臺灣博碩士論文加值系統

本研究的目的在於提出二階段模式，並運用資料包絡分析法（Data Envelopment Analysis, DEA）評估世界主要32家國際航空公司1998-2000年營運績效，並比較我國航空公司與其他國家的差異。第一階段選定5個投入項（營運支出、員工人數、飛航班次、可售座位公里與可售噸公里）及4個產出項（載客人數、載客公里、載運噸公里與營運收入），以評估整體營運績效。第二階段評估國際航空公司的生產與銷售效率，並探討二者間的關係。本研究使用確定區間模式、非控制變數模式、交叉效率、麥氏指標與迴歸分析進行研究。研究發現：（1）在確定區間模式下，只有伊朗航空（1998）具有總體營運績效；在非控制變數模式下，有18家樣本公司具有營運績效；（2）4家處於規模報酬遞增，1家處於固定規模報酬，68家均達規模報酬遞減（3）多數的航空公司都不具有生產及銷售效率；（4）依地區別績效分析，美洲地區有達美航空（1998）及美國航空（1998），亞太地區有伊朗航空（1998），歐洲地區有愛爾蘭雷恩航空（2000）具有總體績效；（5）我國長榮航空營運績效最佳；（6）1998-2000年生產力趨勢平均呈現正成長；（7）內生變數對營運績效據低度影響力。

The purpose of this paper is to propose a two-stage evaluation model for assessing the operation performance of a sample of 32 international airlines over 1998-2000 and making performance comparisons between Taiwanese airlines and other countries’ airlines, using data envelopment analysis (DEA). In the first stage, five inputs (operating expenditures, labor, number of flight, seat-kilometers available, and cargo-ton kilometers) and four outputs (number of passengers, seat-kilometers, cargo-kilometers, operation revenue) were used for overall operation performance measure. In the second stage, productive and selling efficiencies of these airlines were examined. To sharpen our analysis, we used various DEA models and regression analysis. The non-controlled (NCN) model was used to deal with the uncontrollable output of number of passengers; the assurance region (AR) model was employed to take into account of imposing weights restrictions on the inputs and outputs variable; cross efficiency measure is utilized to identify the best performance airline; Malmquist Index was employed to examine the productivity growth of these airlines; finally, regression analysis is used to investigate the impacts of business characteristics on the operation performance. The results of this study show that (1) Iran Air (1998) achieved overall efficiency under the AR model; 18 airlines were rated overall efficiency under NCN model; (2) under the AR model, in general, 4 sample airlines experienced increasing returns to scale, 1 sample airlines experienced constant returns to scale and other 68 sample airlines experienced decreasing returns to scale; (3) the airline that achieved high productive efficiency does not mean it would achieve high selling efficiency; (4) airlines perfored well in different regions are: Delta Airlines and American Airlines in America, Iran Air in Asia, Ryan air in Europe; (5) Eva was the best performance among international airlines; (6) the productivity growth of these airlines over 1998-2000 was increasing; and (7) business characteristics have low impacts on airlines operation performance.

頁次
中文摘要…………………………………………………. Ⅰ
英文摘要………………………………………………….. Ⅱ
誌謝………………………………………………………………… Ⅲ
目錄………………………………………………………………… Ⅳ
表目錄……………………………………………………………… Ⅶ
圖目錄……………………………………………………………… Ⅸ
第一章緒論………………………………………………………… 1
1.1研究動機……………………………………………………… 1
1.2研究目的與問題……………………………………………… 1
1.3研究方法與步驟……………………………………………… 3
1.4研究範圍與限制……………………………………………… 5
1.5研究內容……………………………………………………… 5
第二章文獻探討…………………………………………………… 6
2.1非DEA應用文獻……….…………………………………… 6
2.2 DEA應用文獻……………………………………………… 18
2.3 DEA理論文獻……………………………………………… 27
