Harnessing Dynamic Knowledge Principles in the Technology-Driven World

Nissen, Mark
IGI Global, 30.11.2013 - 291 Seiten
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In a technology-driven world, it is essential that enterprises develop reliable and rapid flows of knowledge to distribute evenly across organizations, time and place, and individuals in order to sustain a competitive advantage. However, most leaders and managers are unacquainted with effective knowledge flow practices.

Harnessing Dynamic Knowledge Principles in the Technology-Driven World provides actionable principles of Knowledge Flow Theory to identify and solve problems for implementing these principles into practice. With emerging developments and widespread applicability, this book is a practical guide for scholars, business managers, and enterprise leaders and managers interested in understanding the dynamics of knowledge flows for competitive advantage in a technology-driven world.


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Practical Application
Emerging Phenomena
Compilation of References
About the Contributors

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Autoren-Profil (2013)

Mark Nissen is Professor of Information Science and Management, and Edge Center Director, at the Naval Postgraduate School. His research focuses on dynamic knowing and organizing. He views work, technology, organization and people as an integrated design problem, and he’s concentrated for some time on the disparate dynamics of tacit and explicit knowledge flows, looking in particular at their measurement and at (re)designing organizations that balance stability with maneuverability. Mark’s 150+ publications span information systems, project management, organization studies, knowledge management and related fields. In 2000 he received the Menneken Faculty Award for Excellence in Scientific Research, the top research award available to faculty at the Naval Postgraduate School. In 2001 he received a prestigious Young Investigator Grant Award from the Office of Naval Research for work on Knowledge Flow Theory. In 2002 – 2003 he was Visiting Professor at Stanford, integrating Knowledge Flow Theory into agent-based tools for computational modeling and experimentation. [Editor]

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