국내 ICT 업종 종사자들의 직장에 대한 불만 요인 분석 및 전/현직자 간 차이 분석: 토픽 모델링 적용
An Exploratory Analysis of Domestic ICT Workers' Dissatisfaction with their Jobs and Differences between Former and Incumbent Employees: Application of Topical Modeling
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초록
본 연구는 대표적 텍스트 마이닝 기법인 토픽 모델링(topic modeling) 방법을 적용하여, ICT 업종 종사자들이 채용 플랫폼의 기업 리뷰에 회사의 단점(불만 요인)으로 기술한 텍스트를 분석하고, 현직자와 전직자 간에 기술 내용에 의미 있는 차이가 있는지를 분석하였다. 구체적으로 Schmidel 등(2019)이 제시한 조직 연구에서의 토픽 모델링 절차를 따라, ICT 업종에 근무하는 전, 현직 종업원들이 국내 채용 관련 플랫폼인 잡플래닛에 회사의 단점으로 기술한 텍스트 데이터(128,464개)를 분석하였으며, 도출된 토픽들을 직무태도 및 이직의 예측변인들에 대한 국내외 연구결과들을 참조하여 해석하였다. 분석 결과, 가장 적절한 것으로 판단된 50개의 토픽 중 44개가 명명되었고, 그중 가장 높은 비율로 나타난 토픽은 ‘낮은 연봉’이었다. 44개의 토픽들 중 현직자와 이직자 간에 유의하게 차이가 나게 언급되는 토픽들이 확인되었고, 추출된 토픽들은 기존의 직무만족 및 이직 연구들에서 중요하게 다루어진 변인들과 유사한 것들도 있지만, 새롭게 도출된 것들도 있었다. 마지막으로, 새로운 접근법으로서의 조직심리 분야의 텍스트 마이닝 방법의 가능성에 대해 논의하였다.
Abstract
This study applied the Topic Modeling method, one of the text mining techniques, to analyze the text describing the shortcomings(unsatisfactory factors) of the company in the corporate review of the recruitment platform and to analyze whether there are meaningful differences in text contents bettween the stayers and the leavers. Specifically, following the procedures proposed by Schmidel et al.(2019), we analyzed the texts describing the company's shortcomings by former and incumbent employees in the ICT sector in South Korea and derived 50 topics through Topic Modeling. 44 of the 50 topics judged to be most appropriate were labeded by referring to domestic and international research results on job attitude and predictors of turnover, and the highest percentage of topic among them was 'low salary'. Among the 44 topics, significant differences were identified between stayers and leavers. In addition, the extracted topics were similar to the variables that were important in the existing job satisfaction and turnover studies, but there were also new ones. Finally, the potential of text mining methods in the field of organizational psychology as a new approach was discussed.
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