1. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang, arafatmy 9-1 Chapter 9 Knowledge Management Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-2 Learning Objectives Define knowledge. Learn the characteristics of knowledge management. Describe organizational learning. Understand the knowledge management cycle. Understand knowledge management system technology and how it is implemented. Learn knowledge management approaches. Understand the activities of the CKO and knowledge workers. Describe the role of knowledge management in the organization. Be able to evaluate intellectual capital. Understand knowledge management systems implementation. Illustrate the role of technology, people, and management with regards to knowledge management. Understand the benefits and problems of knowledge management initiatives. Learn how knowledge management can change organizations.

3. Knowledge Mgmt Basically KM is collaborative computing at the Organization level. The goal is to capture, store, maintain, and deliver useful knowledge in a meaningful form to anyone who needs it anyplace and anytime within an org. Knowledge = Intellectual Capital © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-3

4. The Importance of Managing Knowledge Intellectual capital is appreciable assets, most assets depreciate. Knowledge work is increasing in importance as much as the increase in service economy. Employees with the most intellectual capital have become volunteers to improve B.P.الابداع Most managers ignore intellectual capital and lose out on the benefits of its use. Employees with the most intellectual capital are often the least appreciated. Knowledge mismanagement. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-4

5. Encourage Knowledge SHARING Discouragement due to org. cultural or technical barriers. Educate people on the value of knowledge. Refurbish reward and recognition system. Act as a role model for sharing. Make it a job requirement. Make the tech. work for people; don’t expect the people to work for the tech. (machines in front of machines) Educate people about the value in the know. It is OK to make mistakes, “No one is perfect” © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-5

6. Why people do not share KNOWLWDGE It can cripple an org. As people refuse to share what they know, a situation created by Org. Culture and Barriers. Why people (hide away) their knowledge.? Knowledge as a source of power and will guarantee job security. People wont get credit for sharing knowledge. People don’t have time, or know how to share. People don’t know the value or how much they know. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-6

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-7 Siemens Knows What It Knows Through Knowledge Management Knowledge management –Community of interest Repositories (storehouse) Communities of practice Informal knowledge-sharing techniques (Social Networks) –Employee initiated Created ShareNet –Easy to share knowledge –Incentives for posting –Internal evangelists responsible for training, monitoring, and assisting users –Top management support

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-8 Knowledge Management Process to help organization identify, select, organize, disseminate, transfer information Structuring enables problem-solving, dynamic learning, strategic planning, decision-making and reduce redundancy Leverage value of intellectual capital through reuse The age of knowledge worker.

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-9 9.3 Knowledge Data = collection of facts, measurements, statistics Information = organized data Knowledge = contextual, relevant, actionable info. –Strong experiential and reflective elements –Good leverage and increasing returns –Dynamic –Branches and fragments with growth –Knowledge is valuable when it is shared. –Uncertain value in sharing –Evolves over time with experience

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-10 Knowledge Explicit (Leaky) knowledge –Objective, rational, technical –Policies, goals, strategies, papers, reports –Codified –Leaky knowledge Tacit (sticky) knowledge –Subjective, cognitive, experiential learning –Highly personalized –Difficult to formalize –Cumulative store of the experiences. –Within the brain of individuals or embedded in the group interaction.

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-11 Knowledge Management Systematic and active management of ideas, information, and knowledge residing within organization’s employees Knowledge management systems –Use of technologies to manage knowledge –Used with turnover, change, downsizing –Provide consistent levels of service

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-12 9.4 Organizational Learning Learning organization –Ability to learn from past –To improve, organization must learn –3 Issues: Meaning, Management, Measurement –5 Activities: Problem-solving, Creative experimentation, learning from past, learning from acknowledged best practices, transfer of knowledge within organization -, Organizational memory way to save and share. Must have organizational memory to have a learning Org. Categories ( Well ) 1- Individual 2-information 3-Culture 4-Transformation 5-Structural

13. “Generally when a technology project fails, it is because the technology doesn’t match the organization’s culture” Organizational learning –Develop new knowledge that have the potential to influence behavior. –Corporate memory is critical for success. –Org. Learning Process: Know. Acquisition. Know. Sharing. Know. Utilization. Organizational culture –Pattern of shared basic assumptions –Can cause KM success or failure. –Difficult to measure the impact of culture on org. (Strong culture produce strong results(ROI, Net income, increase in stock price) © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-13

14. KSF To create an enterprise with a culture of continuous change where employees are not threatened by change but are encouraged by it because they believe it will improve their quality of life. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-14

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-15 Knowledge Management Initiatives Aims –Make knowledge visible –Develop knowledge intensive culture –Build knowledge infrastructure Surrounding processes –Creation of knowledge –Sharing of knowledge –Seeking out knowledge –Using knowledge

