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The Impact of Resource Allocation on the Machine Learning Lifecycle

DOI zum Zitieren der Version auf EPub Bayreuth:
URN to cite this document: urn:nbn:de:bvb:703-epub-7534-3

Title data

Duda, Sebastian ; Hofmann, Peter ; Urbach, Nils ; Völter, Fabiane ; Zwickel, Amelie:
The Impact of Resource Allocation on the Machine Learning Lifecycle.
In: Business & Information Systems Engineering. (2023) .
ISSN 1867-0202
DOI der Verlagsversion:

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Projektgruppe WI Künstliche Intelligenz
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Projektgruppe WI Strategisches IT-Management
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An organization’s ability to develop Machine Learning (ML) applications depends on its available resource base. Without awareness and understanding of all relevant resources as well as their impact on the ML lifecycle, we risk inefficient allocations as well as missing monopolization tendencies. To counteract these risks, the study develops a framework that interweaves the relevant resources with the procedural and technical dependencies within the ML lifecycle. To rigorously develop and evaluate this framework the paper follows the Design Science Research paradigm and builds on a literature review and an interview study. In doing so, it bridges the gap between the software engineering and management perspective to advance the ML management discourse. The results extend the literature by introducing not yet discussed but relevant resources, describing six direct and indirect effects of resources on the ML lifecycle, and revealing the resources’ contextual properties. Furthermore, the framework is useful in practice to support organizational decision-making and contextualize monopolization tendencies.

Further data

Item Type: Article in a journal
Keywords: ML management; Machine learning lifecycle; Artificial intelligence; Resource-based view; Design science research
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Faculties > Faculty of Law, Business and Economics
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7534-3
Date Deposited: 13 Mar 2024 08:32
Last Modified: 13 Mar 2024 08:33


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