14127_knowledge mgnt ch11

Chapter 11 Knowledge Management: 

9- 1 Chapter 11 Knowledge Management Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems

Knowledge Management: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 2 Knowledge Management Process that helps organizations to identify, select, organize, disseminate, transfer imp. information & expertise Knowledge Management is the collection of processes that govern the creation, dissemination, and utilization of knowledge Enables effective & efficient problem-solving, dynamic learning, strategic planning, decision-making KM is concerned with the entire process of discovery and creation of knowledge, dissemination of knowledge , and the utilization of knowledge

Knowledge Management: 

Knowledge Management Knowledge management is the systematic & active management of ideas, info & knowledge residing in an organization’s employees. The IT that makes KM available throughout an organization is referred to as KMS KMS refers to an IT based system for managing knowledge in organizations for supporting creation, capture, storage and dissemination of information © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 3

Knowledge: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 4 Knowledge Data = collection of facts, measurements, statistics Information = organized or processed data that Knowledge = contextual, relevant, actionable information Dynamic Evolves over time with experience Can be exercised to solve problems Diag at page 482

Knowledge: 

Knowledge © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 5

Knowledge: 

Knowledge Unlike other assets, knowledge has the following characteristics: Extraordinary leverage & increasing returns- Knowledge is not subject to diminishing returns. When it is not used, it is not consumed. Fragmentation, leaking and the need to refresh- As knowledge grows, it branches and fragments Uncertain value- intangible asset, difficult to estimate the impact of an investment in knowledge Uncertain value of sharing © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 6

Knowledge: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 7 Knowledge Explicit knowledge Objective, rational, technical Policies, goals, strategies, papers, reports Codified (easily documented) Leaky knowledge Easily transferred / taught / learned Eg description of how to process a particular job

Knowledge: 

Knowledge Tacit knowledge Subjective, cognitive, experiential learning Store of experiences, insights, expertise, know-how, skill sets, understanding & learning Highly personal Difficult to formalize Interpersonal interaction Embedded knowledge Unstructured Difficult to codify Intangible Eg explanation of how to ride a bicycle © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 8

Knowledge Management: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 9 Knowledge Management Systematic and active management of ideas, information, and knowledge residing within organization’s employees KMS refers to the use of modern IT (internet, intranet, extranet, DWs etc) to systemize, enhance & facilitate intra and inter-firm KM Knowledge management systems Use of technologies to manage knowledge Used to cope up with turnover, change, downsizing Provide consistent levels of service

Organizational Learning & Transformation: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 10 Organizational Learning & Transformation Learning organization Ability to learn from past To improve, organization must learn Issues while building learning orgns Meaning, management, measurement Meaning- determining a vision of what the learning orgn is to be Management- determining how the firm is to work Measurement- Assessing the rate & level of learning Performs 5 main activities Systematical Problem-solving, experimenting creativity, learning from past experience, learning from the best practices of others, transferring knowledge quickly & efficiently throughout the organization Must have organizational memory, a means to save and share its organizational knowledge

Organizational Learning & Transformation: 

Organizational Learning & Transformation Organizational learning Development of new knowledge & insights that have the potential to influence OB Learning skills include: Openness to new perspectives Awareness of personal biases Exposure to unfiltered data A sense of humility Organizational culture Pattern of shared basic assumptions © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 11

Knowledge Management Initiatives: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 12 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

Knowledge Management Initiatives: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 13 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

Approaches to Knowledge Management: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 14 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

Approaches to Knowledge Management: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 15 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

PowerPoint Presentation: 

Also read KM approaches from book Table 11.1 at page 491 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 16

Knowledge Management System Cycle: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 17 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

Components of Knowledge Management Systems: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 18 Components of Knowledge Management Systems Technologies Communication Access knowledge Communicates with others Collaboration Perform groupwork Synchronous or asynchronous Same place/different place Storage and retrieval Capture, storing, retrieval, and management of both explicit and tacit knowledge through collaborative systems

Components of Knowledge Management Systems: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 19 Components of Knowledge Management Systems Supporting technologies Artificial intelligence 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

PowerPoint Presentation: 

KM enhance the usability of other business info systems. Integration with Business Intelligence. Integration with Artificial intelligence. Integration with Database and Information System. Integration with Customer Relationship Management System. Integration with Supply chain Management. Integration with Corporate Intranets and Extranets. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 20

Knowledge Management System Integration: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 21 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

Knowledge Management System Integration: 

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 22 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

Factors leading to success of knowledge management: 

Factors leading to success of knowledge management Economic performance Technical and organizational infrastructure Standard, flexible knowledge structure Knowledge-friendly culture Clear purpose and language Change in motivational practices Multiple channels for knowledge transfer Worthwhile level of process orientation Nontrivial motivational encouragement Senior management support © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 23

Factors leading to failure of knowledge management: 

Factors leading to failure of knowledge management Unclear definition of knowledge Overemphasis on knowledge stock, not flow Belief that knowledge exists outside people’s heads Not recognizing the importance of managing knowledge Failure to manage tacit knowledge © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 24

Factors leading to failure of knowledge management: 

Factors leading to failure of knowledge management Failure to disentangle knowledge from its uses Downplaying reason and thinking Focusing on the past and present, not the future Failure to recognize the importance of experimentation Substituting technology contact for human interface Overemphasis on measuring knowledge, not its outcomes © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 25

PowerPoint Presentation: 

THANK YOU © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 9- 26