Enhancing tacit knowledge transfer: tailoring NPD practices

Dann, Zoe and Bettley, Alison (2007). Enhancing tacit knowledge transfer: tailoring NPD practices. In: 8th European Conference on Knowledge Management, 6-7 Sep 2007, Barcelona, Spain.

Abstract

Tacit knowledge – subjective and often largely uncodifiable – represents a critical asset for many organisations. Its effective management is crucial to ensure successful organisational improvement, innovation and change. Yet it is considered to be one of the most difficult areas to manage not least because it depends on myriad planned and unplanned inter-personal interactions. Tacit knowledge transfer is typically not proactively managed by organisations – it is either not recognized as important, or left to chance, a serendipitous by-product of more formally managed activities. Thereby a critical resource with huge untapped potential to improve, even transform, a business is neglected.

While the knowledge management literature contains frequent reference to tacit knowledge transfer practices and their importance, there has to date been little attempt to collate these findings and provide theoretically-based guidance to oganizations seeking to design effective relevant tacit knowledge processes into their innovation and improvement activities. This paper first discusses the process of tacit knowledge transfer and the range of mechanisms used to support it.

The theoretical framework adopts a model that distinguishes four types of development project based on the application of complexity theory: linear, recursive, CAS and chaotic. These are related to the level of innovation and uncertainty in the environment. The NPD process is considered in terms of three stages: concept, development, and post development review. Tacit knowledge transfer emphasis is mapped onto each of the four models and the interplay with explicit codified processes explored.

There are significant implications for managers and organisations. Further research work is proposed to validate and refine the model.

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