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Towards a data science platform for improving SME collaboration through Industry 4.0 technologies
Han, Hui
Fraunhofer Institute for Experimental Software Engineering IESE, Kaiserslautern, Germany.
ORCID iD:
0000-0001-9320-3808
Trimi, Silvana
Department of Supply Chain Management and Analytics, College of Business, University of Nebraska - Lincoln, Lincoln, USA.
ORCID iD:
0000-0002-9482-3731
2022 (English)
In:
Technological forecasting & social change, ISSN 0040-1625, E-ISSN 1873-5509, Vol. 174, article id 121242
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 174, article id 121242
National Category
Computer Sciences
Identifiers
URN:
urn:nbn:se:ltu:diva-110030
DOI:
10.1016/j.techfore.2021.121242
ISI:
000709726300010
Scopus ID:
2-s2.0-85115968241
OAI: oai:DiVA.org:ltu-110030
DiVA, id:
diva2:1899074
Available from:
2024-09-19
Created:
2024-09-19
Last updated:
2025-12-03
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