近年來，銀行業面臨過度競爭的困境，銀行家數過多使得業者紛紛採用價格競爭的方式來搶攻市場。加上普遍缺乏金融創新的能力，服務與產品趨向同質化，更使該產業處於競爭激烈的環境。政府為了提升公營銀行的經營績效，遂以民營化作為提升競爭力的方法，民營化後是否能夠有效提升績效，來解決效率不彰的問題，是個值得深入探究的議題。 本研究即探討公營銀行民營化前後經營績效的差異。從銀行經營的五大原則，安全性、經營能力、流動性、獲利性和成長性等五個財務因素切入，運用結構方程式模式探討因素間的關係，再進一步研究15項財務指標在民營化前後之差異。研究發現，安全性與成長性會顯著地正向影響獲利性，而經營能力也會顯著地正向影響成長性。表示銀行的管理者想要維持獲利能力，必須同時兼顧業務成長與風險的控管。 此外，在民營化之後顯著改善的財務指標有三個，分別為股東權益比率、流動比率與速動比率。顯著衰退的則有八個，分別為固定資產轉率、淨值週轉率、總資產週轉率、資產報酬率、淨值報酬率、純益率、淨值成長率與營收成長率等。因此，公營銀行在民營化之後，顯著改善了安全性與流動性指標，雖然加強了風險控管，但是在經營能力、獲利性與成長性指標中卻呈現顯著衰退現象。顯示民營化對於公營銀行競爭力的提升，在研究期間內成效並不如預期。 關鍵字：公營銀行、民營化、經營績效、銀行經營原則、結構方程式模式 / In recent years, there are many difficulties in the Taiwanese banking industry, especially the over-banking problem. Every bank uses the price competition to broaden its market share because they have no innovative ability to create new products and services. In order to solve this problem, Taiwanese government regarded privatization as the best way to improve state-owned banks’ operating performance. This issue has also become a very popular subject of studies. This study investigated the difference of operating performance between pre-privatization and post-privatization. It discussed and examined the relationship among Safety, Activity, Liquidity, Profitability and Growth. Based on the results, Profitability is directly affected by Safety and Growth. In addition, Growth is also directly affected by Activity. So, if managers of a bank want to make profit continuously, they have to care about not only sales growth but also risk management. After privatization, there are three increasing indicators, including equity ratio, current ratio and quick ratio. However, there are still eight decreasing ones, inclusive of fixed asset turnover ratio, equity turnover ratio, total asset turnover ratio, return on asset (ROA), return on equity (ROE), profit margin on sales, equity growth rate and sales growth rate. As a result, privatization is probably not the best solution to improve the operating performance of state-owned banks. Key words: state-owned banks, privatization, operating performance

本研究的目的在於結合部份計分模式(partial credit model, PCM)與模糊集合論(fuzzy set theory)，提出評定量表的不同計分方式：模糊部份計分法(fuzzy partial credit scaling, FPCS)。FPCS是根據 PCM 所估計出的梯度參數(step parameters)來建構三角形模糊數，三角形模糊數代表選擇某個特定選項的受試者的能力分配情形。接著，利用中心法(center of gravity method) 將三角形模糊數解模糊化為純量。最後，利用隸屬度當作權重，計算個別受試者的模糊觀察分數，並且用模糊觀察分數當作量表的總分。 本研究採用貝克憂鬱量表(Beck Depression Inventory-II, BDI)中文版為研究工具。本研究的樣本分為憂鬱症病患與非憂鬱症的一般大學生兩大類。240位憂鬱症病患樣本是由台北市立和平醫院精神科門診募集而來；321位大學生則以便利抽樣的方式募集而來。 為了驗証FPCS的有效性，本研究進行三個子研究，來比較FPCS與傳統計分法在信度、效度、集群分析的分類正確性。 子研究一探討FPCS的信度。本研究以Cronbach alpha係數來衡量量表的內部一致性，並且以結構方程式模式(structure equation modeling)進行驗證性因素分析所估計的各試題的變異數被潛在構念解釋的比例當作信度的指標。由研究結果顯示，以量表整體而言，FPCS計分的結果得到較高的內部一致性；以各題而言，量表各試題的變異數被潛在構念解釋的百分比高於傳統的原始分數。此結果顯示FPCS的計分方式可以降低測量誤差，提升信度。 子研究二探討FPCS的效度，本研究以精神科醫師的診斷當作效標，分別以FPCS與原始分數兩種不同的計分法當作自變項，以預測效度當作效度的指標。首先，將是否罹患憂鬱症編碼為二元變數，不同計分法所得到的量表分數當作自變數，進行Logistic迴歸分析。研究結果顯示，相較於原始分數，FPCS預測罹患憂鬱症的正確率由 74.8% 提升到 77.2%。接下來，依照所有樣本的憂鬱程度，區分為一般樣本、憂鬱症且緩解、憂鬱症無緩解三類，進行區別分析。研究結果顯示，相較於原始分數，FPCS分類正確率由 71.2% 提升到 80.7%。上述的研究結果顯示，FPCS具有較高的效度，可以降低誤判憂鬱症的機率。 子研究三比較模糊集群分析(fuzzy c-means, FCM)與傳統明確邏輯的集群分析。首先利用分群效度(clustering validity)指標，決定群數為三群。並以此結果，指定模糊集群、Wald法、k-means法之群數。為了比較分類的效果，將模糊集群之樣本，指定給獲得最大隸屬度之集群。並且以醫師的診斷的憂鬱程度當作評估分類結果之標準。