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Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007461
URN to cite this document: urn:nbn:de:bvb:703-epub-7461-7

Title data

Hirt, Robin ; Kühl, Niklas ; Martin, Dominik ; Satzger, Gerhard:
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning.
In: Information Technology & Management. (2023) .
ISSN 1573-7667
DOI der Verlagsversion: https://doi.org/10.1007/s10799-023-00399-7

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Abstract

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions—all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.

Further data

Item Type: Article in a journal
Keywords: Meta machine learning; Data confidentiality; Business network; Distributed analytics
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
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems Management > Chair Information Systems Management - Univ.-Prof. Dr.-Ing. Niklas Kühl
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems Management
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7461-7
Date Deposited: 14 Feb 2024 09:13
Last Modified: 14 Feb 2024 09:14
URI: https://epub.uni-bayreuth.de/id/eprint/7461

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