Mass collaborative knowledge management

Abstract

Purpose – The purpose of this paper is to reveal the underlying principles of knowledge processing in a new era of mass collaboration and provide an integrated guideline for organizational knowledge management (KM) based on identifying the gaps between the existing KM theories and emerging knowledge initiatives such as Web 2.0, Pro-Am, Crowdsourcing, as well as Open Innovation. Design/methodology/approach – This research mainly employs three types of research methodologies: Literature study was conducted to connect this study with conventional theories in KM and propose the main principles of Mass Collaborative Knowledge Management (MCKM). Object-oriented modeling was used for designing its interaction model. The case study method was employed to discuss the two typical practices carried out by Goldcorp Inc. as well as the Defence Advanced Research Projects Agency. Findings – This paper proposes the novel KM paradigm called MCKM and also provides its main principles and the interaction model. First, it identifies the gaps between emerging practices and existing KM theories. Second, it embraces the long tails into the scope of organizational KM and extends the scope of prevailing KM studies. Third, it falls back on Pro-Ams to save the costs of and to reduce the risk to organizational KM as well. Fourth, it highlights the advantages of opening organizational internal knowledge and transforms the core beliefs in conventional KM. Finally, it classifies organizational knowledge into two types, domain knowledge and non-domain knowledge, and provides some managing policies, respectively. Practical implications – Introducing MCKM into organizational KM will not only enhance the organizational knowledge creation and sharing, but also help an organization build its open knowledge ecosystem. Originality/value – This is a paper to introduce a new direction of KM studies, which guides an organization to build an open knowledge ecosystem by implementing mass collaborations and taking advantages of the complementary advantages of men and machines in knowledge processing.

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