資料包絡分析方法應用於大學績效之評估十分普遍，但所得之結果往往會具有多重有效率單位，無法真正區分出最績優的學校。本研究採用「以參考網路為基礎的排序方法」，針對國內國立大學院校進行績效評估之實證研究，其特色為結合資料包絡分析與社會網路分析兩種方法，將傳統資料包絡分析的結果轉成一種廣義的社會網路，然後再以社會網路分析方法的中心性觀念，將有效率之決策單位做進一步的排名，來補足以往以一般性資料包絡分析評估大學經營績效之不足。
本研究採取兩階段的效率評估方式，將大學院校經營效率畫分成行政管理效率與研究教學效率，讓大學經營者得以深入了解其管理上之優缺點。結果顯示，在行政管理階段共計有23所大學院校是有效率的，且其表現整體差異度不大；而研究教學階段有效率之大學院校共計有21所，且與第一階段有效率之大學院校有顯著的差異。經由排序之後發現，傳統社會大眾所認知較具知名度的大學院校，在研究教學階段其效率表現與預期的相符合，但在行政管理效率的表現上卻不盡然如此。雖然一般綜合型大學院校與技職體系型大學院校相比，相對擁有較多的行政資源，但其資源之運用效率卻不及於技職體系型大學院校。而在研究教學效率方面，技職體系型大學院校可能因為與一般綜合型大學院校之教學目的不同，所以在期刊發表、教學收入等研究能量上稍嫌不足。
對整體有效率之大學院校將兩階段各別之輸出、輸入項目作排序後，也能找出各項目之標竿學校，配合強弱項分析，讓各大學院校檢討本身不足之處，並參考各標竿學校作為學習的對象，汲取其精華以提升自我之績效表現。最後，本研究將所有有效率之大學院校進行群組分類，探討影響各類型大學院校績效良窳的關鍵因素。結果發現，重點培育型大學院校無論是因為有較佳的績效而受到政府擬訂相關政策來補助，還是因為受到政府資源之贊助而有較佳的效率，相對於非重點培育型大學院校而言，擁有更優異之研究教學效率表現。因此建議未來政府可多推動此類對於大學院校資金挹注或是培育計畫的方案，或許能夠藉此相形競爭而大幅的提升我國大學院校整體之經營績效與水平。

This study evaluates of the performance of the national universities in Taiwan. It applies the data envelopment analysis and the network-based ranking method. The network-based approach applies the centrality concept developed in social network analysis to discriminate efficient decision making units (DMUs) as determined by standard DEA. .It removes the bias caused by a scale difference among organizations and highlighting the approach’s ability to identify the strengths and weaknesses of each organization.
In order to evaluate the operational performance of each DMU (national university) precisely, two-stage analysis model is used which separates the university performance into the “administration performance” and “teaching and research performance”. Furthermore, we separate the efficient DMUs into four categories which are general normal university, technical university, government-supported university and non-government-supported university to discuss the management inspiration. The analysis results show that the average performance of normal university in the first stage (administration performance) has no more differences between technical university, but with significant difference in the second stage (research performance); and the average performance of the two stages between government-supported university and non-government-supported university both exist a huge gap which means the support from government is work.
According to the analysis results, we successfully apply the network-based DEA ranking method and two-stage analysis model to evaluate the operational performance of the national university in Taiwan. This method not only distinguishes the efficient DMUs, but also rank sort the efficient DMUs to let the manager get more information for analyzing and discussing.

