Successes and Failures of Knowledge Management

 
 
Morgan Kaufmann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 17. Juni 2016
  • |
  • 240 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-805337-9 (ISBN)
 

Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well-and not so well-so others can learn from these cases during their knowledge management journey.

Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility.

Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization.


  • Provides keen insights for knowledge management practitioners and educators
  • Conveys KM lessons learned through both successes and failures
  • Includes straightforward, jargon-free case studies and research developed by the leading KM researchers and practitioners across industries


Dr. Jay Liebowitz is the Distinguished Chair of Applied Business and Finance at Harrisburg University of Science and Technology. He previously was the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). He served as a Professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide, and was ranked #2 in KM Strategy worldwide according to the January 2010 Journal of Knowledge Management.

Prior to joining Hopkins, Dr. Liebowitz was the first Knowledge Management Officer at NASA Goddard Space Flight Center. Before NASA, Dr. Liebowitz was the Robert W. Deutsch Distinguished Professor of Information Systems at the University of Maryland-Baltimore County, Professor of Management Science at George Washington University, and Chair of Artificial Intelligence at the U.S. Army War College.
Dr. Liebowitz is the Founding Editor-in-Chief of Expert Systems With Applications: An International Journal (published by Elsevier), which is ranked #1 worldwide for AI journals according to the h5 index of Google Scholar journal rankings (as of November 26, 2014). He is a Fulbright Scholar, IEEE-USA Federal Communications Commission Executive Fellow, and Computer Educator of the Year (International Association for Computer Information Systems). He has published over 40 books and a myriad of journal articles on knowledge management, analytics, intelligent systems, and IT management. As of January 2014, Dr. Liebowitz served as the Editor-in-Chief of Procedia-CS (Elsevier). He is also the Series Book Editor of the new Data Analytics book series (Taylor & Francis). In October 2011, the International Association for Computer Information Systems named the 'Jay Liebowitz Outstanding Student Research Award" for the best student research paper at the IACIS Annual Conference. He has lectured and consulted worldwide.
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 9,78 MB
978-0-12-805337-9 (9780128053379)
0128053372 (0128053372)
weitere Ausgaben werden ermittelt
  • Back cover
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Contributors
  • Preface
  • Chapter 1 - Parameters of knowledge management success
  • Introduction
  • Foundation
  • Knowledge
  • Conduct of knowledge management
  • Superiority and the PAIR model
  • Parameters
  • Conclusions
  • References
  • Chapter 2 - Why are companies still struggling to implement knowledge management? Answers from 34 experts in the field
  • Introduction
  • The state of knowledge management
  • Original source material knowledge base
  • Research method
  • Key findings
  • Data analysis
  • Text mining analysis
  • Data interpretation
  • Culture
  • Measurement/benefits
  • Strategy
  • Organizational structure
  • Governance and leadership
  • IT-related issues
  • Lack of KM understanding/standards
  • Comparative analysis with previous studies
  • Conclusions
  • Acknowledgments
  • References
  • Chapter 3 - REAL knowledge and the James Webb Space Telescope: success and failure coexisting in NASAa
  • Introduction
  • NASA knowledge services governance and strategic imperatives
  • Strategic imperatives in the modern project knowledge environment
  • REAL (rapid engagement through accelerated learning) knowledge model
  • Success and failure coexist: the James Webb Space Telescope
  • Pioneering technology and instrumentation
  • Expectations versus performance
  • Technology readiness
  • Waiting for assessments
  • JWST in terms of REAL knowledge
  • Conclusions
  • References
  • Chapter 4 - Processes: Still the poor relation in the knowledge management family?
  • Introduction
  • People, processes, and technology in knowledge management
  • Analyzing examples of KM failure and success
  • Connections between people and business processes
  • Connections between people and knowledge processes
  • Connections between business processes and knowledge processes
  • You can't win them all!
  • Conclusions: reflections on the future
  • References
  • Chapter 5 - KM successes and failures: some personal reflections on major challenges
  • Introduction
  • Evolution of knowledge management
  • Problems with the nature of knowledge
  • Familiar challenges in knowledge elicitation
  • Studies in the nature of expertise and judgment
  • Challenges in knowledge representation
  • What we know and do not know
  • Problem of big data and machine learning
  • Knowledge, knowledge management, and culture
  • Importance of trust and the sharing of knowledge
  • Digital asset management
  • Importance of forgetting
  • Support and changing of routines
  • Recapturing knowledge
  • Value of knowledge
  • Knowledge processes
  • Conclusions
  • References
  • Chapter 6 - Lessons learned from nearly 200 cases of KM journeys by Hong Kong and Asian Enterprises
  • Introduction to the nature of the knowledge management initiative and its specific objectives
  • The infrastructure-people, systems, hardware, software, etc.-required to launch the initiative
  • The challenges that were encountered, how they developed, and how they were overcome
  • How the initiative was received by the users or participants
  • The efficiency, effectiveness, or competitive advantage outcomes that were achieved and how they were measured and evaluated
