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林雪蘭

林雪蘭 Hsueh-Lan Lin

碩士論文 (2009)

知識商務之知識服務團隊建立方法研究
An Approach to Knowledge Service Team Formation for Knowledge Commerce

關鍵字 Keywords

基因演算法, 知識工作者, 知識商務, 團隊建立
Knowledge Commerce, Knowledge Worker, Team Formation, Genetic Algorithm

摘要

知識商務為企業帶來知識資產管理的創新思維及新的獲利模式,透過將知識重新組裝或提供客制化的知識服務皆可為組織創造獲利。然而在知識服務過程中,當面對複雜且多元的問題時,往往無法只靠單一知識工作者來解決問題,此時若能結合不同知識背景及專業之專家組成「知識服務團隊」將能提供需求者更佳的知識服務。 為滿足前述需求,本研究發展一「知識商務之知識服務團隊建立方法」,主要研究包含兩部分:(1)「知識服務團隊成員評選方法」設計:依據知識商務與虛擬團隊之特性,設計一包含五大構面之成員評選指標模式,同時針對每個指標之屬性特徵設計適當之量化方法,而後結合模糊權重總計運算(Fuzzy Aggregation Operator)與綜合指數法(Composite Index)以綜合評價知識工作者;(2)「知識服務團隊組合方法」設計:依據團隊組合之指標分析與目標界定,建立一團隊組合數學模式,並採用基因演算法以求取最適解。最後本研究實作一系統以驗證所提之方法。 本研究提出之方法能有效建立一兼具成員個人能力與團隊整體契合度之知識服務團隊,使團隊發揮更大之績效,提供知識需求者最佳之知識服務。

Abstract

Knowledge commerce (k-commerce) brings innovative thinking of knowledge asset management and new profit models to enterprises. Through re-assembling or customizing, knowledge can create profit for individuals and enterprises. However, in knowledge service processes, complex knowledge-based services are difficult to be solved by a single knowledge worker. Therefore, forming a multiple functional virtual knowledge service team is a proper solution for offering customized knowledge service to knowledge requesters. This study proposes a knowledge service team formation approach that consists of a team member selection method and a team combination method. The former includes a fuzzy aggregation operation and a composite index method, which are developed based on the characteristics of knowledge commerce and virtual team, related member selection indicators and correspondent indicator quantified methods. The latter includes an optimization mathematical model for virtual team combination and a genetic algorithm for seeking feasible solutions. Finally, the prototype of a knowledge service team formation system is developed for verifying the feasibility of these methods proposed by this study. The proposed approach considering individual member’s capabilities and the cooperative harmony between team members can form virtual knowledge service teams that can offer optimal knowledge service to requesters.


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