運用數據探勘技術來發現影響台灣女性就業的潛在因素;The Use of Data Mining Techniques to Discover the Influential Factors of Women’s Employment in Taiwan

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Title:	運用數據探勘技術來發現影響台灣女性就業的潛在因素;The Use of Data Mining Techniques to Discover the Influential Factors of Women’s Employment in Taiwan
Authors:	蔡恩心;Tsai, En-Hsin
Contributors:	人力資源管理研究所
Keywords:	影響台灣女性就業因素;資料探勘;決策樹;人力資源管理政策;結婚與生育;influential factors of women’s employment in Taiwan;data mining;decision tree;human resource management policy;marriage and childbirth
Date:	2020-05-15
Issue Date:	2020-06-05 16:26:01 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究透過政府之長期調查，運用數據探勘之決策樹技術生成預測模型，旨在獲得台灣女性面臨結婚與生育階段時影響其就業考量的潛在因素與規則。利用2016年資料庫中2741筆職業婦女資料作為實證研究樣本。研究結果顯示二項特徵：最小孩童三歲前主要照護者和婦女結婚前之就業類型（是否為部分工時、臨時性或人力派遣工作）是預測台灣工作婦女經歷婚育階段就業考量之最重要的潛在影響因素。本研究成果能夠提升台灣婦女於制定人力資源管理策略或公共雇傭政策的品質，藉由相對易於解釋、計算成本不昂貴的資料探勘技術與善用組織周圍的既存資料，本研究所提供之婦女就業考量方向得以更有效率的方式，來協助台灣政府與企業促進台灣婦女的勞動參與率。;This study uses decision tree of data mining technique to generate predictive models from national survey data, aiming at obtaining the influencing factors with rules that affect Taiwanese women’s employment considerations during the periods of marriage and childbirth. We use a sample of 2741 Taiwan mother workers in 2016 in our empirical research. Our results discover that two potential influencing factors, namely, major carer for the youngest child when he/she was under the age of 3 and job type before marriage (i.e., whether you are part-time, temporary or dispatched workers), predict Taiwanese female workers’ employment considerations during the periods of marriage and childbirth. Our findings could improve the quality in formulating successful human resource strategies or public employment policies for Taiwanese women. Via relatively easy to interpret, computationally inexpensive data mining approach and utilize the existing data in the surrounding organizations, we provide the directions of women′s employment considerations could help government and companies to boost female labor force participation with a more effective way.
Appears in Collections:	[Graduate Institute of Human Resource Management ] Electronic Thesis & Dissertation

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