Managing Knowledge for Asset Management: Shifting from Process to Relational Frames

Abstract

AbstractThe purpose of this paper is to review existing knowledge management (KM) practices within the field of asset management, identify gaps, and propose a new approach to managing knowledge for asset management. Existing approaches to KM in the field of asset management are incomplete with the focus primarily on the application of data and information systems, for example the use of an asset register. It is contended these approaches provide access to explicit knowledge and overlook the importance of tacit knowledge acquisition, sharing, and application. In doing so, current KM approaches within asset management tend to neglect the significance of relational factors; whereas studies in the KM field have showed that relational modes such as social capital is imperative for effective KM outcomes. In this paper, we argue that incorporating a relational approach to KM is more likely to contribute to the exchange of ideas and the development of creative responses necessary to improve decision making in asset management. This conceptual paper uses extant literature to explain KM antecedents and explore its outcomes in the context of asset management. KM is a component in the new integrated strategic asset management (ISAM) framework developed in conjunction with asset management industry associations (AAMCoG 2012) that improves asset management performance. In this paper, we use Nahapiet and Ghoshal’s [24] model to explain antecedents of relational approach to KM. Further, we develop an argument that relational KM is likely to contribute to the improvement of the ISAM framework components, such as organizational strategic management, service planning, and delivery. The main contribution of the paper is a novel and robust approach to managing knowledge that leads to the improvement of asset management outcomes.KeywordsSocial CapitalService DeliveryKnowledge ManagementKnowledge SharingExplicit KnowledgeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.