Finding the right fuel for the analytical engine: Expanding the leader trait paradigm through machine learning?
Finding the right fuel for the analytical engine: Expanding the leader trait paradigm through machine learning?
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DOI：
10.1016/j.leaqua.2019.05.005
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
B. Spisak;P. V. D. Laken;B. Doornenbal
中科院分区：
文献类型：
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作者：
B. Spisak;P. V. D. Laken;B. Doornenbal
我们利用自我报告的性格数据和对不同背景下的 973 名管理者的 360 度绩效评估，利用一系列机器学习方法研究了领导者特质范式。我们发现，在低维条件（即少量预测变量）下预测领导者有效性时，相对简单的线性普通最小二乘模型结合了特征和背景的直接影响，与我们表现最好的复杂机器学习替代方案（例如套索和随机森林）表现相同。然后我们增加了维度，发现更新的机器学习方法表现出色。总体而言，我们的计算密集型方法支持以下论点：（a）特征和环境的直接影响（而不是相互作用）是领导者有效性的重要预测因素，以及（b）适当匹配方法、模型和数据的组合（从简单和传统到复杂和新颖）创建了一个强大的机器学习引擎来研究领导力。最后，我们提供了未来研究的机会，讨论了实际影响，并为那些有兴趣更多地了解这一分析未来的人提供了资源列表。
Using self-report personality data and 360-degree performance evaluations of 973 managers across various contexts, we investigated the leader trait paradigm using a range of machine learning methods. We found that a relatively simple linear ordinary least squares model incorporating direct effects of traits and context performed equally as well as our best performing complex machine learning alternatives (e.g., lasso and random forests) at predicting leader effectiveness under low-dimension conditions (i.e., a small number of predictors). We then increased dimensionality and found that newer machine learning methods excelled. Overall, our computationally intensive approach supports the argument that (a) direct effects (not interactions) of traitsandcontext are important predictors of leader effectiveness and (b) appropriately matching combinations of methods, models, and data (from simple and conventional to complex and novel) creates a powerful machine learning engine for investigating leadership. We end with opportunities for future research, discuss practical implications, and provide a list of resources for those interested in learning more about this analytical future.