本文使用Network DEA模型，將台灣銀行業的生產過程分解成營運階段以及財務階段來討論，並將所有樣本銀行分成金控下子銀行V.S.非金控下子銀行，民營銀行V.S.泛公股銀行，商業銀行V.S.其他專業銀行以及新銀行V.S.舊銀行進行比較，希望藉此使各銀行經理人更了解自己經營銀行在各方面的優劣點，並且更容易從中檢討出無效率的來源，進而改善之。另外，各銀行的風險管理愈來愈受主管機關以及社會大眾的重視，本文特別將風險變數以及資本適足率限制納入模型中，以期能更忠實反映各銀行的經營績效。 結果發現，泛公股銀行的財務效率有顯著優於民營銀行的傾向、舊銀行在營運效率以及財務效率也有顯著優於新銀行的傾向；顯示民營銀行以及新銀行在成本控管能力上的績效表現明顯較差，需要透過適當的管理制度改善等以有效提升經營績效。 / This paper examines the performance of the banking industry in Taiwan via Network DEA model, which decomposes the production process into operational, and financial ones. Furthermore, as more and more attention is aroused by the public in the topic of the risk management, we incorporate risk variables and the restriction of the BIS ratio in order to have efficiency scores more faithfully. We divide the banks into groups and compare the efficiency results, and it turns out that the banks in public ownership exhibit superior performance on financial efficiency than the privately owned banks; The banks established before 1990s perform better operational and financial efficiency than that of the banks established after 1990s.

The issues of performance measurement and efficiency analyses for transit industries have been gaining significance due to severe operating conditions and financial constraints in which these transit agencies provide service. In this research, we present an approach to measure the performance of Urban Transit Networks, specifically, bus routes that comprise the network. We propose a math programming model that evaluates the efficiencies of bus routes taking into consideration, the service providers, the users and the societal perspectives. This model is based on Data Envelopment Analysis (DEA) methodology and derives from Network Theory, Network Modeling in DEA, Goal Programming & Goal-DEA and 'Environmental' Variables. This approach enables the decision maker to determine the performance of its units of operations ('bus routes' in our case), optimally allocate scarce resources and achieve target levels for 'externality' variables for these bus routes and for the whole network. We further recommend modifications to the model, for adaptation to other modes of transportation as well as extend its applicability to other applications/scenarios. / Master of Science

傳統DEA效率分析假設產業具單一生產過程，直到2000年，Färe and Grosskopf 提出Network DEA，闡明產業生產過程應分屬多階段性質。本研究應用其架構，假設職棒產業生產過程為兩個階段，並特別以Sexton and Lewis (2003) 增加中間產出的Two-Stage DEA 法，即第一階段的產出作為第二階段的投入，進行1992年至2004年「中華職業棒球聯盟」，共71個DMUs的實證分析。 Two-Stage DEA的第一階段是指花錢聘雇球員，而球團有效花錢聘雇球員的程度稱為「前置效率」，效率平均值為0.994；第二階段效率是指球隊正式比賽時，球員是否充分發揮技術潛能贏球，稱為「臨場效率」，效率平均值為0.969；而包含第一、二階段的整體球團運作效率則為「組織效率」，效率平均值為0.798。透過與傳統DEA的BCC模型之比較，發現Two-Stage DEA提供較豐富的組織運作過程資訊，俾管理者找出球團之無效率階段。 復以Tobit截斷迴歸模型，探討影響球團「前置效率」、「臨場效率」與「組織效率」之變數為何。結果指出，球隊對戰觀眾數、聯盟變革與現場直播對「前置效率」有顯著影響，「臨場效率」則受到臨時性獎勵制度與投手平均年齡的影響，而對戰觀眾數、聯盟變革、總教練的更動頻率與常設性獎勵制度，則是造成球隊「組織效率」差異的主因。 關鍵字：Network DEA、中間產出、Two-Stage DEA、職業棒球、效率 / Traditional DEA gauges efficiencies with only one production process, while in this study we apply Network DEA initiated by Färe and Grosskopf (2000), and in particular follow the Two-Stage DEA model incorporating the intermediate products, outputs from the first stage becomes inputs to the second stage, by Sexton and Lewis (2003) to evaluate the production efficiency of 71 DMUs of the Chinese professional Baseball League (CPBL ) from 1992 to 2004. How fair are the ball teams paying the players is called ”front office efficiency”, arithmetic mean is 0.994, in the first stage, how potentially successful are the teams playing the games is called “on-field efficiency”, arithmetic mean is 0.969, in the second stage, and how potentially successful are the teams playing the games if with perfect front office efficiency of the teams is called “organization efficiency”, arithmetic mean is 0.798. Comparing Two-Stage DEA model with BCC model of traditional DEA, we find that the former model provides more information of organizational operations for managers to understand and better the performance of the teams. Tobit regression analysis shows that (1) the front office efficiency is significantly positively influenced by spectators, variation of the league and television live, and (2) the on-field efficiency is significantly positively influenced by extemporaneous bonus and pitcher’s age. (3) The organization efficiency is significantly positively influenced by the spectators, variation of the league and fixed bonus, but the organization efficiency is significantly negatively influenced by change of coaches. Keyword：Network DEA, Intermediate products, Two-Stage DEA , Efficiency, Professional baseball