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研究生:顏佳芸
研究生(外文):Chia-yun Yan
論文名稱:以資料融合方法探討影響員工流動率之因素
論文名稱(外文):Identifying Factors Related to Turnover Rate by Data Fusion Approach
指導教授:吳植森
指導教授(外文):Chih-Sen Wu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系碩博士班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:79
中文關鍵詞:員工流動資料融合類神經網路
外文關鍵詞:Artificial Neural NetworkEmployee turnoverData Fusion
相關次數:
  • 被引用被引用:3
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近年來,員工流動在人力資源管理中是一個相當重要的議題。某種程度的流動率對企業組織而言是健康的,可以注入「新血」(new blood)、讓公司永久保持活潑、創新的氣氛,有助於組織保持彈性。但員工流動率過高會造成員工對企業忠誠度較低、人事成本的增加、公司服務品質較差等問題;而低的流動率也未必是一件好事,員工流動率太低則可能會使得企業陷入僵化,缺乏注入新想法。
  因此,本研究除了透過相關分析及迴歸分析探討員工工時、薪資對員工流動是否會有影響,並瞭解公司規模是否為影響員工流動的干擾因素,以及員工流動對生產力造成的影響;由於過去之研究大多是以問卷或訪談來進行員工流動議題之探討,無法考慮到時間因素,故本研究希望首先透過加入時間序列分析之自我迴歸整合移動平均模式(Autoregressive Integration Moving Average, ARIMA Model)及複迴歸分析(Multiple Regression analysis)對員工流動率進行預測,爾後再透過類神經網路(Neural Networks)對時間序列之資料進行資料融合(Data Fusion)以提高訊息的價值,並提供業界實行員工流動管理之參考,希望企業能夠防患未然,採取有效的策略維持企業最適的流動率,以減少不必要的人事成本。
  本研究透過類神經網路進行資料融合,實證研究發現經資料融合後可提升預測效能。同時,由於本研究所探討之25個工業相關行業皆屬於傳統產業,故經濟變數對於員工流動的影響程度較大,雖然薪資、工時相關變數對員工流動具有影響,但加入總體經濟相關變數後發現,其對員工流動之影響程度比薪資、工時相關變數大。經資料融合後可發現經濟相關變數中,尤其是以「失業率」及「經濟成長率」這二個變數對員工流動的影響最為顯著。因此,傳統產業主管在分析員工流動率時,可以經濟相關指標為主要考量。
Employee turnover is an important issue in human resource management field. Because keeping turnover rate in fixed level is beneficial for business. High turnover rate would make some problems, such as lower employee loyalty, higher cost of human resource, inferior service or quality. However low turnover rate may also jeopardize a company; such as making company become rigid, and being lack of new ideas.
In this study, we use regression analysis to detect the relationship among working hours, pay of employee, and employee turnover rate. And employee turnover is a effect of factor to productivity. We also use Autoregressive Integration Moving Average Model (ARIMA Model) and multiple regression analysis to predict employee turnover rate. Data fusion is then used to predict turnover rate and identify factors relating to turnover rate. Business can adopt strategies to maintain the optimal turnover, and reduce cost of human resource.
The result of this study shows that modeling with data fusion can perform better than that of original data. Because this study focuses on traditional industries, we use organizational size, regular earnings, average earnings, hourly salary, regular working hour, average working hour, Unemployeement Rate, Consumer Price Index(CPI), Misery index, Economic Growth Rate, and Stock price index rate for the data fusion model. We suggest that managers that employee turnover analysis requares considering external environmental factors.
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 3
1.4 研究流程 4
1.5 論文架構 5
第二章 文獻探討 6
2.1 員工流動(Employee turnover) 6
2.1.1 員工流動之定義 6
2.1.2 員工流動之類型 7
2.1.3 員工流動之效果 9
2.2 影響員工流動率之因素 11
2.2.1 員工薪資對員工流動率之影響 12
2.2.2 員工工時對員工流動率之影響 13
2.2.3 總體經濟因素對員工流動率之影響 14
2.3 員工流動率對生產力(Productivity)之影響 16
2.4 時間序列(Time Series)之概述 18
2.5 自我迴歸整合移動平均模式(Autoregressive Integration moving Average, ARIMA Model) 20
2.5.1 自我迴歸模式(autoregressive, AR) 21
2.5.2 移動平均模式(moving average, MA) 21
2.5.3 自我迴歸移動平均模式(autoregressive moving average, ARMA) 22
2.5.4 自我迴歸整合移動平均模式(ARIMA model) 23
2.5.5 自我相關函數(ACF)與偏自我相關函數(PACF) 24
2.5.6 ARIMA模式之建立 26
(一)模式鑑認 26
(二)參數估計 27
(三)模式檢定 27
2.6 資料融合(Data Fusion) 28
2.6.1 資料融合之定義與特性 28
2.6.2 資料融合之技術 29
2.6.3 類神經網路(Artificial Neural Network, ANN) 31
第三章 研究方法 34
3.1員工薪資、工時、生產力與員工流動率關係之假設 34
3.2 研究方法流程 35
3.3 資料來源 37
3.4 研究範圍與限制 38
3.5 變數定義與衡量 38
3.6資料融合模式之建立 39
3.6.1 融合模式 40
3.6.2 融合資料 41
3.6.3參數設定 42
3.7 預測效能評估之準則 43
第四章 實證研究與分析 44
4.1 迴歸分析 44
4.1.1 假設檢驗 44
4.1.2 研究假設之結果 53
4.1.3 迴歸分析之預測效能 54
4.2 ARIMA之預測結果 55
4.3 資料融合之結果與分析 58
4.3.1 資料前置處理 58
4.3.2 資料融合模式之建立 58
4.3.3 資料融合之模式效能評估 59
4.3.4 資料融合模式之分析 62
4.4 實證研究與分析之結論 68
第五章 結論 72
5.1 研究成果總結 72
5.2 未來研究建議 73
參考文獻 74
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