The Fit Profile of Task/Technology/Individual of Knowledge Management Systems

• Main Authors: Chien-Chih Chen, 陳建志
• Other Authors: T.S-C Chou
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/27163246549954308871

博士 === 國立臺灣大學 === 資訊管理學研究所 === 95 === Knowledge Management Systems (KMS) are the emerging applications of the information technologies to support knowledge management activities. An effective adoption of KMS to improve knowledge workers’ tasks performance has become an important weapon for the business competitiveness enhancement. According to Task/Technology/Individual Fit Theory (TTIF), the fit of task characteristics, technology characteristics and individual characteristics will influence task performance. The greater the degree of adherence to an ideal fit profile, the better the performance. This paper develops a model of task/technology/individual fit in KMS environments based on attributes of task’s knowledge demand (tacit/explicit knowledge), technology function supply (P2D: People to Database / P2P: People to People), and individual capabilities (CSE: Computer Self-Efficacy). An empirical study was then conducted to validate the proposed fit model. The results indicated that: 1. For tasks which rely on explicit knowledge, P2D KMS shows better supports than P2P KMS. 2. For tasks which rely on tacit knowledge, P2P KMS shows better supports than P2D KMS. 3. For tasks which rely on both tacit and explicit knowledge, there is no difference between P2P KMS and P2D KMS. 4. When CSE is high, the fit relationship between tacit/explicit and p2d/p2p is supported. However, when CSE is low, the task/technology fit is not supported. 5. The combinations of knowledge tasks, KMS, and individual’s CSE result in different performance impacts. These results show a promising development direction for KMS TTIF theory. Practical implications of KMS implementation are also discussed.