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Machine Learning for Human Resource Management in the Public Sector: Early-Career Turnover Intention Prediction Model

Year: 2025

Type: article

Abstract: The study aims to develop an optimal turnover intention prediction model through training, including explanatory variables that have causal relationships with early-career turnover intention in the public sector. This study employs job-related factors, organization system factors, and socio-psychological factors, using the Public Employee Perception Survey data. According to the result, the random forest prediction model provides a better fit to predict early-career turnover intention and most variables of the importance index of turnover intention are P-O fit, P-J fit, satisfaction with the welfare benefits system, and HR, job satisfaction, autonomy, education, and prospects. This study provides policy implications and future direction to decrease early-career turnover intention in the public sector in South Korea.

Source: Journal of Information Systems Engineering & Management

DOI: 10.52783/jisem.v10i34s.5815

Author: Eunjin Hwang

Institution: Jeju National University

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea