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Knowledge Management, Habitual Domains, and Innovation Dynamics

  • Conference paper
Data Mining and Knowledge Management (CASDMKM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3327))

Abstract

Knowledge Management (KM) with information technology (IT) has made tremendous progresses in recent years. It has helped many people in making decision and transactions. Nevertheless, without continuous expanding and upgrading our habitual domains (HD) and competence set (CS), KM may lead us to decision traps and making wrong decisions. This article introduces the concepts of habitual domains and competence set analysis in such a way that we could see where KM can commit decision traps and how to avoid them. Innovation dynamics, as an overall picture of continued enterprise innovation, is also introduced so that we could know the areas and directions in which KM can make maximum contributions and create value. KM empowered by HD can make KM even more powerful.

This research was supported by the National Science Council of the Republic of China. NSC92-2416-H009-009.

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Author information

Authors and Affiliations

  1. Institute of Information Management, National Chiao Tung University, 1001, Ta Hsueh Road, HsinChu City, 300, Taiwan

    P. L. Yu & T. C. Lai

Authors
  1. P. L. Yu
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  2. T. C. Lai
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Editor information

Editors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China; College of Information Science and Technology, University of Nebraska at Omaha, 68182, Omaha, NE, USA

    Yong Shi

  2. Institute of Policy & Management, Chinese Academy of Sciences, P.O. Box, 100080, Beijing, P.R. China

    Weixuan Xu

  3. College of Information Science and Technology, University of Nebraska at Omaha, 68182, NE, USA

    Zhengxin Chen

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© 2004 Springer-Verlag Berlin Heidelberg

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Yu, P.L., Lai, T.C. (2004). Knowledge Management, Habitual Domains, and Innovation Dynamics. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_2

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  • DOI: https://doi.org/10.1007/978-3-540-30537-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23987-1

  • Online ISBN: 978-3-540-30537-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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