本論文主要目的在探討，工作十年以上之在職中生代優秀之專業主播，受過高等教育，在新聞界有一定之定位，為什麼選擇轉職？轉職的真正原因為何？本文研究目的有三： 一、瞭解電視新聞主播轉職原因； 二、探討哪些內部因素與外部因素導致主播轉職； 三、分析主播轉職後之斷層危機。 在瞭解內部因素與外部因素為何後，分析新聞主播在目前職場環境是否面臨斷層危機，接下來，造成轉職的主要動機。因此本研究發現如下： 一、轉職主播對媒體生態環境感到失望； 二、內部因素與外部因素的不認同： (一)外部因素：組織因素的介入 (二)內部因素：自我價值認同不夠 三、實務界的危機：中生代主播式微造成主播界的斷層與人才流失。 本研究係透過深度訪談法，訪問五位已轉職主播與兩位在職主播，進而得到上述研究發現。 / The purpose of the subject is to study the real reason why the anchors of TV news reporter who are with higher education and have settled down in the News Field for more than ten years, and decided to redirect their careers to something else? There are three approaches: 1.Understanding the circumstances of transferring 2.Researching the internal and external factors 3.Analyzing the risk of changes and challenges In light of internal and external concerns, it is the issue that we are confronting against with whether there is a supply shortage in the profession of Anchorpersons and the current working conditions of the press; subsequently , if might also cause the Anchorpersons’ career transformation. The followings are the disclosures: 1.The disappointment of media behavior and environment 2.The disagreement between internal and external system exchanges: (1) Outside factor: functional involvement (2) Inside factor: higher self-value 3.The risk of system construction: The disconnection and skill lost in the middle generation of the prime anchors in the TV news field. The above result comes from a constructive interview to five news reporters who have been transferred to the other business fields as well as two from the current news anchors.

轉職對於職涯發展來說，是非常重要的人生課題；而求職者目前在面臨轉職問題時，大多時候顯得手足無措，只能詢問親友的經驗或者憑著直覺找自己有興趣的工作；整個求職的過程就像是拿人生當賭注，運氣不好時即可能賠上美好的未來。 本篇研究使用國內某知名人力銀行的求職者資料，採用資料科學的方式，利用大量求職者的實際轉職資料來做資料分析與探勘，分析轉職高峰期、工作轉換頻率、跨職類轉職、跨產業轉職及轉職與景氣的關係，並使用J48、Naïve Bayesian Classifier、Logistic Regression、Random Forest、AdaBoost和Support Vector Machines這6種分類方法來預測轉職行為。 為了方便呈現實驗結果，本研究使用Google App Engine建立了一個轉職分析查詢系統，透過分析結果可以了解台灣各產業與各職類的轉職趨勢，而轉職預測功能也可以提供給求職者與人資人員做為參考。 / Career transition is important for employees. However, most of job seekers are helpless in decision of career transition. They can only make the decision based on the experience from their friends and family members, or by intuition. The decision of job seeking is like a gamble that may lose a better future when they faced with bad luck. This research tried to analyse and discover the behaviours of job transition from the job seeking data based on the data science approach. The job seeker’s data used in the study was obtained from the well-known job bank’s database. We analyse the behaviours of the job transition, including the peak months of transition, transition frequency, cross-job and cross-industry career transition. Moreover, we investigate the methods to predict the behavior of job transfer. Six kinds of classification algorithms were used to predict the behavior of career transfer, including the J48, Naïve Bayesian Classifier, Logistic Regression, Random Forest, AdaBoost and SVM. We develop the web-based Career Transition Analysis System to provide users the capability for behaviour analysis and prediction of career transition based on Google App Engine. The findings in this study are helpful for industry trends and career transition forecasts for job seeker and human resource staffs.