Sharing Knowledge When it Cannot be Made Explicit: The Case of Product Lifecycle Management Systems

Sharing Knowledge When it Cannot be Made Explicit: The Case of Product Lifecycle Management Systems

Pierre-Emmanuel Arduin (CNRS DRM, Université Paris-Dauphine, PSL Research University, Paris, France), Julien Le Duigou (Roberval UMR CNRS 7337, Université de Technologie de Compiègne, Compiègne, France), Marie-Hélène Abel (Heudiasyc UMR CNRS 7253, Université de Technologie de Compiègne, Compiègne, France), and Benoît Eynard (Roberval UMR CNRS 7337, Université de Technologie de Compiègne, Compiègne, France)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJKBO.2018100102
OnDemand PDF Download:
Available
$29.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $29.50

Abstract

Information systems often strengthen a preference for working alone: interoperability as much as interpretation variance restrain the ability of people and systems to interact and to work together within an extended enterprise. In this article, the authors propose to extend product lifecycle management (PLM) systems in order to share not only (1) knowledge that has been made explicit and which is strongly contextualized so that there is no interpretation variance, but also (2) knowledge that cannot be made explicit and which remains tacit knowledge, needing social interaction and shared understanding to be actually shared. The use of a collaborative platform is proposed in this article in order to allow stakeholders to produce a shared understanding of what a concept means through the use of ontologies. The conditions as well as the limits of the proposition are discussed at the end of this article.
Article Preview
Top

Background Literature

Relying on the assumption that within extended enterprises individuals may interpret differently the same information, this work focuses on knowledge as being the result of the interpretation of information by someone, according to Tsuchiya (1993).

The vision of knowledge in the organization adopted in this paper is introduced in the first part of this section. A few models of PLM are then presented in the second part of this section.

Complete Article List

Search this Journal:
Reset
Volume 13: 1 Issue (2023)
Volume 12: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 11: 4 Issues (2021)
Volume 10: 4 Issues (2020)
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing