臺灣博碩士論文加值系統

以往，我國在勞動人口充分就業之下，一直以2.0%以下的低失業率與高勞動力著稱。但近年來，我國面臨經濟結構、產業結構與企業走向的轉變，在全球化的驅使之下，勞力密集的產業逐漸外移，導致失業率從1995年的2.6%急遽上升至2004年的4.44%，失業人口約為45萬餘人，失業率居高不下造成人力資源閒置並且衍生出許多的社會問題。因此，如何協助失業員工再就業便成為我國當前國政重點之一。
本研究以行政院主計處1997〜2002年台灣地區人力運用調查中之非自願性失業再就業員工為研究對象。首先，從失業再就業員工資料中篩選出其相關特徵之屬性，將失業再就業員工資料中的個人特質、前次就職公司特質及尋職管道等相關特徵，應用資料探勘 (Data Mining) 技術中決策樹 (Decision Tree) 的方法進行歸納，共得出64條失業員工再就業的特性規則，且進一步發現所有分析的特徵中以個人特質之年齡為最重要之屬性，其中年齡又以35歲對非自願性失業再就業員工特性之區隔性最大。反觀，非自願性失業員工個人特質中之性別對再就業的影響式微。隨之應用集群化(Clustering)的方法進行資料分群，將篩選出之非自願性失業再就業員工資料區分為兩個群組，分別為群組1：具備國中程度之已婚男性為主；群組2：具備高職程度之未婚女性為主。針對集群化結果複以關聯分析(Association Analysis)獲取出群組1共2229條關聯規則；群組2共2275條關聯規則，且進一步由整體關聯規則中發現非自願性失業再就業員工目前就職公司特質與前次就職公司特質中以未轉換工作地點、擔任之職務別及行業別佔大多數。
本研究針對非自願性失業再就業員工特性之研究，從中探究非自願性失業再就業員工具備之特質，瞭解何種特性的失業員工較易在職場上再就業，及其非自願性失業再就業員工之前次就職公司特質與目前就職公司特質之關聯性，希望可作為政府制訂相關失業決策與補救措施之參考依據。

In the past, Taiwan was known as low unemployment rate (below 2.0%) and high working force because of the ample employment population. However, in recent years, driven by the globalization, Taiwan faces the changes of economic structure, industrial structure and enterprise strategies which cause the migration of labor-intensive industry and therefore result in an increase in the unemployment rate from 2.6 % in 1995 to 4.44 in 2004. The unemployed population is about 500000. High unemployment rate leads to unused workforce and many social problems. Therefore, assisting the unemployed to become employed is one of the major government policies.
This research uses the manpower resources survey of DGBAS (Directorate-General of Budget, Accounting and Statistic Executive Yuan) as the raw data, selects the information of those reemployment people, and targets on those who were unwilling to be thrown out of employment. Focusing on the characters of those reemployment people, the features of their previous working place, and job seeking channels used, the research applies the decision tree algorithm to analyze all the related factors in order to explore the traits of those reemployment people and also to grasp specific traits which enable unemployed people to find another job easily. According to the result of the research, 64 traits rules of reemployment people are induced. It is further discovered that among all the attributes, age is the most prominent one. As far as age is concerned, age 35 is the critical point to distinguish those reemployment people. However, it is found that gender appears to be the least influential attribute. Then, the research applies clustering for data grouping, and divides the involuntary into two groups. Group 1 focuses on married males with junior high school degree. Group 2 consists of unmarried female with vocational graduates. The results deriving from clustering are reanalyzed by association analysis, which further concludes 2229 association rules in Group 1 and 2275 ones in Group 2. Moreover, it is found that most of the involuntary reemployed whose present work place, positions, and types of occupations are the same as their former one.
This research emphasizes on the characters of the involuntarily reemployed, the characteristics that they have, and the association between the involuntarily reemployed former and latter companies, figuring out what kind of unemployed can be reemployed easily. To conclude, it’s expected that the government can make more efficient policies and compensatory measures related to unemployment in accordance with the traits of reemployment people found in this research.

摘要
致謝
目 次
表目錄
圖目錄
第一章 緒論
1.1 研究動機
1.2 研究目的
1.3 研究步驟
1.4 研究範圍與限制
1.5 論文結構
第二章 文獻探討
2.1 台灣地區就業與失業概況
2.2 失業的定義
2.3 影響失業員工再就業之因素
2.3.1 個人特質與再就業關係
2.3.2 前次就職公司特質與再就業關係
2.3.3 尋職管道與再就業之關係
2.3.4 前次就職公司特質與再就業公司特質之關聯性
2.4資料探勘文獻探討
第三章 研究方法
3.1 資料來源與研究對象
3.2 資料探勘技術之應用
3.2.1 決策樹方法
3.2.2 集群化方法
3.2.3 關聯分析
3.3 研究流程
3.3 研究架構
3.3.1非自願性失業再就業特性之研究架構
3.3.2集群化與關聯分析之研究架構
3.5 資料分析方法
第四章 研究結果
4.1 總體樣本敘述分析
4.2 研究樣本敘述分析
4.3 決策樹探勘結果
4.4 集群化探勘結果
4.5 關聯分析探勘結果
第五章 結論與建議
5.1 結論
5.2 建議
5.3 後續研究建議
參考文獻
附錄
附錄A 集群參數設定與各群組特徵區隔之結果

1.王麗蓉，「婦女就業之檢視與政策建議-以再就業為例」，就業與訓練，第十九卷，第一期，第27-32頁 (2001)。
2.行政院主計處，http://www.dgbas.gov.tw/census~n/four/manpower.htm (2005)。
3.行政院勞委會， http://dbs1.cla.gov.tw/stat/h05/h050103.htm (2005)。
4.辛炳隆，「如何協助被資遣勞工再在就業」，就業與訓練，第十九卷，第一期，第17-26頁 (2001)。
5.李誠，「非自願性失業與台灣的永續發展」，國立中央大學社會文化學報，第八期，第95-118頁 (1999)。
6.林祖嘉，「工作搜尋模型與失業期間-台灣地區大專畢業生之經驗」，經濟論文，第十九卷，第二期，第183-215頁 (1991)。
7.苗坤齡，「台灣地區勞動市場失業與缺工問題之研究」，臺灣銀行季刊，第五十一卷，第一期，第179-210頁 (1999)。
8.郎若帆，「對外投資會是失業潮的元兇？」，台灣經濟研究月刊，第二十四卷，第五期，第29-33頁 (2001)。
9.苗坤齡，「台灣地區勞動市場失業與缺工問題之研究」，臺灣銀行季刊，第五十一卷，第一期，第179-210頁 (1999)。
10.耿靜宜，「非自願性與自願性失業者尋職行為之比較-以台北地區為例」，碩士論文，國立中央大學人力資源管理研究所，桃園縣 (1998)。
11.張清溪、許嘉棟、劉鶯釧、吳聰敏等，經濟學：理論與實務（下），翰蘆圖書，台北市 (1987)。
12.舒昌榮，「當前我國中高齡勞工失業問題之省思-人力資源動態結構的觀點」，研習論壇，第五期，第41-45頁 (2001)。
13.詹火生、彭台臨、陳聰憲、郭振昌，職業訓練與就業服務，國立空中大學，台北市 (1999)。
14.劉天賜、苗坤齡，「台灣地區產業結構與勞動市場變更之研究」，臺灣銀行季刊，第五十卷，第一期，第147-17頁 (1999)。
15.劉鶯釧、黃聰智，「各地區的失業差異/兼論女性勞動力」，誰偷了我們的工作：一九九六年來台灣的失業問題，李誠主編，天下遠見，台北市，第231-262頁 (2003)。
16.薛承泰、簡文吟，「再就業婦女的職業流動初探」，人口學刊，第十八期，第67-98頁 (1997)。
17.蘇光培，「關廠歇業後員工再就業之探討-以高雄加工出口區M 電子公司為例」，人力資源管理學報，第二卷，第一期，第75-97頁 (2000)。
18.Adnett, N. European Labor Markets: Analysis and Policy, Longman Press, London (1996).
19.Agrawal, R. and Srikant, R. “Fast Algorithms for Mining Association Rules in Large Database,” Proceedings of the 20th International Conference on Very Large Data Bases, 487-499 (1994).
