Machine Learning Advice in Managerial Decision-Making: The Overlooked Role of Decision Makers’ Advice Utilization

Timo Sturm, Luisa Pumplun, Jin Gerlach, Martin Kowalczyk and Peter Buxmann

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

Abstract: 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.

Date: 2023-12
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-hrm
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Published in The Journal of Strategic Information Systems 4 (2023-12)

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Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:139044

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