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-16 Knowledge Management Initiatives Knowledge creation –Generating new ideas, routines, insights –Modes Socialization, externalization, internalization, combination Knowledge sharing –Willing explanation to another directly or through an intermediary Knowledge seeking –Knowledge sourcing

17. Core competency linked to Tacit and Explicit Knowledge Core Competencies of the organization Explicit Knowledge Policies, Patents, Decisions, strategies Tacit Knowledge Expertise, Know- how, Org. Culture, Values © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-17 Convert tacit know. To measurable Explicit knowledge Process of explication may generate tacit knowledge

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-18 Approaches to Knowledge Management Process Approach –Codifies knowledge Formalized controls, approaches, technologies Fails to capture most tacit knowledge Practice Approach –Assumes that most knowledge is tacit Informal systems –Social events, communities of practice, person-to- person contacts Challenge to make tacit knowledge explicit, capture it, add to it, transfer it

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-19 Approaches to Knowledge Management Hybrid Approach –Practice approach initially used to store explicit knowledge –Tacit knowledge primarily stored as contact information –Best practices captured and managed Best practices –Methods that effective organizations use to operate and manage functions Knowledge repository –Place for capture and storage of knowledge –Different storage mechanisms depending upon data captured

20. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-20 9.6 Knowledge Management System Cycle Creates knowledge through new ways of doing things Identifies and captures new knowledge Places knowledge into context so it is usable Stores knowledge in repository Reviews for accuracy and relevance Makes knowledge available at all times to anyone Disseminate

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-21 Components of Knowledge Management Systems KMS are developed using 3 sets of technologies: 1.Communication Access knowledge Communicates with others 2.Collaboration Perform group work Synchronous or asynchronous Same place/different place 3.Storage and retrieval Capture, storing, retrieval, and management of both explicit and tacit knowledge through collaborative systems

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-22 Components of Knowledge Management Systems Supporting technologies –Artificial intelligence KM is a systems often have AI methods embedded in them Expert systems, neural networks, fuzzy logic, intelligent agents –Intelligent agents Systems that learn how users work and provide assistance –Knowledge discovery in databases Process used to search for and extract information –Internal = data and document mining –External = model marts and model warehouses –XML Extensible Markup Language Enables standardized representations of data Better collaboration and communication through portals

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-23 Knowledge Management System Implementation Challenge to identify and integrate components –Early systems developed with networks, groupware, databases Knowware is Technology tools that support KM 1.Collaborative computing tools –Groupware 2.Knowledge servers 3.Enterprise knowledge portals 4.Document management systems DMS 1.Content management systems 5.Knowledge harvesting tools 6.Search engines 7.Knowledge management suites –Complete out-of-the-box solutions

24. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-24 9.7 Knowledge Management System Implementation Implementation –Software development companies. –Information systems vendors. Consulting firms. – Application Service Providers ASP Outsourcing,

25. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-25 Knowledge Management System Integration Integration with enterprise and information systems DSS/BI –Integrates models and activates them for specific problem Artificial Intelligence –Expert system = if-then-else rules –Natural language processing = understanding searches –Artificial neural networks = understanding text –Artificial intelligence based tools = identify and classify expertise

26. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-26 Knowledge Management System Integration Database –Knowledge discovery in databases CRM –Provide tacit knowledge to users Supply chain management systems –Can access combined tacit and explicit knowledge Corporate intranets and extranets –Knowledge flows more freely in both directions –Capture knowledge directly with little user involvement –Deliver knowledge when system thinks it is needed

27. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-27 9.8 Role of PEOPLE in KM Chief knowledge officer –Senior level –Sets strategic priorities –Defines area of knowledge based on organization mission and goals –Creates infrastructure –Identifies knowledge champions –Manages content produced by groups –Adds to knowledge base CEO –Champion knowledge management Upper management –Ensures availability of resources to CKO Communities of practice Knowledge management system developers –Team members that develop system Knowledge management system staff –Catalog and manage knowledge

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-28 9.9 Valuation ensuring success KM Asset-based approaches –Identifies intellectual assets –Focuses on increasing value Knowledge linked to applications and business benefits approaches –Balanced scorecard –Economic value added –Inclusive valuation methodology –Return on management ratio –Knowledge capital measure Estimated sale price approach

29. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-29 Metrics Financial –ROI –Perceptual, rather than absolute –Intellectual capital not considered an asset Non-financial –Value of intangibles External relationship linkages capital Structural capital Human capital Social capital Environmental capital

30. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-30 Factors Leading to Success and Failure of Systems Success –Companies must assess need –System needs technical and organizational infrastructure to build on –System must have economic value to organization –Senior management support –Organization needs multiple channels for knowledge transfer –Appropriate organizational culture Failure –System does not meet organization’s needs –Lack of commitment –No incentive to use system –Lack of integration

31. End of ch9 KM Than You © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9-31