  • Gap between KM in the books and in practice
  • Knowledge audit and knowledge strategy: which one comes first?
  • KM journeys are rarely started from scratch
  • Natural KM "entry points" in organizations
  • KM strategies vary among multinational corporations, local companies, and small to medium-sized businesses
  • Factors for sustaining the use of knowledge management systems that differ from factors that affect adoption
  • IC is much harder than KM to sell
  • Chapter 7 - Knowledge loss and retention: the paradoxical role of IT
  • Introduction
  • Review of the literature
  • Knowledge and the knowledge-based view
  • Knowledge retention and loss
  • Research model
  • Research methodology
  • Findings
  • GovSystem overview
  • Bright side of GovSystem: effective knowledge acquisition and retrieval
  • Dark side of GovSystem: knowledge loss during acquisition and retrieval
  • Findings summary
  • Discussion and implications
  • Discussion
  • Implications
  • Conclusions
  • References
  • Chapter 8 - Knowledge and knowledge-related assets: design for optimal application and impact
  • Introduction
  • Background: knowledge management
  • Rethinking the DIKW hierarchy
  • Competitive intelligence systems
  • The knowledge-related hierarchy and the disciplines
  • Big data and business analytics
  • Discussion: what is KM missing?
  • Conclusions
  • References
  • Chapter 9 - Knowledge management success and failure: the tale of two cases
  • Introduction
  • Case study 1: language, culture, and leaders: a case study of the challenges of installing a knowledge management system in...
  • Business setting
  • Business problem
  • Goals of the KM system
  • Proposed solution
  • Implementing the KM system
  • Challenges
  • Results and conclusion
  • Case study 2: building a better knowledge management and customer service system
  • Introduction
  • Business setting
  • Business problem
  • Goals
  • System implementation
  • Help desk ticket system
  • KM system
  • Incentive program
  • Results and conclusion
  • References
  • Chapter 10 - Social knowledge: organizational currencies in the new knowledge economy
  • The odometer reading: evolution of social knowledge management
  • Conversations build communities
  • More than an idea, it's a practice
  • An evolutionary road
  • Managing social knowledge: people, process, technology, and the human experience
  • Showing value with SKM (putting miles on the odometer)
  • Merging into traffic: trusting the rules of the road in the new social economy
  • A generational shift
  • The emerging social (knowledge) economy
  • What has worked? Where to start?
  • Acknowledgment
  • Chapter 11 - Knowledge management and analytical modeling for transformational leadership profiles in a multinational company
  • Introduction
  • Theoretical framework
  • Transformational leadership model
  • Sand cone model
  • Analytic hierarchy process (AHP)
  • Research environment and methods
  • Transformational leadership index (TLI)
  • Results
  • Transformational leadership profiles
  • Total leadership index-reexamination
  • Discussion
  • Conclusions
  • References
  • Appendix 1: sand cone traffic light values In the black and white printed version, the green color is signified with a chec...
  • Appendix 2
  • Appendix 3
  • Appendix 4
  • Appendix 5
  • Appendix 6: specific index traffic light values In the black and white printed version, the green color is signified with a...
  • Chapter 12 - Success and failure in improvement of knowledge delivery to customers using chatbot-result of a case study in a Pol...
  • Introduction
  • The needs and difficulties in management of knowledge delivery to customers in the selected SME
  • Improvements of knowledge bases and delivery processes using chatbots
  • The improvements of knowledge delivery to customers in Janas company
  • Results of improvements of knowledge delivery to customers using SAGA
  • Discussion
  • Conclusions and directions for future research
  • References
  • Chapter 13 - Don't neglect the foundation: how organizations can build their knowledge architecture and processes for long-term ...
  • Diverse, fast-changing information sources
  • Knowledge that serves the customer
  • Incorrect architecture reduces organizational agility
  • Architectural problems across the information ecosystem
  • Foundational architecture as a project rather than a program
  • Parochial view of the application
  • Balancing centralized versus distributed control
  • Passing on data and content quality issues
  • Cutting corners or checking the boxes
  • Incorrect development and application of use cases and scenarios
  • Lack of understanding of user types and the needs of users
  • Lack of appreciation of the value of unstructured information
  • Lack of meaningful metrics or interpretation to tie business value to information
  • Lack of maturity in enterprise architecture, user experience, and governance
  • Ownership and sponsorship
  • Short-term objectives with long-term vision
  • Goals of procurement versus needs of the program
  • Focus on the outcome value, not the cost of the engagement
  • Socialization
  • Summary
  • Chapter 14 - Semantic technologies for enhancing knowledge management systems
  • Introduction
  • Background
  • Knowledge management
  • Intelligent agents
  • XML and knowledge management
  • Semantic technologies
  • RDF-resource description framework
  • RDF schema
  • Ontology
  • Web services
  • Semantic technologies-based knowledge management environment
  • Internal components
  • Intelligent user interface
  • Database and RDF schema
  • Knowledge base
  • Ontologies
  • KM system manager
  • Knowledge acquisition module
  • External components
  • Semantic web service providers
  • Semantic web service registry
  • Knowledge resources
  • Summary
  • Acknowledgment
  • References
  • Subject Index
  • Back cover

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