近年來台灣電信業的各項業務用戶數日趨飽和，同業間合併效應產生大者恆大的趨勢，而且政府持續解除法令管制以建立公平競爭環境。由於這些挑戰使得各電信業者面臨更險峻的經營環境與激烈的競爭情勢，因此，如何保有長期競爭力與不斷提升經營效率成為各電信業者最重要的課題。基於此，本研究利用資料包絡分析法(DEA)分析電信服務業者於2001 至2005 年的相對經營效率及生產力變動之情形。
由於電信業者近幾年因合併的結果，造就目前電信三雄之態勢(中華電信、台灣大哥大、遠傳電信)，故本研究為避免在進行效率分析時受評單位數目過少的問題，將使用DEA 視窗分析方法，其原理是同一個受評單位於不同時期則視為不同受評單位互相比較效率。為清楚觀察經營效率因動態時間所造成的影響，本研究則結合DEA 視窗分析方法與Malmquist 生產力指標，藉此衡量視窗Malmquist 生產力指數之值，以評估業者在各期間的生產力變動、效率變動及技術變動之值並分別加以探討。最後再利用無母數檢定法中的Kruskal-Wallis 檢定及趨勢性檢定，探討各業者其生產力變動值在觀察期間是否有所差異，以及是否有顯著成長之趨勢。
研究結果顯示在技術效率方面，台灣大哥大表現最佳；遠傳電信其次；中華電信則相對表現較差。在純技術效率方面，顯示各電信業者在投入資源上皆有高度的有效運用。在規模效率方面，中華電信則相對低落於其他兩家業者。生產力變動分析方面，台灣大哥大為表現最好的公司，中華電信則是相對表現較差。效率變動分析方面，各家電信業者於研究期間之效率變動值呈現微幅低落之情形。技術變動分析方面，則顯示各電信業者於研究期間的生產技術都有進步之情形。無母數檢定結果發現，三家電信業者於研究期間的生產力變動值皆有顯著之差異，且具有遞增或遞減之趨勢。

The growth rate of subscribers of telephone services in Taiwan has gradually shown signs to halt over the past few years owing to the limited market size and continuing deregulation by the government. The consequence of this halt is the declining growth rate of productivity and the prevailing merger among the companies to increase profit margins. Currently, the three largest companies,Chunghwa Telecom (CHT), Taiwan Mobil (TWM), and Far Eastone
Telecommunications (FET), have captured market share up to 80 percents altogether. Despite this majority of market share, how to maintain long-term competitiveness remains a focused issue for companies.
Therefore, this research analyzes the trend of efficiency and productivity change from 2001 to 2005, and discovers important factors leading to the change.To analyze efficiency and productivity, this research uses Data Envelopment Analysis (DEA). Because the data ranges from 2001 to 2005, the first part of the research uses DEA window analysis that is appropriate to observe dynamic efficiency change over the periods and to compare how one decision making unit (DMU) performs in different periods. Since there are only three companies, using window analysis can increase the number of DMUs and thus increases the discriminating power of DEA. The second part of theresearch incorporates window analysis into Malmquist productivity index to measure the technical efficiency change and shift in technology frontier over the periods. Finally, the research uses Kruskal-Wallis rank order test to verify whether the change trend coincides with the result from DEA Malmquist.
According to the computational results regarding to technical efficiency, TWM performs best, followed by FET. Considering the technical efficiency can be decomposed into pure technical efficiency and scale efficiency, the results suggest that all three companies achieve good pure technical efficiency, while the CHT performs relatively poor in scale efficiency. In view of productivity change, TWM is the best, and CHT is the worst. In terms of technical efficiency change, all three companies show the trend of slightly decreasing. In terms of technological change,the results indicate the progress of technology. Using the non-parametric rank tests verifies that the productivity change not only is significantly different, but shows the trend of increasing or decreasing.

