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研究生:邱啟原
研究生(外文):CHIOU, CHI-YUAN
論文名稱:企業人才用留策略-以A企業在職與離職因素分析為例
論文名稱(外文):Enterprise Talent Retention Strategy-Taking the Analysis of Factors of A Company's Incumbency and Resignation as an Example
指導教授:翁頌舜翁頌舜引用關係
指導教授(外文):WENG, SUNG-SHUN
口試委員:魏銪志劉上嘉翁頌舜
口試委員(外文):WEI, YU-CHIHLIU, SHANG-CHIAWENG, SUNG-SHUN
口試日期:2021-03-31
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:管理學院資訊與財金管理EMBA專班
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:49
中文關鍵詞:組織氣候策略性人力資源管理獎勵性薪酬離職風險離職原因
外文關鍵詞:Organizational ClimateStrategic Human Resource ManagementIncentive CompensationResignation RiskReason for Resignation
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透過主計處2018年事業人力僱用狀況調查統計結果顯示,年資與離職率呈負相關,年齡與離職率呈負相關。另外,在教育程度上則顯示出較高教育程度的進入率與退出率都高於較低教育程度,代表較高教育程度,有較多選擇職場的能力與機會。
因此企業內部服務年資在1-3年內之新進同仁,約有1/4會選擇離職轉換職場,工作10年之內離職人員則有62.8%,這部分的人才以未滿25歲~34歲人才為最多,企業在此階段投入較多的資源培訓此階段的人才,當企業積極投入教育訓練成本,無法反映在公司本身內部的報酬,在某種程度上呈現投入與產出不對等的無形損失,因此對於資源較缺乏的企業,則可能會吝於投入人才培訓成本,影響企業及社會整體經濟發展。
在離職因素非常複雜並非單一因素造成,其主要影響有組織信任(Organization Trust)、組織文化(Organizational Culture)、組織氣候(Organization Climate)、員工自我體現在工作上的滿意度、員工對薪酬體系的滿意度等原因。隨著大數據的發展,組織內部如何快速透過內部既有系統資訊取得相關訊息,如固定薪資、激勵性報酬、出勤異常狀況、延長工時、額外工作投入、薪資、職位、年資、考績、學歷、年齡、性別、婚姻狀況、等因素資訊,透過大數據工具之決策樹、類神經網路、K-means等模型等資料探勘(Data Mining)技術,使得企業能提早預測離職員工的跡象,提早擬定因應策略有效留住人才,本研究利用類神經網路預測離職人員與K-means方法將人員分出重要人才等兩項工具交叉比對人員清單,再透過決策樹分析出留用與離職敏感因子,提供企業更精準人員的用留策略。
基於資料探勘與大數據已是成熟技術,並結合公司內部HR擁有的龐大的資料基礎上,本研究透過工具達成:
1. 利用現有ㄧ般大多數企業內部已存在共同資料訊息內容,透過大數據分析工具,進行人員資料分析與群集。
2. 透過類神經網路預測將離職重要關鍵人才,透過決策樹分析在職影響因素、及離職影響因素與類別,提供有效留才參考決策方向。

According to the statistical results of the 2018 Business Manpower Employment Survey by the Comptroller and Accounting Office, seniority and turnover rate are negatively correlated, and age and turnover rate are negatively correlated. In addition, in terms of education level, it shows that the entry and exit rates of higher education levels are higher than those of lower edu-cation levels, which means higher education levels and more ability and opportunities to choose the workplace.
Therefore, about one-fourth of the new employees who have served in the company within 1-3 years will choose to leave and switch to another workplace, and 62.8% of those who leave within 10 years of work. This part of the talents is under the age of 25 to 34. Talents are the most. Companies invest more resources at this stage to train talents at this stage. When compa-nies actively invest in education and training costs, they cannot be reflected in the internal re-muneration of the company itself. To some extent, there is an intangible imbalance between in-put and output. As a result, companies that lack resources may be hesitant to invest in personnel training costs, which will affect the overall economic development of the company and society. The resignation factor is very complex and not caused by a single factor. It mainly affects the organizational trust (Organization Trust), organizational culture (Organizational Culture), organ-izational climate (Organization Climate), employee satisfaction at work, and employees’ percep-tion of the salary system. Satisfaction and other reasons. With the development of big data, how can the organization quickly obtain relevant information through internal existing system infor-mation, such as fixed salary, incentive compensation, abnormal attendance, extended working hours, additional work input, salary, position, seniority, performance appraisal, and academic qualifications Information on factors such as age, gender, marital status, etc., through data min-ing technology such as decision trees, neural networks, K-means and other models of big data tools, enables companies to predict the signs of leaving employees early. To formulate response strategies to effectively retain talents, this study uses neural networks to predict resignation per-sonnel and K-means personnel to classify important talents and other two tools to cross compare the personnel list, and then analyze the retention and resignation sensitivity factors through the decision tree. The company's more precise personnel retention strategy.
Based on the mature technology of data mining and big data, combined with the huge data owned by the company's internal HR, this research uses tools to achieve:
1. Use the existing common data message content in most companies to analyze and cluster personnel data through big data analysis tools.
2. Use neural networks to predict key talents who will resign, analyze in-service influencing factors, and resignation influencing factors and categories through decision trees, and pro-vide effective reference for decision-making directions for retaining talents.

摘 要 i
ABSTRACT iii
誌 謝 v
目 錄 vi
圖目錄 viii
表目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 4
第二章 文獻探討 5
2.1 策略人力資源管理 5
2.2 策略人力資源管理定義與研究 6
2.3 SHRM模型 8
2.4 激勵性薪酬 11
2.4.1 總體獎酬系統 11
2.4.2 薪酬管理原則:公平理論 12
2.4.3 激勵性薪資策略應用 13
2.5 組織氣氛 15
2.6 離職傾向 15
第三章 研究方法 18
3.1 研究設計 18
3.2 資料處理及研究方法流程 18
3.3 研究分析工具 26
3.3.1 K-means群集分析 26
3.3.2 決策樹分析 27
3.3.3 類神經網路分析 28
第四章 結果分析與討論 30
4.1 K-Means結果分析 33
4.2 類神經網路預測分析結果 34
4.3 決策樹結果分析 36
4.4 模型綜合分析結果應用 37
第五章 結論與未來展望 41
5.1 研究結論 41
5.2 各模型研究發現 42
5.3 研究貢獻 43
5.4 研究限制與未來展望 44
參考文獻 46


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