知識管理系統之任務/科技/個人之適配模式探討

Abstract

TTIF理論（Task/Technology/Individual Fit Theory）強調科技對個人工作任務績效的影響，是來自於任務、科技、個人的適配結果，理想的適配關係可有效提升績效。過去TTIF理論運用在其他系統環境中，但尚未運用在知識管理系統的環境下，目前仍欠缺相關的KMS適配模式，探討在KMS環境下的適配模式有其理論與實務上的必要性。本文根據任務/科技/個人適配理論探討知識管理環境下之是知識型任務、KMS、知識工作者之適配模式，根據相關文獻的基礎與適配理論，首先探討任務與科技的適配關係探，並進而考量個人特性因子，以建構更完整的KMS TTIF模式。 本文以調查研究的方式考驗本文所提出的假說。研究可歸納為以下幾項結果：（一）當任務倚賴外顯知識時，P2D（People to Database）型系統支援任務的績效優於P2P（Person to Person）型系統支援任務的績效；當任務倚賴中性知識時，P2D型系統支援任務的績效與P2P型無差；當任務倚賴內隱知識時，P2P型系統支援任務的績效優於P2D型系統支援任務的績效。（二）P2D型系統支援外顯任務的績效顯著高於內隱知識；P2P型系統支援內隱知識任務的績效顯著高於外顯型知識任務。（三）低電腦自我效能下，任務與科技的適配結果不明顯；高電腦自我效能下，任務與科技的適配可明確成立。（四）任務、科技、個人三方面因素形成的各種組合關係，形成不同的適配影響，例如、P2D型+外顯+高電腦自我效能以及P2P型+內隱+高電腦自我效能的組合較具績效影響。 對學術而言，本研究擴展TTIF理論的適用性於KMS環境中，研究結果不僅驗證內隱/外顯與P2D/P2P的基本配合關係，亦拓展電腦自我效能在任務科技適配關係中的影響，並進而發展更完成的TTIF模式，此KMS TTIF模式具有充實的理論基礎，並可作為未來研究者進一步探討KMS TTF相關研究的基礎。對實務而言，本文的研究貼近企業界的實務需求，對經營管理者、系統開發者、使用者各具不同的意義與價值，可作為企業推行KMS時的參考依據。

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.