如何破解企业数字技能人才培养悖论?——一项基于机器学习的研究

How to Break Paradox of Digital Skill Talent Development in Business?--Study Based on Machine Learning

通过机器学习技术,借助XGBoost模型和K-means聚类分析,预测员工是否离职并构建留职员工画像,旨在为破解这一困境提供新视角。基于自愿离职模型,采用Kaggle数据分析平台上某包含19 158个样本点的开源员工离职数据集开展研究。结果发现:(1)存在四种留职员工画像,包括初入职场型、艺商文巾帼型、资深职业型和机会追逐型;(2)根据四种留职类型,提出员工筛选方案和培训策略。结果揭示了机器学习技术在建立准确的员工留职预测模型上的潜力,这对于组织在提供数字技能培训前筛选具有培养价值的员工,以及制定科学的员工保留决策等方面具有理论参考价值和实践指导意义。

In the era of digital economy,organizations need to invest a lot of resources in developing employees’digital skills to maintain a competitive advantage;however,organizations face the risk of employee turnover when they succeed in developing highly-skilled employees.This paradox between talent cultivation and talent turnover has not been properly resolved for a long time.The study aims to provide new perspectives to solve this problem by predicting whether employees will leave and constructing a portrait of retained employees through machine learning techniques with the help of XGBoost model and K-means cluster analysis.The study is based on the voluntary redundancy model and is conducted by using an open source employee turnover dataset containing 19,158 sample points on the Kaggle data analytics platform.The study shows:(1)There are four types of retained employee portraits,including entry-level,women with arts-business-humanities background,senior-stability,and opportunity-chasing;(2)Employee screening plans and training strategies are proposed based on the four types.The findings reveal the potential of machine learning techniques in building accurate employee retention prediction models,which are of theoretical reference and practical guidance for organizations to screen employees with training value before implementing digital skill training and to make scientific employee retention decisions.