Capturing and managing tacit knowledge creates a collective organizational intelligence capability that is the differential for high performance enterprises. Traditional tacit knowledge capture methods are labor-intensive, some of which include mentoring, interviewing and direct observation that rely on the accuracy of those collecting the information. The researchers set out to prove traditional approaches to capturing knowledge assets could be replaced using Web 2.0/3.0 technologies. This paper introduces an innovative approach to harvest, share and manage tacit knowledge created as a by-product of normal cognitive and technical workflow activities within a net-centric environment. The innovative proto-type model, Most Important Knowledge and Expertise (MIKE), utilizes network sensor, integrated semantic, natural language and computational analysis technologies that incorporate the design of classifiers, corpus and taxonomies to identify tacit knowledge embedded in explicit knowledge found in workforce transactional activity containing both formal content (policy, training, and process guides) and unstructured content (emails, wikis, blogs, instant messaging, and social media). Using this combination of Web 2.0/3.0 tools and processes to harvest the tacit-to-explicit knowledge from network transactions and then sharing it via a trusted social network offers human resource, learning and knowledge management practitioner's a new solution design and enterprise tacit knowledge management capability. MIKE is a prototype designed to promote tacit knowledge transfer and high performance using a simple yet robust framework of Web 2.0/3.0 tools to improve expert connectiveness and establish trust networks.
展开▼
