Machine learning (ML) analyses offer great potential to craft profound advice for augmenting managerial decision-making. Yet, even the most promising ML advice cannot improve decision-making if it is not utilized by decision makers. We therefore investigate how ML analyses influence decision makers’ utilization of advice and resulting decision-making performance. By analyzing data from 239 ML-supported decisions in real-world organizational scenarios, we demonstrate that decision makers’ utilization of ML advice depends on the information quality and transparency of ML advice as well as decision makers’ trust in data scientists’ competence. Furthermore, we find that decision makers’ utilization of ML advice can lead to improved decision-making performance, which is, however, moderated by the decision makers’ management level. The study’s results can help organizations leverage ML advice to improve decision-making and promote the mutual consideration of technical and social aspects behind ML advice in research and practice as a basic requirement.

机器学习 (ML) 分析具有巨大的潜力，可以为增强管理决策制定深刻的建议。然而，如果决策者不加以利用，即使是最有前途的机器学习建议也无法改善决策。因此，我们研究机器学习分析如何影响决策者对建议的利用以及由此产生的决策绩效。通过分析现实组织场景中 239 个 ML 支持的决策的数据，我们证明决策者对 ML 建议的利用取决于 ML 建议的信息质量和透明度，以及决策者对数据科学家能力的信任。此外，我们发现决策者对机器学习建议的利用可以提高决策绩效，但这会受到决策者的管理水平的调节。