第三章研究方法…………………………………………………… 40
3.1研究流程……………………………………………………… 40
3.2 研究設計……………………………………………………. 42
3.2.1 績效觀念評估模式….…………………………………... 42
3.2.2 決策單位選擇………………………………….......... 47
3.2.3定義與選擇變數…………………………………………. 49
3.2.4分析模式…………………………………………………. 52
第四章實證分析…………………………………………………… 54
4.1一階段模式分析..……………………………………………. 54
4.1.1效率分析.………………………………..………………. 54
4.1.2參考群體分析………………………………………….. 60
4.1.3差額變數分析…………………………………………… 60
4.1.4目標改善分析……………………………………………. 64
4.2二階段模式分析……………………………………………… 67
4.3地區別績效分析……………………………………………… 71
4.4生產力指標趨勢分析.……………………………………….. 82
4.5迴歸分析……………………………………………………… 94
第五章結論與建議.…..………………………………………… 96
5.1結論………….………..……………………………………….96
5.2建議………….………………………………………….……. 99
參考文獻………………….…………………………………………100
一、中文文獻…………………………………………………….. 100
二、英文文獻…………………………………………………….. 101
表 目 錄
頁次
表2.1非DEA應用文獻整理表…….……………………………… 11
表2.2非DEA評估方法表…….…………………………………… 17
表2.3應用DEA評估航空公司文獻摘要表……………………… 22
表2.4資料包絡分析法適用性說明表…………….……………… 25
表2.5 DEA模式使用表……………………………………………. 38
表3.1文獻使用變數歸納表……….……………………………… 42
表3.2不同區域評估模式整理表…………………………….…… 46
表3.3研究對象表…………………….……………………………..48
表3.4變數定義表…..………………………………………………49
表3.5變數敘述統計表………..……………………………………50
表3.6變數相關係數表.……………………..………………………51
表4.1總體效率值表..………………………………………………58
表4.2差額變數表……………………………………………………61
表4.3改善目標表……………………………………………………65
表4.4生產/銷售效率值表…………………………………………...68
表4.5美洲地區效率值表..…………………………………………73
表4.6美洲地區與我國效率值表……………………………………75
表4.7亞太地區效率值表………………………………………… 77
表4.8歐洲地區效率值表……………………………………………79
表4.9歐洲地區與我國效率值表……………………………………81
表4.10 1998年為基準生產力指標變動值…………………………83
表4.11 1999年為基準生產力指標變動值…………………………87
表4.12 1998~2000年為基準生產力指標變動值…………………..91
表4.13迴歸分析表………………………………………………… 95
圖 目 錄
頁次
圖1.1研究步驟圖……………………………………………….. 4
圖2.1交叉效率矩陣圖…………………………………….……. 34
圖3.1研究流程圖……………………………………………….…..41
圖3.2變數影響圖……………………………………………………44
圖3.3一階段績效觀念模式圖……………………………….……..45
圖3.4二階段績效觀念模圖…………………………………….…..46
圖4.1生產效率與銷售效率關係圖…………………………….…..71
圖4.2 1998年技術效率變動圖…………………………………… 84
圖4.3 1998年技術變革變動圖…………………………………… 84
圖4.4 1998年純技術效率變動圖………………………………… 85
圖4.5 1998年規模效率變動圖…………………………………… 85
圖4.6 1998年總效率變動圖……………………………………… 86
圖4.7 1999年技術效率變動圖…………………………………… 88
圖4.8 1999年技術變革變動圖…………………………………… 88
圖4.9 1999年純技術效率變動圖………………………………… 89
圖4.10 1999年規模效率變動圖…………………………………..89
圖4.11 1999年總效率變動圖………………………………. 90
圖4.12 1998-2000年技術效率變動圖…………………………….92
圖4.13 1998-2000年技術變革變動圖…………………………….92
圖4.14 1998-2000年純技術效率變動圖………………………….93
圖4.15 1998-2000年規模效率變動圖…………………………….93
圖4.16 1998-2000年總效率變動圖……………………………….94

(1)交通部民航局（民八八），「八十七年民航統計年報」，民航局會計室編印，台北市。
(2)交通部民航局（民八九），「八十八年民航統計年報」，民航局會計室編印，台北市。
(3)交通部民航局（民九0），「八十九年民航統計年報」，民航局會計室編印，台北市。
(4)林延儒（民八二），「台灣地區航空公司生產效率之評估─DEA彈性函數方法之應用」，國立清華大學經濟學研究所碩士論文。
(5)陳俊男（民八八），「國內航空公司營運績效之研究」，國立交通大學交通運輸所碩士論文。
(6)陳敦基、蕭智文（民八三），「公路客運業總體績效DEA評估模式建立之研究」，運輸計畫季刊，第二十三卷，第一期， 11-40頁。
(7)馮正民、往榮祖（民九十），「應用灰色關聯分析於航空運輸業營運與財務績效代表性指標之擷取」，民航季刊，第三卷，第一期， 109~126頁。
(8)黃崇興、黃蘭貴（民八九），「應用數據包絡法於航空公司航線經營績效之分析」，管理學報，第十七卷，第一期，頁149-181。
(9)黃蘭貴（民八三），「企業內部經營績效診斷之研究--數據資料包絡網路分析模式之應用」，國立台灣大學商學研究所碩士論文。
(10)葉清江（民八三），「國內線航空公司營運效率之研究」，國立交通大學管理經營科學研究所碩士論文。
二、英文文獻
(1)Adler, N. and Golany, B.,（2001）,“Evaluation of Deregulated Airline Networks Using Data Envelopment Analysis Combined with Principal Component Anyalysis with An Application to Western Europe”,European Journal of Operational Research,Vol. 132,pp. 260-273.
