O2O 是一個嶄新的商業模式，透過網路上的服務提供導引線上消費者到線下店面進行消費或是取用實體服務。在近年，O2O迅速成為眾企業討論的話題。除了知名零售和電子商務企業，O2O商業模式以在各行各業中逐漸發展，舉例來說，知名零售業的Walmart和Target; 服飾業的Uniqlo和GAP; 配件-眼鏡的Warby Parker和餐飲業麥當勞和Instacart。業界充滿林林種種的實際個案，但學術上卻缺乏完整的研究來說明O2O的現況，我們不了解O2O的定義和範圍，甚至對於企業來說，到底該如何成功地實現O2O的精華。此篇研究的目的是希望能提供企業更具體了解O2O的概觀，並可以檢視在規劃一個O2O專案的發展需要有哪些關鍵成功因素。 基於之前少數的文獻和大量從網路、報章雜誌所蒐集的業界個案，我們的研究透過這些資料蒐集分為兩階段研究。第一階段，根據蒐集的資料，我們建立五種型態的O2O 商業模式 –(1) Commerce O2O; (2) Try-on O2O; (3) Promotional O2O; (4) Experience O2O; and (5) Crowdsourcing O2O。我們專注在promotional O2O的五家在台灣企業的個案研究。第二階段，根據文獻我們先定義出可能的關鍵成功因素列表，再舉辦深入的企業訪談，透過訪談的方式來驗證並修改原本的關鍵成功因素列表，為3個分類-(1)4個技術關鍵成功因素: 系統操作友善, User Interface 設計, 精確的定位, 穩定的IT系統承載的能力;(2)5個管理關鍵成功因素-管理者支持, 客戶關係管理, 使用者參與方案設計, 專案執行的高貫徹度, 專案的監控管理追蹤;(3)4個組織關鍵成功因素-完整的人員訓練, 線上線下的流程無縫整合, 不斷創新與思考方向, 明確的專案目標。最後，我們希望這篇研究可以幫助企業更了解O2O商業模式，並且幫助他們有效的發展O2O專案。 / Online-to-Offline (O2O), a brand-new business model that drives online visitors to purchase in-store by offering services online, has received a great deal of attention. In addition to well-known retailers and ecommerce businesses, the O2O business model has been adopted by companies across a variety of industries, such as Target and Macy's in retailing, GAP and Uniqlo in apparel, Warby Parker in accessories and McDonald’s and Instacart in food. However, less attention has been paid in the literature to the clear definition and scope of both online and offline businesses in the O2O business model, and there is limited understanding of how to build a successful O2O project. The objective of this study is to organize comprehensive information for the O2O business model and to examine the critical success factors (CSFs) for building O2O business models. Based on the literature and case studies of the O2O business model, this study builds a framework for data collection and is conducted in two stages. First, we build a preliminary finding regarding the five major types of O2O business models—(1) Commerce O2O; (2) Try-on O2O; (3) Promotional O2O; (4) Experience O2O; and (5) Crowdsourcing O2O—based on the literature and more than 50 practical cases and select five promotional O2O companies in Taiwan as our focus. Second, we conduct an in-depth case study on selected cases related to possible CSFs for successful O2O implementation. The critical success factors (CSFs) for building O2O business models are–(1) Four factors of technological dimension- User Interface design, Ease of use application, Accurately located function and IT load balancing capability; (2) Five factors of management dimension- Management support, Good CRM system, Strategic execution capability, Actively involve end users in solution design and Measure, monitor, and track; (3) Four factors of organization dimension- Complete staff training, Seamless the process of online and offline channels, Reinvent the company's future and Establish a clear project goal. The research results not only provide a complete O2O overview but also verify and enhance the list of CSFs for building O2O business models. It is hoped that we can gain a better understanding of the O2O business model from these cases and thus help companies develop effective plans for building O2O projects.

