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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/66061

    Title: 工程技術顧問業員工海外派遣意願之規則探勘;Prediction rules of the employees’ expatriation willingness for engineering consulting companies
    Authors: 林佳正;Lin,Jia-Zheng
    Contributors: 營建管理研究所
    Keywords: 外派意願;約略集理論;SFNN演算法;C5.0演算法;分類規則;expatriation willingness;rough set;SFNN;C5.0 algorithm;prediction rules
    Date: 2014-07-08
    Issue Date: 2014-10-15 17:28:01 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來國內大型工程逐漸飽和,走向國際儼然成為工程技術顧問業重要的發展方向。隨海外案量之增加,海外派遣人力需求亦隨之大增,然各公司卻面臨多數員工外派意願低落之況狀。過往研究指出,合適之外派人選將是海外專案成功關鍵,其中員工外派意願影響甚鉅,故本研究欲建立員工海外派遣意願之預測規則,協助企業了解員工考量並快速篩選合適外派人選。本文透過文獻回顧與專家訪談初步確立22項影響因素,並以具代表性之大型工程技術顧問公司之員工作為問卷發放對象,共發放1000份問卷並回收486份問卷,其中413份為有效問卷。
    ;The objective of this research is to identify and classify the factors affecting the expatriation willingness (EW) of engineering consulting company employees. Thirteen EW impact factors are summarized from the literature review and divided into four categories. From the collected factors and expert interviews, 22 impact factors are obtained and divided into eight categories, with the exception of demographic variables. A survey aiming at the top five engineering consulting companies is carried out. Out of a total of 1,000 questionnaires sent out, 41.3% valid responses are returned. The statistical analysis shows that the survey is reliable and one of the 22 factors is removed. The rough set theory (RST) is utilized to classify these factors into three classes based on the impact level. The conclusions provide practitioners with six core impact factors, nine medium impact factors and six Insignificant Impact factors on employees’ EW. Among them 15 factors are set as the inputs to establish prediction rules.

    This paper describes the use of the recently developed SOM-based Optimization (SOMO) algorithm to determine the optimal parameter settings for a neurofuzzy classifier for dealing with a practical expatriation willingness (EW) problem. The results show that the SOMO neurofuzzy classifier yields 6 determination rules, one for positive EW and the rest for negative EW. Loneliness and marital status are the most significant attributes for deciding on personal EW for international projects. They both have high coverage and accuracy rates greater than 80%. Compared with C5.0 algorithm, we conclude that the proposed model apparently outpaces the C5.0 algorithm in terms of accuracy and coverage. SOMO is effective and efficient for optimizing parameter selection.
    Appears in Collections:[營建管理研究所 ] 博碩士論文

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