博士 / 國立中央大學 / 營建管理研究所 / 102 / 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.
| Identifer | oai:union.ndltd.org:TW/102NCU05718005 |
| Date | January 2014 |
| Creators | Jia-Zheng Lin, 林佳正 |
| Contributors | Jieh-Haur Chen, 陳介豪 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 138 |
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