通过特征预测领导地位 情绪智力和认知能力
Bogdan S Zadorozhny1, K V Petrides1, Yongtian Cheng1
1Department of Psychology, University College London (UCL), London WC1E 6BT, UK.
Behavioral sciences (Basel, Switzerland)
|March 28, 2025
概括
情绪智能特征 (EI特征) 比认知能力更好地预测领导角色. 机器学习,特别是随机森林,有效地识别了关键预测因素,如管理职位的社交能力.
科学领域:
- 组织心理学 组织心理学
- 领导力研究 领导力研究
- 数据科学数据科学数据科学
背景情况:
- 管理角色预测涉及人口统计,认知能力和特征情感智能 (特征EI).
- 传统的线性模型可能无法捕捉到领导力预测的复杂性.
- 监督机器学习 (SML) 为分析复杂关系提供了先进的方法.
研究的目的:
- 为了比较特征EI的预测能力和管理角色的认知能力.
- 评估SML算法的有效性与传统的线性方法相比.
- 用SML识别影响领导职位的关键因素和相互作用.
主要方法:
- 物流回归作为传统的线性基线.
- 监督机器学习 (SML) 算法的应用.
- 在SML中利用特征重要性和相互作用分析.
主要成果:
- 与认知能力相比,特征EI表现出优越的预测能力.
- 特征EI的社交因素特别有影响.
- 随机森林 (RF) 算法显示出显著的预测效用.
结论:
- 像随机森林一样,SML方法对于理解领导力复杂性是有价值的.
- 性格特征EI,尤其是社交性,是管理角色的关键预测因素.
- 未来的研究应该在领导力研究中探索更复杂的SML方法.
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