Knowledge Networks and Next Generation Knowledge Networks
Knowledge networks have been discussed as strategy to formalize intra- and inter-organizational knowledge transfer and creation.
One constituting characteristic of these networks is the focus on social networks. Knowledge networks thereby aspire to deliver better performances in knowledge management by introducing organizational structures, which encourage the establishment of social relationships, which foster the exchange of knowledge.
This project explores Next Generation Knowledge Networks, which extend traditional knowledge networks in a number of aspects. The following table provides an overview of key differences between knowledge networks (KN) and next generation knowledge networks (XNETs). Below, each of the aspects is discussed in greater detail.
|Knowledge Networks||Next Generation Knowledge Networks|
|Knowledge process focus||Business process focus|
|Tacit||Encultured, embrained, embodied, embedded and encoded|
|Qual/Quant and action research||Design science research|
Formal/ Ad Hoc Traditional knowledge networks are often implemented in top-down approach. Top management identifies strategic goals, which are to be achieved by the means of knowledge management. Key experts are identified and a networks is composed of these experts. Formal mechanisms such as regular meetings or employee placements are mechanisms of choice to assure the success of these networks.
However, knowledge-related work is often conducted in a erratic and unpredictable manner; for instance, in innovation processes or organizational decision processes. XNETs therefore aspire to deliver mechanisms, which account for ad hoc processes or improvisation/bricolage and embed these in an overarching knowledge network framework.
Knowledge process focus/ Business process focus Traditional knowledge networks often origin from knowledge management initiatives and have a focus on the traditional knowledge processes such as knowledge creation and sharing. These result, for instance, in mechanisms such as meetings, in which knowledge is to be exchanged between experts.
However, organizational reality often shows that there is little time for knowledge management activities, which are said to interfere with the activities, which generate immediate value. In order to find ways in which knowledge can be managed ‘along the way‘ in the execution of processes, XNETs aspire to provide means to manage knowledge which closely align with business processes.
Social/ Socio-technological Knowledge networks originate from business networks and social networks and the necessity to align these networks. Although technology is often mentioned as enabling factor for the success of knowledge networks, little focus is given to the exact nature of technological artifacts, which could be employed for the network.
XNETs aspire to closely align the dimension of social and business networks with technological mechanisms, which can support and guide the development of knowledge networks.
Tacit/ 5 Dimensions of Knowledge One of the key discoveries of knowledge management research is the identification of tacit knowledge as often most important factor in the management of knowledge. Knowledge networks often focus on tacit knowledge.
Tacit knowledge is doubtlessly the most valuable form of knowledge, especially for high-value processes such as organizational innovation. XNETs follow a classification of knowledge, which divides it into five dimensions. Only tacit dimensions, as embedded in enactment, generate value, however, great attention is further given to implicit and encoded dimensions, as tacit knowledge can be scaffolded and channeled by these dimensions.
Qual/Quant and action research/ Design science research Research on intra- and inter-organizational research has been conducted using quantitative and qualitative studies, for instance involving action research. Fewer attention is given to design science research involving the design, implementation and evaluation of closely aligned organizational and technological artifacts to support knowledge networks.