Selection model for the decision making method based on the knowledge sharing point
| Autor: | Li-Hsin Wang, 王麗幸 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 The knowledge management has been proper used in many industries, which also had positive value from many enterprises’. The enterprises or organization apply the knowledge management in order to raise the capability to face the industries’ problems. The enterprises have inputted a lots effort in knowledge management to improve their competitiveness. The Multiple Criteria Decision Making Method(MCDM) has been used in many filed of decision problems. The appropriate choosing the decision making method, which can give the decision makers the right way to solve the problem directly. Therefore, this research paper tries to apply the perspective of knowledge sharing to construct the application model for the decision making method in order to share the many decision methods. According to MCDM need to consider multidimensional key factors in order to get the ways solving the problem. Also, the different MCDM has its own applying conditions which would make hard time choosing the appropriate method for the problem. The research including guidelines for the application in MCDM, including the assumptions, characteristics, scope, and the advantages and disadvantages of compile, In order to build decision-making methods of sharing model are providing application-sharing mechanisms of the ways in which applications to share the decision-making model more efficient. This application model for decision making method was applied to two completely different areas, expected results by the two major validation to prove that application model in this study is indeed to solve the queries raised by institutions to achieve the purpose of solving the problem completely. By the above two examples of the validation results, that can confirm the practicability for this application model of knowledge sharing. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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