中文摘要................... i
英文摘要................... ii
誌謝...................... iv
目錄....................... v
表目錄..................... vii
圖目錄..................... viii
第一章 緒論................. 1
1.1. 研究動機............... 1
1.2. 研究目的............... 2
1.3. 研究範圍............... 3
1.4. 研究架構............... 4
第二章 文獻回顧.............. 6
2.1. 績效評估之目的及意義..... 6
2.2. 效率與生產力的評估方法... 7
2.2.1. 迴歸分析法............ 7
2.2.2. 總要素生產力法........ 8
2.2.3. 隨機前緣法............ 9
2.2.4. 資料包絡分析法........ 10
2.3. 相關文獻回顧............ 14
2.3.1. DEA基礎理論之主要發展.. 14
2.3.2. DEA實務應用之相關文獻.. 17
第三章 研究方法............... 20
3.1. 效率的基本觀念........... 20
3.2. 效率分析之理論模式........ 22
3.2.1. CCR模式............... 22
3.2.2. BCC模式............... 25
3.2.3. DEA視窗分析............ 28
3.3. 生產力變動之理論模式...... 30
3.3.1. Malmquist生產力指數.... 30
3.3.2. 視窗Malmquist生產力指數. 35
第四章 實證分析................ 38
4.1. 台灣主要電信服務業者發展沿革..38
4.2. 研究使用資料及項目之說明.... 40
4.3. 台灣電信服務業效率分析...... 42
4.3.1. 效率分析................. 42
4.3.2. 生產力變動分析........... 46
第五章 結論及建議............... 53
5.1. 實證分析結論............... 53
5.2. 未來研究建議............... 56
參考文獻....................... 58
附錄A. 內文表格................. 65

中文部分
1. 高強、黃旭男、Toshiyuki Sueyoshi，2003，管理績效評估:資料包絡分析法，初版，華泰，台北。
2. 黃旭男，1993，資料包絡分析法使用程序之研究及其在非營利組織效率評估上之應用，國立交通大學，博士論文。
3. 黃亭瑜，2001，行動電話效率分析－資料包絡分析法，東吳大學，碩士論文。
4. 葉萬福，2002，臺灣地區行動電話業競爭效率之研究，國立台灣科技大學，碩士論文。
5. 蔡慶鐘，2003，臺灣電信產業經營績效分析之研究: DEA 與Malmquist 生產力指數之應用，東海大學，碩士論文。
6. 劉柏立、曾俊洲，2002，我國電信統計規劃與電信競爭力分析，交通部電信總局，台北。
7. 盧佳宏，2003，我國民營行動電話服務業者經營績效之研究，國立成功大學，碩士論文。
8. 交通部統計處，2005，http://www.motc.gov.tw。
9. 交通部電信總局，2005，http://www.dgt.gov.tw。
10. 行政院主計處，2005，http://www.dgbas.gov.tw。
11. 台灣經濟研究院產經資料庫，2005，http://tie.tier.org.tw。
12. 台灣證券交易所，2005，http://www.tse.com.tw。
13. 中華電信，2005，http://www.cht.com.tw。
14. 台灣大哥大，2005，http://www.taiwanmobile.com。
15. 遠傳電信，2005，http://www.fetnet.net。
英文部分
1. Aigner, D.J., C. A. K. Lovell and P. Schmidt, (1977), Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6, 21-37.
2. Andersen, P. and Petersen, N. C., (1993), A procedure for ranking efficiency units in data envelopment analysis, Management Science, 39, 1261-1264.
3. Asmild, M., Paradi, J.C., Aggarwall, V., Schaffnit, C. (2004) Combining DEA window analysis with the Malmquist index approach in a study of the canadian banking industry. Journal of Productivity Analysis, 21(1), 67-89.
4. Balk, B. (2001) Scale efficiency and productivity change. Journal of Productivity Analysis, 15(3), 159-183.
5. Banker, R.D., Charnes, A., Cooper, W.W. (1984) Some model for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078-1092.
