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Showing asymmetries in knowledge creation and learning through proactive vision
Authors:Jussi Kantola  Hannu Vanharanta  Petri Paajanen  Antti Piirto
Affiliation:1. Department of Knowledge Service Engineering , KAIST, Korea Advanced Institute of Science and Technology , 291 Daehak-ro, Yuseong-gu, Daejeon , Korea jussi@kaist.edu;3. Department of Industrial Management and Engineering , Tampere University of Technology , Pori , Finland;4. Teollisuuden Voima Ltd. , Olkiluoto , Finland
Abstract:Communicating with personnel is difficult if the concept in hand is complex, hard to perceive, has characteristics that are fuzzy in nature and that need a long-term perspective to show the results of them as a real benefit and advantage. Knowledge creation and learning concepts both belong to these kinds of management objects. Both have characteristics that are difficult to manage and lead, and that are difficult to articulate in detail to the organisation. Knowledge creation has long been one of the key concepts in modern management science and research. Learning, however, has not achieved that high a status. Many contemporary scientists like to bring in new constructs to better understand the mechanisms behind knowledge creation and learning; however, measuring these kinds of abstract concepts needs support from theory as well as methodology, so that communication to personnel can be objective and, from a management point view, effective. In this research, we have used Internet-based computer applications to measure current knowledge creation and learning levels, and to gain insight into how members of organisations are willing to show their proactive vision, as well as priorities in knowledge creation and learning concepts, inside their organisation. Practical asymmetries can be shown with test subjects, which are important to understand from a leadership and management point of view. The dataset used for this article contains academic and private organisations.
Keywords:knowledge creation  organisational learning  ontology  fuzzy logic  neural network  SOM
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