Resilience beyond Formal Structures: A Network Perspective towards the Challenges of an Aging Workforce in the Oil and Gas Industry
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Date
2019-03-04Metadata
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Original version
Bento F, Garotti L. Resilience beyond Formal Structures: A Network Perspective towards the Challenges of an Aging Workforce in the Oil and Gas Industry. Journal of Open Innovation: Technology, Market, and Complexity. 2019;5(1) https://dx.doi.org/10.3390/joitmc5010015Abstract
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.
Publisher
MDPISeries
Journal of Open Innovation: Technology, Market, and Complexity;Volume 11, Issue 5Journal
Journal of Open Innovation: Technology, Market, and Complexity
Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).