开发一个先进的预测模型,用于新的员工流动意图,利用机器学习技术
Jungryeol Park1, Yituo Feng2, Seon-Phil Jeong3
1Technology Policy Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
Scientific reports
|January 12, 2024
概括
新大学毕业生的高流动率是一个越来越令人担忧的问题. 这项研究使用机器学习来预测营业额的意图,找到工作安全是关键因素,优于传统预测器.
科学领域:
- 组织行为 组织行为
- 人力资源管理 人力资源管理
- 数据科学数据科学数据科学
背景情况:
- 由于招聘和培训成本,新大学毕业生之间不断增加的营业额给企业带来了重大财务挑战.
- 之前的研究发现了影响营业额意向的因素,但缺乏对实际工作变化的预测能力.
- 有效地识别和管理面临流动风险的新员工对于组织稳定至关重要.
研究的目的:
- 开发一种基于机器学习的模型,用于预测新大学毕业生的营业额意图.
- 克服传统计量经济学模型在预测员工流动方面的局限性.
- 确定职业生涯早期专业人员的营业额意向的关键预测因素.
主要方法:
- 利用韩国就业信息局对大学毕业生就业流动路径调查的数据.
- 运用普通最小平方 (OLS) 回归来分析预测因素的影响.
- 实现了机器学习分类器,包括后勤回归 (LR),K-最近邻居 (KNN) 和极端梯度提升 (XGB) 用于模型学习和分类.
主要成果:
- 工作安全成为营业额意向的最重要的预测因素,超过了传统因素,如工作负载的重要性和主要相关性.
- 极端梯度增强 (XGB) 模型实现了最高的预测准确度,达到78.5%.
- 与之前的研究相比,某些传统因素对营业额意向的影响减少或逆转.
结论:
- 机器学习模型,特别是XGB,在预测新毕业生营业额的意图方面取得了重大进展.
- 工作安全和组织满意度是保留早期职业生涯人才的关键因素.
- 组织应重新评估影响营业额的传统因素,并优先考虑工作安全,以改善保留策略.
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