(text-mining) 및 네트워크 분석(network analysis)을 활용하여 연구동향을 파악하는 것이 목적이다. 이를 위해 이들 논문의 주제어를 수집 및 코딩하여 최종적으로 871개의 노드(node)와 2625개의 링크(link)로 변환시켜 네트워크 텍스트로 분석하였다. 첫째, 네트워크 텍스트 분석 결과로 동시출현빈도에 따른 상위 4개 주요 주제어는 번아웃, 이직의도, 직무스트레스, 직무만족 순으로 나타났으며, 연결중심성에 따른 상위 4개 주제어들의 빈도와 연결중심성 모두 비교적 높은 것으로 확인되었다. 둘째, 연결중심성 상위 4개의 주제어를 바탕으로 자아(ego)연결망 분석을 실시하여 각 네트워크의 연결중심도에 대한 주제어를 제시하였다.

Keywords

JKOHBZ_2019_v9n3_68_f0001.png 이미지

Fig. 1. Overall research process

JKOHBZ_2019_v9n3_68_f0002.png 이미지

Fig. 2. Rule set for data cleansing

JKOHBZ_2019_v9n3_68_f0003.png 이미지

Fig. 3. Co-occurrence keyword network (top-50)

JKOHBZ_2019_v9n3_68_f0004.png 이미지

Fig. 4. Ego-network of ‘Burnout’ keyword

JKOHBZ_2019_v9n3_68_f0005.png 이미지

Fig. 5. Ego-network of ‘Turnover Intention’ keyword

JKOHBZ_2019_v9n3_68_f0006.png 이미지

Fig. 6. Ego-network of ‘Occupational Stress’ keyword

JKOHBZ_2019_v9n3_68_f0007.png 이미지

Fig. 7. Ego-network of ‘Job Satisfaction’ keyword

Table 1. Co-occurrence matrix of high frequency keyword

JKOHBZ_2019_v9n3_68_t0001.png 이미지

Table 2. Top 10 keyword with centrality and frequency

JKOHBZ_2019_v9n3_68_t0002.png 이미지

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