Maasland Christian, Weißmüller Kristina S
Independent Researcher, Hamburg, Germany.
KPM Center for Public Management, University of Bern, Bern, Switzerland.
Front Psychol. 2022 May 25;13:779028. doi: 10.3389/fpsyg.2022.779028. eCollection 2022.
Algorithms have become increasingly relevant in supporting human resource (HR) management, but their application may entail psychological biases and unintended side effects on employee behavior. This study examines the effect of the type of HR decision (i.e., promoting or dismissing staff) on the likelihood of delegating these HR decisions to an algorithm-based decision support system. Based on prior research on algorithm aversion and blame avoidance, we conducted a quantitative online experiment using a 2×2 randomly controlled design with a sample of = 288 highly educated young professionals and graduate students in Germany. This study partly replicates and substantially extends the methods and theoretical insights from a 2015 study by Dietvorst and colleagues. While we find that respondents exhibit a tendency of delegating presumably unpleasant HR tasks (i.e., dismissals) to the algorithm-rather than delegating promotions-this effect is highly conditional upon the opportunity to pretest the algorithm, as well as individuals' level of trust in machine-based and human forecast. Respondents' aversion to algorithms dominates blame avoidance by delegation. This study is the first to provide empirical evidence that the type of HR decision affects algorithm aversion only to a limited extent. Instead, it reveals the counterintuitive effect of algorithm pretesting and the relevance of confidence in forecast models in the context of algorithm-aided HRM, providing theoretical and practical insights.
算法在支持人力资源（HR）管理方面的相关性日益增强，但其应用可能会带来心理偏差以及对员工行为产生意想不到的副作用。本研究考察了人力资源决策类型（即晋升或解雇员工）对将这些人力资源决策委托给基于算法的决策支持系统的可能性的影响。基于先前关于算法厌恶和责任规避的研究，我们采用2×2随机对照设计进行了一项定量在线实验，样本为德国288名受过高等教育的年轻专业人员和研究生。本研究部分复制并大幅扩展了迪特沃斯特及其同事2015年研究中的方法和理论见解。虽然我们发现受访者表现出将可能令人不快的人力资源任务（即解雇）委托给算法的倾向，而不是委托晋升任务，但这种效应在很大程度上取决于对算法进行预测试的机会，以及个人对基于机器和人工预测的信任程度。受访者对算法的厌恶超过了通过委托进行的责任规避。本研究首次提供了实证证据，证明人力资源决策类型仅在有限程度上影响算法厌恶。相反，它揭示了算法预测试的反直觉效应以及在算法辅助人力资源管理背景下对预测模型信心的相关性，提供了理论和实践见解。