研究結果顯示，相較於傳統明確邏輯的集群分析(Wald法、k-means法)，模糊集群分析得到分群結果，與醫師的診斷的結果有最高的相關。結果顯示模糊集群分析更能夠忠實的反映資料結構。 整體而言，相較於原始分數，FPCS有較高的信度、效度、分類正確性。此實証性研究結果支持了模糊集合論應用於心理學研究的可行性；多值的模糊邏輯比二值明確邏輯更能夠正確反映出人類的思維。 / The aim of this study was to propose and validate the new scaling method, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory with the partial credit model (PCM) to score rating scales. To achieve this goal, the Chinese version of BDI (Beck Depression Inventory-II) was administrated to a depressed sample of patients and a non-depressed sample. The depressed sample consisted of 240 outpatients who were diagnosed as depressed by a psychiatric doctor, while 321 undergraduate students were recruited for the nondepressed sample. In FPCS, triangular fuzzy numbers were generated by step parameters to characterize distributions of each alternative value. Next, the center of gravity (COG) method was applied to “de-fuzzify” the fuzzy number into a scalar. Then, the “observed fuzzy scores” defined in FPCS were calculated as the sums of fuzzy number values weighted by membership degrees for the following analysis. Three studies were performed to compare the differences in reliability, validity and clustering precision between the raw score and FPCS. In Study One, the reliability issue of FPCS was discussed. The results of confirmatory factor analysis demonstrate that the BDI reliability was higher in FCPS than in raw scoring. That is, compared with raw scoring, scoring via FPCS produced fewer measurement errors, meaning that more variances in an item of BDI were explained by depression. In Study Two, the predictive validity issue of FPCS was investigated. First, logistic regression analysis was used to predict the odds of suffering depression based on FPCS and the raw scores. The analytical results showed that, via FPCS, the probability of correct classification of depressed and non-depressed was raised from 74.8% to 77.2%. Next, discrimination analysis was performed to classify the subjects according to the severity of depression into three categories: non-depression, depression with remission and depression without remission. The analytical results exhibited that, via FPCS, the probability of correct classification of severity of depression was raised from 71.2% to 80.7%. These two statistical analyses consistently show that FPCS exhibited higher predictive validity than did the raw score. That is, BDI scoring via FPCS makes more accuracy predictions for depression than raw score. In Study Three, fuzzy c-means (FCM) clustering was applied to partition the sample according to severity of depression. To examine explore whether fuzzy-based clustering methods uncover the information inherent in the latent structure more accurately than crisp clustering, FCM, Wald’s method, and k-means method were performed. The analytical results reveal that the association between the original and classified membership generated by FCM was stronger than that of the Wald and k-means methods. Hence, FCM revealed the data structure most accurately. Overall, FPCS has been consistently shown to be superior to raw scoring in terms of reliability, validity, and clustering accuracy. This study has empirically shown that fuzzy set theory is applicable to psychological research.