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中国管理科学 ›› 2024, Vol. 32 ›› Issue (9): 303-312.doi: 10.16381/j.cnki.issn1003-207x.2021.2587cstr: 32146.14.j.cnki.issn1003-207x.2021.2587

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基于画像的员工离职预测模型研究

闫泓序(),余顺坤   

  1. 华北电力大学经济与管理学院,北京 102206
  • 收稿日期:2021-12-13 修回日期:2022-04-26 出版日期:2024-09-25 发布日期:2024-10-12
  • 通讯作者: 闫泓序 E-mail:hxyan@ncepu.edu.cn
  • 基金资助:
    中央高校基本科研业务费专项资金(2018QN066)

Research on Employee Turnover Prediction Model Based on the Portrait

Hongxu Yan(),Shunkun Yu   

  1. Academy of Economics and Management,North China Electric Power University,Beijing 102206,China
  • Received:2021-12-13 Revised:2022-04-26 Online:2024-09-25 Published:2024-10-12
  • Contact: Hongxu Yan E-mail:hxyan@ncepu.edu.cn

摘要:

员工离职是当今组织中的重要问题。通过大数据对员工离职进行事前预测,能够为企业用人、留人决策提供科学依据,从而提升企业人力资源管理的前瞻性与智慧性。现有员工离职预测模型存在的关键问题在于业务驱动性不足,表现在其只能向人力资源管理者回答某位员工是否具有离职倾向,而无法进一步指示出其将为何离职,以及该如何针对性挽留。因此,针对这一问题,本研究依照数据准备、信息浓缩、模型构建与模型应用的全流程数据分析与应用脉络,集成PCA(主成分分析法)、CLARA(大规模数据聚类技术)、CART(分类与回归树算法),构建基于画像的员工离职预测模型,简称PCC(PCA-CLARA-CART)模型。最后,采用Kaggle数据分析平台上某包含14999个样本点的开源员工离职数据集对PCC模型开展实验研究,实验结果包括:(1)实验挖掘得到三种离职员工画像,包括职业倦怠型、追求成就型与劳累失望型;(2)采用多种指标对模型整体及分部标签的预测性能进行评估,结果为模型整体Accuracy达到0.913,Kappa系数达到0.8以上,各标签Balanced Accuracy达到0.92以上;(3)根据三种离职画像,提出针对性员工保留策略设计建议。理论分析与实验研究表明,本模型能够为员工离职预测、离职员工画像描摹与精准性员工保留策略设计提供理论参考,能够用于员工个体离职倾向预测与员工全体离职分布监测,可以从微观与宏观两个层面为企业员工保留和人才储备决策提供数据支持,是一套有可行效的人力资源智慧管理系统。

关键词: 离职员工画像, 员工离职预测模型, 员工保留策略, PCA, CLARA, CART

Abstract:

Nowadays, employee turnover is an important issue in organizations. Predicting employee turnover in advance using big data can provide a scientific basis for making decisions of employment and retention, which will enhance the foresight and wisdom of human resource management. The key problem of reported employee turnover prediction models is the lack of business driving force, which is manifested in that they can only answer to the human resource manager whether an employee has the turnover intention, but cannot further indicate why he or she will leave and how to retain it in a targeted manner. Therefore, focusing on this problem, a portrait-based employee turnover prediction model named PCC is proposed, integrating PCA (Principal Component Analysis), CLARA (Clustering Large Application), and CART (Classification and Regression Tree). Finally, the PCC model is experimented on an open-source employee turnover dataset with 14,999 samples from Kaggle. Theoretical and experimental studies show that the PCC model can provide theoretical reference for employee turnover prediction, turnover portrait description and accurate retention strategy design. Besides, it can be used to predict the turnover intention of individual employee and monitor the turnover distribution of all employees. Furthermore, it can provide data support for employee retention and talent reserve from both micro and macro levels. In summary, the PCC model is a feasible, effective and intelligent system for human resource management.

Key words: turnover employee portrait, employee turnover prediction model, employee retention strategy, PCA, CLARA, CART

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