表面增強雷射脫附遊離/飛行時間質譜(SELDI-TOF-MS)是種屬於高維度的蛋白質質譜儀資料，主要是用來偵測蛋白質分子的表現。由於SELDI技術的限制，導致掃描出來的質譜儀資料往往存在誤差與雜訊，因此在分析前通常會先針對原始資料進行低階的事前處理，步驟包括去除基線、正規化、峰偵測(peak detection)與峰調準(peak alignment)。本文中所探討前列腺癌資料，可分成正常、良性腫瘤、癌症初期與癌症末期四種類別。我們分析及比較兩筆事前處理的蛋白質質譜資料，包括我們自行處理的以及Adam等人所處理的資料。為了解決SELDI在偵測分子質量時常出現的位移誤差以及同位素的問題，我們提出以”質荷比段落”當作新的特徵變數的想法來進行分析。本文利用「ROC曲線下面積」(AUC)當作選取的準則來挑選出重要的質荷比段落，而分類方法則採用支援向量機(SVM)。在四分類的分類結果中，我們自行處理的事前處理資可以得到訓練資料89%及測試資料63 %的正確率。而Adam等人所處理的事前處理資料，則得到訓練資料94%及測試資料86 %的正確率。本研究結果指出不同事前處理的方法對分類結果確實有影響，同時也驗證了利用”特徵變數段落”的方法來進行分析的可行性。 / The surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a technique for presenting the expression of molecular masses. It is obvious that every spectrum has a huge dimension of features. In order to analyze these types of spectra samples, preprocessing steps are necessary. The steps of preprocessing include baseline subtraction, normalization, peak detection, and alignment. In our study, we use a prostate cancer data for demonstration. This prostate cancer data can be classified into four categories, namely, healthy men, benign prostate hyperplasia, early stage prostate cancer, and late stage prostate cancer. We analyzed both the preprocessed data processed by ourselves and the preprocessed data done by Adam et al.. In this thesis, we use segmentations of features as “new features” in attempt to solve problems due to location shifts and isotopes. The selection of important segmentations was based on the values of AUC and the SVM was applied for classification. For four-class classification, 94 % and 86 % of accuracy were obtained for training samples and validation samples, respectively, by using Dr. Adam et al.’s preprocessed data, and 89% for training samples, and 63% for validation samples by using our preprocessed data. This study suggested that the preprocessed method does have effect on classification result and a reasonable classification result can be obtained by using segmentations of features.

本論文的研究目標是建構一個由多標記複合成的最佳疾病診斷工具，所考慮的評估準則為操作者特徵曲線在特定特異度範圍之線下面積(pAUC)。在常態分布假設下，我們推導多標記線性組合之pAUC以及最佳線性組合之必要條件。由於函數本身過於複雜使得計算困難。除此之外，我們也發現其最佳解可能不唯一，以及局部極值存在，這些情況使得現有演算法的運用受限，我們因此提出多重初始值演算法。當母體參數未知時，我們利用最大概似估計量以獲得樣本pAUC以及令其極大化之最佳線性組合，並證明樣本最佳線性組合將一致性地收斂到母體最佳線性組合。在進一步的研究中，我們針對單標記的邊際判別能力、多標記的複合判別能力以及個別標記的條件判別能力，分別提出相關統計檢定方法。這些統計檢定被運用至兩個標記選取的方法，分別是前進選擇法與後退淘汰法。我們運用這些方法以選取與疾病檢測有顯著相關的標記。本論文透過模擬研究來驗證所提出的演算法、統計檢定方法以及標記選取的方法。另外，也將這些方法運用在數組實際資料上。 / The aim of this work is to construct a composite diagnostic tool based on multiple biomarkers under the criterion of the partial area under a ROC curve (pAUC) for a predetermined specificity range. Recently several studies are interested in the optimal linear combination maximizing the whole area under a ROC curve (AUC). In this study, we focus on finding the optimal linear combination by a direct maximization of the pAUC under normal assumption. In order to find an analytic solution, the first derivative of the pAUC is derived. The form is so complicated, that a further validation on the Hessian matrix is difficult. In addition, we find that the pAUC maximizer may not be unique and sometimes, local maximizers exist. As a result, the existing algorithms, which depend on the initial-point, are inadequate to serve our needs. We propose a new algorithm by adopting several initial points at one time. In addition, when the population parameters are unknown and only a random sample data set is available, the maximizer of the sample version of the pAUC is shown to be a strong consistent estimator of its theoretical counterpart. We further focus on determining whether a biomarker set, or one specific biomarker has a significant contribution to the disease diagnosis. We propose three statistical tests for the identification of the discriminatory power. The proposed tests are applied to biomarker selection for reducing the variable number in advanced analysis. Numerical studies are performed to validate the proposed algorithm and the proposed statistical procedures.