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Turnover rate prediction among IT firms according to job satisfaction and dissatisfaction factors: Using topic modeling and machine learning
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IT 기업 직원의 만족 및 불만족 요인에 따른 이직률 예측: 토픽모델링과 머신러닝을 활용하여

논문 기본 정보

Type
Academic journal
Author
Jinwook Choi (고려대학교) Dongwon Shin (명지대학교) Hanjun Lee (명지대학교)
Journal
The Korean Data and Information Science Society Journal of the Korean Data And Information Science Society Vol.32 No.5 KCI Excellent Accredited Journal
Published
2021.9
Pages
1,035 - 1,047 (13page)
DOI
10.7465/jkdi.2021.32.5.1035

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Turnover rate prediction among IT firms according to job satisfaction and dissatisfaction factors: Using topic modeling and machine learning
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Job turnover in the IT industry is a major challenge for companies in technology accumulation, development, and management, and thus, research on this is essential. However, there are still few studies related to turnover and personnel management in the IT field. This study examines the satisfaction and dissatisfaction factors of employees that affect the turnover rate and proposes a model for predicting the turnover rate of IT companies using factors. To this end, we collected 21,589 reviews from employees of 129 IT companies listed on the domestic stock market on Jobplanet, an online company review site, and conducted topic modeling. Using topics extracted, machine learning-based predictive models for turnover rate were propose. In addition, this study analyzed the degree of influence of each employee satisfaction and dissatisfaction factor on the turnover rate through variable importance evaluation.
#기업 리뷰:머신러닝 #이직률 #직원 만족 #토픽모델링 #Employee satisfaction #firm review #machine learning #topic modeling #turnover rate

Contents

요약
1. 서론
2. 관련 연구
3. 연구 방법
4. 연구결과
5. 시사점 및 결론
References
Abstract

References (38)

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Ahuja, M. K. / 2007 / IT road warriors : Balancing work-family conflict, job autonomy, and work overload to mitigate turnover intentions / MIS Quarterly 31:1~17 google schola 안병대 / 2020 / 기업개성이 직원의 직무만족과 기업 이직률의 관계에 미치는 영향 : 잡플래닛 기업 리뷰를 중심으로 / 한국IT서비스학회지 19(3):35~56 Crossref Atkins, A. / 2018 / Financial news predicts stock market volatility better than close price / The Journal of Finance and Data Science 4:120~137 google schola 방한솔 / 2019 / 텍스트마이닝을 이용하여 텍스트의 주요 토픽을 시계열적으로 표현하는 방법론 연구 / 한국데이터정보과학회지 30(6):1259~1276 dbpia Bartol, K. M. / 1982 / Managing information systems personnel : A review of the literature and managerial implications / MIS Quarterly (Special Issue):49~70 google schola

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