6. Banker, R.D. and Morey, R., (1986), Efficiency analysis for exogenously fixed inputs and outputs, Operations Research, 34(4), 513-521.
7. Bessent, A., Bessent, W., Kennington, j. and Regan, B., (1982) An application of mathematical programming to assess productivity in the Houston independent school district, Management Science, 28, 1355-1367.
8. Byrnes, P., Färe, R. and Grosskopf, S., (1984) Measuring productive efficiency: An application to Illinois strip mines, Management Science, 30, 671-681.
9. Caves, D., Christensen, L., Diewert, E., (1982) The economic theory of index numbers and the measurement of input, Output and Productivity. Econometrica,50(6), 1393-1414.
10. Charnes, A., Clark, C.T., Cooper, W.W., Golany, B., (1985) A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the
U.S. Air Force. Annals of Operations Research, 2, 95-112.
11. Charnes, A., Cooper, W.W., Rhodes, E. (1978) Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
12. Charnes, A., Cooper, W.W., Seiford, L. M. and Stutz, J., (1983), Invariant multiplicative efficiency and piecewise Cobb-Douglas envelopments, Operations Research Letters, 2, 101-103.
13. Charnes, A., Cooper, W.W., Golany, B. and Seiford, L. (1985), Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production function,Journal of Econometrics, 30, 91-107.
14. Charnes, A., Cooper, W.W., Lewin, A. Y., Morey, R. C. and Rousseau, J., (1985),Sensitivity and stability analysis in DEA, Annals of Operations Research, 2,139-156.
15. Charnes, A., Cooper, W.W., Wei, Q. L. and Huang, Z. M., (1989), Cone ratio data envelopment analysis and multi-objective programming, International Journal of Systems Science, 20, 1099-1118.
16. Chen, Y., Ali, A.I. (2004) DEA Malmquist productivity measure: New insights with an application to computer industry. European Journal of Operational Research,
159(1), 239-249.
17. Coelli, T. J. (1996) A guide to DEAP Version 2.1: A data envelopment analysis (computer) program, CEPA working paper 96/08, Department of Econometrics,University of New England, Armidale.
18. Cook, W. D., Kress, M. and Seiford, L., (1993), On the use of ordinal data in data envelopment analysis, Journal of Operational Research Society, 44, 133-140.
19. Cook, W. D., Kress, M. and Seiford, L., (1996), Data envelopment analysis in the presence of both quantitative and qualitative factors, Journal of Operational Research Society, 47, 945-953.
20. Cooper, W. W., Park, K. S. and Yu, G., (1999), IDEA and AR-IDEA: models for dealing with imprecise data in DEA, Management Science, 45, 597-607.
21. Cooper, W.W., Seiford, L.M., Tone, K. (2000) Data envelopment analysis – A comprehensive text with models, applications, references and DEA-solver software. Kluwer Academic Publishers, Norwell, Massachusetts.
22. Cooper, W.W., Park, K.S., Yu, G.. (2001) An illustrative application of IDEA (Imprecise Data Envelopment Analysis) to a Korean mobile telecommunication
company. Operations Research, 49(6), 807-820.
23. Despotis, D. K. and Smirlis, Y. G., (2002), Data envelopment analysis with imprecise data, European Journal of Operational Research, 140, 24-36.
24. Doyle, J. and Green, R., (1994), Efficiency and cross-efficiency in DEA: derivations, meanings and uses, Journal of the Operational Research Society, 45, 567-578.
25. Dyson, R. G. and Thanassoulis, E., (1988), Reducing weight flexibility in data envelopment analysis, Journal of the Operational Research Society, 39, 563-576.
26. Dyson, R. G., R., Allen, A.S., Camanho, V.V., Podinovski, C.S., Sarrico and E.A., Shale, (2001), Pitfalls and protocols in DEA, European Journal of Operational
Research, 132, 245-259.
