Chapter 11

Chapter 11

1. dimensions of knowledge

   • data
   • information
   • knowledge
   • wisdom

2. knowledge is...

   • a firm's asset: intangible, valuable
   • tacit and explicit
   • cognitive, social & individual
   • sticky, situated, contextual
   • situational: conditional, contextual

3. value chain of knowledge

   • knowledge storage
     • databases, mgmt should support storage systems and reward employees
   • knowledge acquisition
     • document tacit and explicit, create knowledge, track data
   • knowledge application
     • provide ROI, become part of decision making
   • knowledge dissemination
     • portals, emails, search engines, collaboration tools

4. building capital

   • CKO: chief knowledge officer
   • COP: communities of practice

5. knowledge mgmt systems

   • enterprise-wide KMS
   • knowledge work systems
   • intelligent techniques

6. enterprise content mgmt systems

   • capture, store, retrieve preserve
   • structured: formal docs, semistructured: emails

7. knowledge workers

   • collaborate through networks and social media
   • keep current knowledge, internal consultants, change agents (eval, initiate, and promote change)
   • systems: create new knowledge and integrate it into business
     • examples: Computer-aided design, virtual reality, augmented reality, virtual reality modelling language (VRML)

8. expert systems

   • knowledge base: model human knowledge as rules
   • inference engine:strategy to search knowledge base
   • forward chaining: interface engine begins w/ information entered
   • backward chaining: search starts w/ hypothesis

9. intelligent techniques

   • case-based reasoning (CBR): describes past human experiences, searches cases for similarity find closest fit (continuously expanded and refined organizational intelligence
   • fuzzy logic: rule-based technology, describe phenomenon, flexible rules, solutions w/ expertise
   • machine learning: computers learn by extracting info
     • neural networks: find patterns and relationships in data and learn those patterns - trained by humans who feed data inputs w/known outputs
   • genetic algorithms: find optimal solution based on evolutionary process
   • intelligent agents: w/out human intervention, developed to model consumer behaviour, supply chain, and stock markets
   • hybrid al systems: integration of egentic, fuzzy, neural, and expert systems