1.李宜芳，「教育部補助與公私立大學辦學績效之評估」，國立臺北大學財政學研究所碩士論文，民國89年。
2.汪漢英、黃文聰、黃開義、畢威寧，「應用資料包絡分析法之大學學系績效評估實證研究」，人文暨社會科學期刊，第三卷第二期，民國96年，55-56頁。
3.沈艷雪，「校務基金績效評估：以某大學個案為例」，國立成功大學會計研究所碩士論文， 民國91年。
4.姜波英，「我國國立大學經營效率之探討」，國立政治大學行政管理研究所碩士論文，民國93年。
5.高強與黃旭男，管理績效評估--資料包絡分析法，華泰書局，臺北，民國92年。
6.高強，「由資料分配提昇多單位組織之整體效率」，中山管理評論，第二卷第二期，民國93年， 18-28頁。
7.孫遜，資料包絡分析法-理論與應用，臺北：揚智出版社，民國93年。
8.教育部，「82-85學年度私立大學校院中成校務發展計畫審查報告」，高教簡訊，特刊，民國83年。
9.郭振雄，「多重生產程序之績效評估：我國大學院校效率衡量」，臺灣大學會計學研究所博士論文，民國89年。
10.陳美菁、陳建勝，「我國高等技職校院辦學績效之研究」，商管科技季刊，第四卷第三期，民國94年，216 -280頁。
11.陳榮方，「以資料包絡法評量我國大學校院之教育品質」，高雄科學技術學院學報，第二十八卷，民國87年，227-238頁。
12.黃旭男、高棟樑，「臺灣地區產險公司經營績效之評估：二階段資料包絡分析法之應用」，保險專刊，第二十一卷第一期，民國94年，57-79頁。
13.黃義中，「大學的經營績效與品質」，逢甲大學經濟學研究所碩士論文，民國91年。
14.曾憲立，「國內大學績效評量有關規模效率之探討-資料包絡分析法之應用」，國立中山大學公共事務管理研究所碩士論文，民國95年。
15.盧文民、何東興，「國立大學校務基金績效評估之研究」，教育政策論壇，第十二卷第三期，民國98年，163-192頁。
16.鄭淑芳，「國立大學校院相對效率之研究－使用資料包絡分析法」，國立臺灣大學會計學研究所碩士論文，民國87年。
17.賴仁基，「我國綜合大學效率差異之衡量－資料包絡分析法的應用」，國立政治大學財政研究所碩士論文，民國86年。
18.顧志遠，「有關非營利機關效率評估及預算再分配之整體規劃模式研究」，國立清華大學工業工程研究所碩士論文，民國76年。
19.Abbott, M. and Doucouliagos, C., “The Efficiency of Australian Universities: A Data Envelopmenta Analysis,” Economics of Education Review, Vol. 22, 2003, pp. 89-97.
20.Ahn, T., Arnold, V., Charnes, A., and Cooper, W. W., “DEA and Ratio Efficiency Analyses for Public Institutions of Higher Learning in Texas,” Research in Governmental and Nonprofit Accounting, Vol. 5, 1989, pp.165–185.
21.Aldler, N., Friedman, L., and Sinuany-Stern, Z., “Review of Ranking Methods in the Data Envelopment Analysis Context,” European Journal of Operational Research, Vol. 140, 2002, pp. 249-265.
22.Ali, A. I. and Seiford, L. M., “The Mathematical Programming Approach to Efficiency Analysis,” In H. O. Fried, C. A. K. Lovell, and S. S. Schmidt (Eds.), The Measurement of Productive Efficiency: Techniques and Applications, New York: Oxford University Press, 1993, pp. 120-59.
23.Andersen, P. and Petersen, N. C., “A procedure for Ranking Efficient Units in Data Envelopment Analysis,” Management Science, Vol. 39, No. 10, 1993, pp. 1261-1264.
24.Angulo-Meza, L. and Lins, M. P. E., “Review of Methods for Increasing Discrimination in Data Envelopment Analysis,” Annals of Operations Research, Vol. 11, 2002, pp. 225-242.
25.Avkiran, N. K., “Investigating Technical and Scale Efficiencies of Australian Universities through Data Envelopment Analysis,” Socio- Economic Planning Science, Vol. 35, 2001, pp. 57-80.
26.Bagozzi, R. P. and Phillips, L. W., “Representing and Testing Organizational Theories: A Holistic Construal,” Administrative Science Quarterly, Vol. 27, No. 3, 1982, pp. 459-489.
27.Banker, R. D., Charnes, A., and Cooper, W. W., “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis,” Management Science, Vol. 30, No.9, 1984, pp. 1078-1092.
28.Banker, R. D. and A. Maindiratta, “Maximum Likelihood Estimation of Monotone and Concave Production Frontiers,” The Journal of Productivity Analysis, No.3, 1992, pp. 401-415.
29.Banker, R. D. and R. C. Morey, “The Use of Categorical Variables in Data Envelopment Analysis,” Management Science, Vol. 32, No.12, 1986, pp. 1613-1627.
30.Batagelj, V. and Mrvar, A., “Pajek-program for Large Network Analysis,” Connections, Vol. 21, No. 2, 1998, pp. 47–57.
31.Bonacich, P., “Factoring and Weighting Approaches to Status and Clique Identification,” Journal of Mathematical Sociology, Vol. 2, 1972, pp.113-120.
32.Bonacich, P., “Some Unique Properties of Eigenvector Centrality,” Social Networks, Vol. 29, 2007, pp. 555-564.
33.Bonacich, P. and Lloyd, P., “Eigenvector-like Measures of Centrality for Asymmetric Relations,” Social Networks, Vol. 23, 2001, pp. 191-201.
