The Organization’s Sustainable Work Stress and Maladjustment Management Plan by Predicting Early Retirement through Big Data Analysis: Focused on the Case of South Korea

Hyunjung Ham,Daeyeon Cho,Eunbee Kim

doi:10.3390/su14010434

Abstract

The purpose of this study is to verify the predictive model of early retirees’ responses to work stress and maladjustment to the company by utilizing big data analytics and to extract the reasons for early retirement from the personnel information. Company A’s personnel information of employees working in the company for 10 years was used, K-Nearest Neighbor (K-NN) algorithm was used to verify the predictive model of early retirees, and Decision Tree Analysis algorithm was used to extract the causing factors. According to the analysis results, first, the verification of the predictive model of early retirees based on the personnel information data showed 98% accuracy. Second, among the personnel information items, the ranking of items with high relevance for early retirement was the distance between the company and the residence (first place), the recent promotion history (second place), and whether or not to have the license (third place) out of a total of 18 items. The results of the analysis conducted in this study suggest that HRD intervention is required in the provision of problem-solving solutions involved in the HRM field, which is expected to be effective as a basic diagnostic tool for HR diagnosis involving HRD and HRM. In addition, this study may provide a detailed analysis of early retirement due to work stress and maladjustment of young people.

Highlights

As revealed in the research model and research procedure, K-Nearest Neighbor (K-NN) analysis technology was first applied to confirm whether it is possible to predict the early departure of company A through big data analysis methods
For all big data analyses, this accuracy depends on the training data and model parameters used to create the analytical model
This study looked into the possibility of predicting early retirement by utilizing big data analytics targeting the personnel information of company A’s early retirement

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The topic of organization in this era is focused on how to survive in an uncertain, volatile, complex, and ambiguous future. In addition to the rapid pace of change, understanding the causal relationship of environments within domestic company markets and organizations is not simple, and it is difficult to predict variables in future situations. It has become important to predict what strategies are needed for the organizations and what management-level coping and prevention strategies are needed to carry them out

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Conclusion