预测员工的组织公民行为:使用人工智能技术进行的比较研究
Akriti Gupta1, Aman Chadha2, Mayank Kumar3
1IIBS, Bengaluru, Karnataka; Department of Management Studies, Indian Institute of Information Technology, Allahabad, Prayagraj, Uttar Pradesh, India.
Acta psychologica
|December 6, 2025
概括
人工智能 (AI) 使用机器学习准确预测组织公民行为 (OCB). 认为增长的可持续培训实践 (STP) 显著影响了OCB,提供了HR数据驱动的洞察力.
科学领域:
- 人力资源管理 人力资源管理
- 组织行为 组织行为
- 人力资源中的人工智能
背景情况:
- 人工智能越来越多地融入人力资源,因此需要对员工行为的预测模型.
- 预测组织公民行为 (OCB) 对组织成功和学术研究至关重要.
- 现有的研究突出了各种因素对OCB的影响,但人工智能驱动的预测模型的探索较少.
研究的目的:
- 开发和验证组织公民行为 (OCB) 的预测模型.
- 为了比较七种不同的机器学习模型在预测OCB方面的有效性.
- 确定OCB的关键预测因素,包括可持续培训实践 (STP),心理合同履行 (PCF),工作满意度 (JS) 和人口统计数据.
主要方法:
- 一项涉及印度公司224名员工的定量研究.
- 对七个机器学习算法的比较分析:SVM,LDA,随机森林,XGBoost,KNN,天真贝叶斯和ANN.
- 使用STP,PCF,JS和人口变量开发OCB的预测模型.
主要成果:
- 人工智能模型显示了OCB的强大预测能力.
- 人工神经网络 (ANN) 模型获得了最高的准确率,达到93.33%.
- 可持续培训实践 (STP),当被员工认为促进个人成长时,它成为了OCB的重要预测因素.
结论:
- 人工智能驱动的预测模型为人力资源管理提供了宝贵的工具.
- 人力资源经理可以利用人工智能模型的数据驱动见解来培养所需的员工行为.
- 该研究强调了STP在通过数据驱动方法增强OCB方面的重要性.
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