Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/241306
Authors: 
Bento, Fabio
Garotti, Luciano
Year of Publication: 
2019
Citation: 
[Journal:] Journal of Open Innovation: Technology, Market, and Complexity [ISSN:] 2199-8531 [Volume:] 5 [Year:] 2019 [Issue:] 1 [Pages:] 1-10
Publisher: 
MDPI, Basel
Abstract: 
Changes in workplace demographics in the oil and gas industry have raised a concern about the risks of a knowledge-loss crisis due to mass retirement. The industry response has often consisted of strategies aimed at mapping knowledge across organizational units, codifying knowledge in databases, and mentoring new staff. However, such common managerial responses show important limitations in terms of grasping tacit and network-based dimensions of knowledge in complex oil production operations. Therefore, there is an industrial need for innovative knowledge management practices. In this conceptual article, we look at the knowledge-loss crisis from the perspective of network resilience in complex systems. A central assumption here is that it is important to look at retiring staff not only in terms of their explicit knowledge, but also in relation to their roles in evolving networks of interactions. Why do some social systems adapt to the departure of some individuals, recover from eventual knowledge-loss crises, and keep performing its functions? From an anticipatory logic, network analysis may show the initial conditions of a system and identify possible loss scenarios. From an adaptive logic, network analysis may inform interventions aimed at facilitating processes of interactions from which new knowledge may emerge and spread. Integrated operations may be a step in this direction.
Subjects: 
oil and gas industry
resilience
aging workforce
network analysis
complex adaptive systems
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

Files in This Item:
File
Size
242.35 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.