27. Färe, R., Grosskopf, S., Lindgren, B., Roos, P., (1992), Productivity changes in Swedish pharmacies 1980-1989: A non-parametric Malmquist approach, Journal of
Production Analysis, 3, 85-101.
28. Färe, R., Grosskopf, S. and Lovell, C. A. K., (1994), Production frontiers,Cambridge: Cambridge University Press.
29. Färe, R., Grosskopf, S. and Njinkeu, D., (1988), On piecewise reference technologies, Management Science, 34, 1507-1511.
30. Farrell, M. J., (1957), The measurement of productive efficiency, Journal of the Royal Statistical Society, Series A, 120, Part 3, 253-281.
31. Farrell, M. J. and Fieldhouse, M., (1962), Estimating efficient production under increasing returns to scale, Journal of the Royal Statistical Society, Series A, 125,
Part 2, 252-267.
32. Façanha, L.O., Resende, M. (2004) Price cap regulation, incentives and quality: the case of Brazilian telecommunications. International Journal of Production
Economics, 92(2), 133-144.
33. Golany, J. A. and Y., Roll, (1989), An application procedure for DEA, OMEGA: International Journal of Management Science, 17, 237-250.
34. Grosskopf, S., (2003), Some remarks on productivity and its decompositions.Journal of Productivity Analysis, 20(3), 459-474.
35. Hsiao Frank S.T., Park, C. (2005) Korean and Taiwanese productivity performance: comparisons at matched manufacturing levels. Journal of Productivity Analysis,
23(1), 85-107.
36. Kao, C., (1994), Efficiency improvement in data envelopment analysis, European Journal of Operational Research, 73, 487-497.
37. Odeck, J. (2000) Assessing the relative efficiency and productivity growth of vehicle inspection services: an application of DEA and Malmquist indices. European Journal of Operational Research, 126(3), 501-514.
38. Peterson, N. C., (1990) Data envelopment analysis on a relaxed set of assumptions,Management Science, 36, 305-314.
39. Retzlaff-Roberts, D. L., (1996), Relating discriminate analysis and DEA to one another, Computers and Operations Research, 23, 311-322.
40. Roll, Y., Cook, W. D., and Golany, B., (1991), Controlling factor weights in data envelopment analysis, IIE Transactions, 23, 2-9.
41. Ross, A., Droge C., (2002) An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management,20(1), 19-32.
42. Sengupta, J. K., (1987) Data envelopment analysis for efficiency measurement in the stochastic case, Computers and Operations Research, 14, 117-129.
43. Shephard, R.W. (1970) Theory of cost and production functions, Princeton University Press, Princeton, NJ.
44. Sueyoshi, T., (1997) Measuring scale efficiencies and returns to scale of Nippon telegraph and telephone and telephone in production and cost analysis, Management
Science, 43, 779-796.
45. Sueyoshi, T., Aoki, S. (2001) A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test, OMEGA, 29(1), 1-18.
46. Sueyoshi, T., Goto, M.S. (2001) Slack adjusted DEA for time series analysis:performance measurement of Japanese electric power generation industry in 1984-1993, European Journal of Operational Research, 133(2), 232-259.
47. Thompson, R. G., Langemeier, L. N., Lee, E. And Thrall, R. M., (1990), The role of multiplier bounds in efficiency analysis with application to Kansas farming, Journal of Econometrics, 46, 93-108.
48. Thompson, R. G., Singleton, F. D., Jr., Thrall, R. M. and Smith, B. A., (1986),Comparative site evaluations for locating a high-energy physics lab in Texas,Interfaces, 16(6), 35-49.
49. Wong, Y. B. and Beasley, J. E., (1990), Restricting weight flexibility in data envelopment analysis, Journal of the Operational Research Society, 41, 829-835.
50. Zhu, J. (2004) Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Operations Research, 52(2), 323-329.