34.Boungnol, M. and Dulá, J. H., “Validating DEA as a Ranking Tool: An Application of DEA to Assess Performance in Higher Education,” Annals of Operations Research, Vol. 145, 2006, pp. 339-365.
35.Breu, T. M. and Raab, R. L., “Efficiency and Perceived Quality of the Nation’s ‘Top 25’ National Universities and National Liberal Arts Colleges: An Application of Data Envelopment Analysis to Higher Education,” Socio-Economic Planning Sciences, Vol. 28, No. 1, 1994, pp.35–45.
36.Brin, S. and Page, L., “The Anatomy of a Large-scale Hypertextual Web Search Engine,” Computer Networks, Vol. 30, 1998, pp. 107-117.
37.Byrnes, P., R. Fare and S. Grosskopf “Measuring productive efficiency: an application to Illinois strip mines,” Management Science, Vol. 30, No.6, 1984, pp.671-681.
38.Chakravarthy, B. S., “Measuring Strategic Performance,” Strategic Management Journal, Vol. 7, 1986, pp. 437-458.
39.Charnes, A., Cooper, W. W., and Rhodes, E., “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, Vol. 2, No.6, 1978, pp. 429-444.
40.Cooper, W. W., Li. S., Seiford, L. M., Tone K., Thrall R., and Zhu J., “Sensitivity and Stability Analysis in DEA: Some Recent Developments,” Journal of Productivity Analysis, Vol. 16, 2000, pp. 31-47.
41.Farrell, M. J., “The Measurement of Productive Efficiency,” Journal of the Royal Statistical Society, Series A, Vol. 120, No. 3, 1957, pp. 253-281.
42.Golany, A. and Roll, Y., “An Application Procedure for DEA,” MAGA, Vol. 17, No.3, 1989, pp. 237-250.
43.Johnes, G. and Johnes, J., “Measuring the Research Performance of UK Economics Departments: An Application of Data Envelopment Analysis,” Oxford： Economic Papers, Vol. 45, 1993, pp. 332-347.
44.Kao, C. and Hung, H. T., “Efficiency Analysis of University Departments: An Empirical Study,” Omega, International Journal of Management Science, Vol. 36, 2008, pp. 653-664.
45.Lo, S. F. and Lu, W. M., “Does Size Matter? Finding the Profitability and Marketability Benchmark of Financial Holding Companies,” Journal of Operational Research, Vol. 23, No. 2, 2006, pp. 229-246.
46.Liu, J. S. and Lu, W. M., “DEA and Ranking with the Network-based Approach: A Case of R&D Performance,” Omega-The International Journal of Management Science, accepted, 2010.
47.Liu, J. S., Yang, C., Lu, W. M., and Chuang, M., “A Network-based Approach for Increasing Discrimination in Data Envelopment Analysis,” Journal of the Operational Research Society, Vol. 60, No. 11, 2009, pp. 1502-1510.
48.Petersen, Donald E., “Beyond Satisfaction,” In Creating Customer Satisfaction, New York: The Conference Board, Research Report No. 944, 1990, pp. 33-34.
49.Sarrico, C. S., Hogan, S. M., Dyson, R. G., and Athanassopoulos, A. D., “Data Envelopment Analysis and University Selection,” Journal of the Operational Research Society, Vol. 48, No. 12, 1997, pp. 1163–1177.
50.Seiford, L. M. and Zhu, J., “Profitability and Marketability of the Top 55 U. S. Commercial Banks,” Management Science, Vol. 45, No. 9, 1999, pp. 1270-1288.
51.Sexton, T. R., “Measuring Efficiency: An Assessment of Data Envelopment Analysis,” Methodology of Data Envelopment Analysis, 1986, pp.7-30.
52.Sherman, H. D., “Hospital Efficiency Measurement and Evaluation: Empirical Test of a New Technique,” Medical Care, Vol. 22, No.10, 1984, pp. 922-935.
53.Thanassoulis, E., Boussofiane, A., and Dyson, R. G., “DEA Models for the Assessment of the Provision of the Provision of Perinatal Care in England on the Basis of Quantity and Quality of Outputs,” Research Paper, 1991, No. 26, pp.1-35.
54.Torgersen, A. M., Forsund, F. R., and Kittelsen, S. A. C., “Slack-adjusted Efficiency Measures and Ranking of Efficient Units,” Journal of Productivity Analysis, Vol. 7, 1996, pp. 379-398.
55.Zhu, J., “Multi-factor Performance Measure Model With an Application to Fortune 500 Companies,” European Journal of Operational Research, Vol. 123, No. 1, 2000, pp. 105-124.