Material Intelligence: Cross-Organizational Collaboration Driven by Detailed Material Data

dc.contributor.author Hakanen, Esko
dc.contributor.author Eloranta, Ville
dc.contributor.author Töytäri, Pekka
dc.contributor.author Rajala, Risto
dc.contributor.author Turunen, Taija
dc.date.accessioned 2016-12-29T00:13:40Z
dc.date.available 2016-12-29T00:13:40Z
dc.date.issued 2017-01-04
dc.description.abstract The application of the Internet of Things (IoT) technologies has the potential to reshape inter-organizational collaboration across industries. This study explores the influences of the use of IoT for information sharing in the steel industry networks. Shared data may have multiple uses, including optimization, integration, automatization, and adaptation of objects in their environments. Our study indicates that increase in the information intensity of products and processes changes the nature of competition in the industry. To date, research on IoT has mainly proposed its use in independent nodes and clusters possessing excessive data from their own actions. Conversely, our study emphasizes the benefits that accrue from intensified collaboration. Our findings emphasize that IoT enabled material intelligence can restructure companies’ roles and responsibilities in the steel industry supply networks. This can be achieved by bridging the structural holes in the inter-organizational networks.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.043
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41192
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject cross-organizational collaboration
dc.subject intelligent materials
dc.subject internet of things
dc.subject material intelligence
dc.subject structural holes
dc.title Material Intelligence: Cross-Organizational Collaboration Driven by Detailed Material Data
dc.type Conference Paper
dc.type.dcmi Text
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