20.Allen, R.E. and Keaveny, T.J. “The Relative Effectiveness of Alternative Job Sources,” Journal of Vocation Behavior, 16, 18-32(1980).
21.Burtless, G. and Quinn, J. “Is Working Longer the Answer for an Aging Workforce?” Boston College Working Paper in Economics, Boston College Department of Economics (2002).
22.Curti, M. “Does Economic Stagnation Affect Unemployed Workers, Even When Re-employed?” International Journal of Manpower, 19(6), 410-423 (1998).
23.DeFrank, R.S. and Ivancevich, J.M. “Job Loss: An Individual Level Model and Review,” Journal of Vocational Behavior, 28, 1-20 (1986).
24.Ermisch, J.F. and Wright, R.E. “Wage Offers and Full-time and Part-time Employment by British Women,” The Journal of Human Resources, 28(1), 111-133 (1993).
25.Feelders, A., Daniels, H. and Holsheimer, M. “Methodological and Practical Aspects of Data Mining,” Information and Management, 37(5), 271-281 (2000).
26.Finch, J.H. “The Re-employment Experiences of Former Aerospace Employees within a Local Economy,” Regional Studies, 32(5), 421-433 (1998).
27.Frawley, W.J., Shapiro, P.G. and Matheus, C.J. “Knowledge Discovery in Databases-An Overview,” AI Magazine, 13, 57-70 (1992).
28.Granovetter, M.S. Getting a job, 2nd Ed, Chicago University Press, Chicago (1995).
29.Han, J. and Kamber, M. Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco (2000).
30.Kaufman, L. and Rousseeuw, P.J. Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons Press (1990).
31.Latham, V.M. and Leddy, P.M. “Source of Recruitment and Employee Attitudes: An analysis of Job Involvement, Organizational Commitment, and Job Satisfaction,” Journal of Business & Psychology, 1, 230-235 (1987).
32.Lilien, D.M. “Sectoral Shift and Cyclical Unemployment,” Journal of Political Economy, 90(4), 777-793 (1982).
33.Renes, G. and Ridder, G. “Are Women Overqualified?” Labour Economics, 2(1), 3-18 (1995).
34.Mehta, M., Agrawal R. and Rissanen, J. “SLIQ: A Fast Scalable Classifier for Data Mining,” Proceedings of the 5th International Conference on Extending Database Technology, 18-32 (1996).
35.McCall, B.P. “The Determinants of Full-time versus Part-time Reemployment Following Job Displacement,” Journal of Labor Economics, 15(4), 714-734 (1997).
36.Petrongolo, B. “Reemployment Probabilities and Returns to Matching,” Journal of Labor Economics, 19(3), 716-741 (2001).
37.Polsky, D. “Changing Consequences of Job Separation in the United States,” Industrial and Labor Relations Review, 52(July), 565-580 (1999).
38.Shaw, M.J., Subramaniam, C. Tan, G.W. and Welge M.E. “Knowledge Management and Data Mining for Marketing,” Decision Support Systems, 31(1), 127-137 (2001).
39.Silvestri, G.T. “Occupational Employment to 2005,” Monthly Labor Review, 118, 60-87 (1995).
40.Richard, J.M. and Michael, W.G. Data Mining: A Tutorial-Based, Addison Wesley Press (2003).
41.Wamberg, C.R., Banas J.T. and Kanfer, R. “Predictors and Outcomes of Networking Intensity among Unemployed Job Seekers,” Journal of Applied Psychology, 85(4), 491-503 (2000).
42.Wielgosz, J.B. and Carpente, S. “The Effective of Alternative Methods of Searching for Jobs and Finding Them: An Exploration Analysis of the Data Bearing upon the Ways of Coping with Joblessness,” The American Journal of Economics and Sociology, 46(2), 151-164 (1987).
43.Williamson, J. and McNamara T. “Why Some Workers Remain in the Labor Force Beyond the Typical Age of Retirement,” Boston College Working Paper in Economics, Boston College Department of Economics (2001).
44.Zait, M. and Messatfa, M. “Comparative Study of Clustering Methods,” Future Generation Computer Systems, 13, 149-159 (1997).