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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

從意識到行動:以《女聲》網站兩位站長生命故事為例

田乙汝, Tien, Yi-Ju Unknown Date (has links)
藉由接觸《女聲》網站,研究者得知兩位女性站長獨立經營網站五年多,從中理解站長強調各種女性的發聲權,並以書寫日常生活事件作為論述基調,以及鼓勵女性網友發言,引發研究者對兩位站長本身產生濃厚興趣,欲探討兩位女性站長的女性主義意識發展過程、參與女性運動的經歷,及其與網路科技的密切關係。 本論文採用生命故事研究取徑,透過接觸網站與親身訪談,理解兩位女性站長的生命內涵與故事。本研究發現兩位站長的女性主義意識發展與其個人婦運經驗,呈現彼此辯證的情形,具體顯現經驗與覺醒,或說實踐與意識,兩兩交互影響,以循環或螺旋方式持續進展,非線性走向,不斷歷經微妙或戲劇性轉折;此外,兩位站長與網路科技的關係,則呈現親近與親密的樣貌。換言之,兩位女性的個別經驗與感受,實則展現性別、科技與運動主體性交織的生命故事。
2

金融業與網路科技業導入巨量資料系統的關鍵因素之個案研究 / Case Studies of Key Factors for implementing Big Data system on Financial and Internet Technology Industries

陳冠廷, Chen, Kuan Ting Unknown Date (has links)
隨著網際網路的普及,智慧型手機與物聯網開始興起,根據資策會的調查,台灣約有49.5%的智慧型手機占有率,大約每2人就有一人擁有智慧型手機,而物聯網的興起,製造了大量的數據與資料,而這些數據與資料透過不同的處理方式,可帶給企業不同的商業智慧與洞見,而傳統產業因此面臨了巨大的轉變與挑戰,優步(Uber)就是改變傳統計程車產業與物聯網的一個例子,顧客不再需要招手才能搭上計程車,靠著網路、手機APP與GPS定位系統即可獲知車輛資訊、到達時間與聯絡司機,而優步可掌握乘客資訊、行車路線與顧客服務。不僅僅是計程車產業,亞馬遜的崛起也代表了傳統零售業的轉變,因此如何面對巨量資料對傳統企業都將是一項挑戰。 巨量資料的導入與分析可以提供企業掌握消費者行為,也可透過數據分析研發新服務與產品,此研究從三方面來探討金融產業與網路科技業導入巨量資料系統的關鍵因素,分別為導入流程、企業本身與巨量資料系統,另外藉由三家個案公司訪談,並輔以文獻所探討的研究架構來進行驗證”金融業與網路科技業導入巨量資料的流程為何?”、”金融業與網路科技業導入巨量資料時的關鍵因素?”、” 金融業與網路科技業導入巨量資料後有何優勢?” 本研究最後發現,金融業與網際網路業導入巨量資料分成三個階段,首先企業會先詮釋自身對巨量資料之定義,定義自身巨量資料之意義後,企業會開始集體研討導入流程,依照自身對巨量資料的詮釋來集體擬定對企業最好的導入流程,此階段通常會是三階段中耗時最長,也需做許多內外部研究、規劃與管理。最後一階段為實做階段,企業會依照集思廣益後所擬定出的計畫來完成巨量資料的導入。而本研究透過個案訪談也發現七項導入巨量資料之關鍵因素,包含,導入隊伍的組成、高層管理者的支持、導入時機、巨量資料系統的選擇、明確的目標與策略、內外部員工訓練與支援。最後企業運用第三方與開放式資料軟體來處理巨量資料使企業更了解顧客需求與運用於新產品研發。 / With the popularity of internet, smart phones and Internet of Things begin to emerge. According to Institute for Information Industry, there are approximately 49.5% of smart devices in Taiwan, which mean every two people will own at least one smart device. In addition, more devices are connected to the internet. Therefore, tremendous amount of data is created and increased exponentially. With applicable and correct techniques, Big Data can provide valuable insight and business intelligent. Traditional industries are forced to change. For example, Uber is one innovative idea that changes the ways people ride taxi. Riding taxi become more efficient and effective with Uber. This research explores critical factors of Big Data implementation on financial and internet technology industries from three perspectives. This includes key processes of the Big Data implementation, enterprises factors, and the Big Data system. Moreover, literature review was conducted to. In addition, three case studies were interviewed and analyzed based on research framework. Lastly, three research questions are answered. First, what are the key process for financial and internet technology industries implementing the Big Data system? Second, what are the critical factors for financial and internet technology industries implementing the Big Data system? Third, what are the potential benefits after the Big Data implementation? The research findings are primarily categorized into two parts. First, there are three phases of financial institution and internet technology industries implementing the Big Data system. The three phases included defining, brainstorming and implementation phases. The three phases are described below: 1. Defining Phase: Companies will first define their own interpretation of Big Data in order to plan and coordinated their implementation. 2. Brainstorming Phase: Companies averagely spent most of the time in this phase. The implementation team leads must brainstorm to find the best way to enforce and carry out the Big Data project by searching, organizing and surveying internal and externally. 3. Implementation Phase: Companies follow their previous made proposal steps by steps. This research also concluded and found several critical factors during the Big Data implementation. The critical factors included but not limited to: 1. An implementation team regardless the size to carry out the Big Data project 2. Top management’s commitment on implementation 3. Timing on the implementation 4. Big Data system selection 5. Clear goals and objectives

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