標題: 從能力集觀點探 討企業能力檢索模型之建構
Constructing Models for Enterprise Competence Retrieval from Competence Set Perspectives
作者: 辛紀宗
游伯龍
Po-Lung Yu
資訊管理研究所
關鍵字: 習慣領域;能力集分析;資訊檢索;Habitual Domain;Competence set analysis;Information retrieval
公開日期: 2001
摘要: 企業擁有許多有形或無形的資產,但必須妥善安排及運用,方能達到永續經營的目標。在眾多資產中,最寶貴的非企業員工莫屬。如果將企業視為人體的話,員工就是組成人體的最基本單位─細胞,負責各項資訊的傳遞與擴展,甚至於組成各個具有不同能力的部門。在這當中值得我們注意的課題之一便是「在發現問題後,如何用最快且最有效率的藉由員工組成特定部門,然後解決問題、達成目標?」由習慣領域(Habitual Domain)的角度來看,就是迅速取出自己或他人(企業)有效的電網(Circuit Pattern),進而解除自己或他人(企業)的痛苦和壓力[14]。 本研究試圖從習慣領域(Habitual Domain)及能力集分析(Competence Set Analysis)的觀點出發,先定義出員工所具有的能力,並將企業內部所有員工的能力予以匯總整理,再輔以知識管理(Knowledge Management)及資訊檢索(Information Retrieval)的理論和技術,建立「企業能力檢索系統(Enterprise competence retrieval system)」。並從「向量空間模型(Vector space model) 」、「最短距離模型(Minimum distance model)」、「集合涵蓋模型(Set covering model)」及「目標設定與折衷辦法(Goal setting and compromise solution)」等不同的角度探討能力檢索模型。一旦企業或個人遭遇問題後,則能依企業能力檢索系統的建議迅速找到所需人選及能力,尋求協助,得以解決。
To successfully run a company we should make good use of it’s assets, visible and invisible. “Employee” is one of the most valuable assets. If we regard the company as a human body, then the employees could be the basic units of the human body – the body cell, which transmit information and perform various functions. One of the main issues of the company is to choose a most efficient way to solve the arriving problems by an individual or by a certain team, composed by some employees with special skills. From habitual domain point of view, this means to retrieve effective circuit pattern inside or outside of the company to release the pain and frustrations generated by the problems. Using concepts of habitual domain and competence set analysis, in this research we first collect the competences of employees as a database. Using the technology of knowledge management and information retrieval, we build a logistics or “Competence Set Retrieval System” which can select the best employee who has the needed competence set for solving the arriving problem. The algorithm we use to choose the best employee include “similarity by vector inner product”, “minimum distance”, “set covering” and “goal setting and compromise solution”.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT900396012
http://hdl.handle.net/11536/68642
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