TU Darmstadt / ULB / TUbiblio

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

Sturm, Timo ; Pumplun, Luisa ; Gerlach, Jin ; Kowalczyk, Martin ; Buxmann, Peter (2023)
Machine Learning Advice in Managerial Decision-Making: The Overlooked Role of Decision Makers’ Advice Utilization.
In: The Journal of Strategic Information Systems, 32 (4)
doi: 10.1016/j.jsis.2023.101790
Article, Bibliographie

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.

Item Type: Article
Erschienen: 2023
Creators: Sturm, Timo ; Pumplun, Luisa ; Gerlach, Jin ; Kowalczyk, Martin ; Buxmann, Peter
Type of entry: Bibliographie
Title: Machine Learning Advice in Managerial Decision-Making: The Overlooked Role of Decision Makers’ Advice Utilization
Language: English
Date: December 2023
Journal or Publication Title: The Journal of Strategic Information Systems
Volume of the journal: 32
Issue Number: 4
DOI: 10.1016/j.jsis.2023.101790
URL / URN: https://www.sciencedirect.com/science/article/abs/pii/S09638...
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.

Additional Information:

Article ID 101790

Divisions: 01 Department of Law and Economics
01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete
01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Information Systems
01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management
Date Deposited: 15 Aug 2023 12:15
Last Modified: 15 Aug 2023 12:15
PPN:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details