Prior research on the relations between the five-factor model (FFM) of personality traits and job performance has suggested mixed findings: Some studies pointed to linear relations, while other studies revealed nonlinear relations. This study addresses these gaps using machine learning (ML) methods that can model complex relations between the FFM traits and job performance in a more generalizable way, particularly interpretable ML techniques that can more effectively reveal the nature (linear, curvilinear, interactive) and strength (feature/relative importance) of the personality-job performance relations. Overall, the results based on a sample of 1,190 employees suggest that nonlinear ML methods perform slightly yet consistently better than linear regression methods in modeling the relation of job performance with FFM facets, but not with factors. On the factor level, conscientiousness exhibits a noticeable curvilinear relation with job performance, and it also interacts with other FFM factors to predict job performance. Conscientiousness displays the strongest feature importance across job types, followed by agreeableness. On the facet level, most FFM facets show limited evidence for curvilinear and interactive (with other facets) relations with job performance. While several conscientiousness facets (order, deliberation, self-discipline) display the strongest feature importance in predicting job performance, some agreeableness (straightforwardness, altruism) and extraversion (positive emotionality) facets also emerge as important features for different sales job types (corporate vs. individual sales). We discuss the implications of these findings for research and practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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重新审视人格与工作绩效关系的本质和强度：可解释机器学习的新见解。


先前对人格特质五因素模型（FFM）与工作绩效之间关系的研究得出了不同的结果：一些研究指出线性关系，而另一些研究则揭示非线性关系。本研究使用机器学习 (ML) 方法来解决这些差距，这些方法可以以更通用的方式对 FFM 特征和工作绩效之间的复杂关系进行建模，特别是可解释的 ML 技术，可以更有效地揭示性质（线性、曲线、交互）和强度人格与工作绩效关系的（特征/相对重要性）。总体而言，基于 1,190 名员工样本的结果表明，在对工作绩效与 FFM 方面（但与因素）的关系进行建模时，非线性 ML 方法的表现略好于线性回归方法，但始终优于线性回归方法。在因素层面上，责任心与工作绩效表现出明显的曲线关系，并且还与其他FFM因素相互作用来预测工作绩效。在所有工作类型中，尽责性表现出最强的特征重要性，其次是宜人性。在方面层面上，大多数 FFM 方面显示与工作绩效的曲线和交互（与其他方面）关系的证据有限。虽然一些责任心方面（秩序、深思熟虑、自律）在预测工作绩效方面表现出最强的特征重要性，但一些宜人性（直率、利他主义）和外向性（积极情绪）方面也成为不同销售工作类型（公司销售工作与销售工作类型）的重要特征。个人销售）。我们讨论这些发现对研究和实践的影响。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。