(2)Alam, I.M.S., and Sickles, R.C.（1998）,“The Relationship Between Stock Market Returns and Technical Efficiency Innovations： Evidence from The US Airline Industry”, Journal of Producitivity Analysis, Vol. 9, pp. 35-51.
(3)Al-Jazzaf, M.I. （1999）,“Impact of Privatixation on Airlines Performance: An Empirical Analysis”, Journal of Air Transport Management,Vol. 5, pp. 45-52.
(4)Banker, R.D. (1993), “Maximum Likelihood, Consisrtency and Data Envelopment Analysis: A Statistical Foundation”, Management Science, Vol. 39, No. 10, pp. 1265-1273.
(5)Banker, R.D. and Morey, R. (1986), “The Use of Categorical Variables in Data Envelopment Analysis” Management Science, Vol.32, No.10, pp. 1613-1627.
(6)Banker, R.D. and Thrall, R.M.（1992）, “Estimating Most Productive Scale Size Using Data Envelopment Analysis” European Journal of Operational Research, Vol. 62, pp. 74-84.
(7)Banker, R.D., Charnes, A., and Cooper, W.W. (1984), “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis.”, Management Science, Vol. 30, No.9, pp. 1078-1092.
(8)Borger, B.D. and Kerstens, K. (1996),“Cost Efficiency of Belgian Local Governments: A Comparative Analysis of FDH, DEA, and Econometric Approaches”, Regional Science and Urban Economics, Vol. 26, pp. 145-170.
Bowlin, W.F.（1987）,“Evaluating the Efficiency of U.S. Air Force Real-Property Maintenance Activities.”, European Journal of Opertional Reaserch, Vol. 38, pp.127-135.
(9)Caves, D.W., Christensen, L.R., and Diewert , W.E.（1982）, “The Economic Theory of Index Number of the Measurement of Input , Output, and Productivity”, Econometrica, Vol. 50, No. 6, pp. 1393-1414.
(10)Ceha, R. and Ohta, H. (2000),“Productivity Change Model in Airline Industry:A Parametric Approach”, European Journal of Operational Research, Vol. 121, pp. 641-655.
(11)Centre for Efficiency and Productivity Analysis , University of New England (1997), DEAP2.1（http://www.une.edu.au/econometrics/deap.htm）.
(12)Chang, K.P. (1999),“Measuring Efficiency with Quasiconcave Production Frontiers”, European Journal of Operational Research,Vol. 115,pp. 487-506.
(13)Chang, Y.H. and Yeh, C.H. (2001),“Evaluating Airline Competitiveness Using Multiattribute Decision Marking”, Omega, Vol. 29,pp. 405-415.
(14)Charnes, A., Cooper, W.W., and Rhodes, E. (1978), “Measuring the Efficiency of Decision Making Units” , European Journal of Operational Research, Vol. 2, pp. 429-444.
Charnes, A., Cooper, W.W., and Seiford, L. (1983), “Invariant (15)Multiplicative Efficiency and Piecewise Cobb-Douglas Envelopments”, Operations Research Letters, Vol. 2, No. 3, pp. 101-103.
(16)Charnes, A., Gallegoes, A., and Li, H. （1996）, “Robustly Efficient Parametric Fronties via Multiplicative DEA for Domestric and Interational Operations of The Latin American Airline Industry” , European Journal of Operations Research, Vol. 88, pp. 525-536.
(17)Coelli, T., Perelman, S. and Romano, E.（1999）,“Accounting for Environmental Influences in Stockhastic Frontier Models： with Application to International Airlines”, Journal of Producitivity Analysis, Vol. 11, pp. 251-273.
(18)Cooper W. W., Seiford, L.M., and Tone, K.（2000）, Data Envelopment Analysis－A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Kluewr Academic Publisher, U.S.A.
(19)Deprins D., Simar, L., and Tulkens, H.,（1984）,“Measuring Labor Efficiency in Post Offices” in M. Marchand, P. Pestieau and H. Tulkens (eds.), The Performance of Public Enterprise: Concept and Measurement. North Holland, Amsterdam, 1984, pp. 243-267.
(20)Doyle, J. and Green, R.(1994), “Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses” Journal of Operational Research Society, Vol.45, No.5, pp.567-578.
(21)Fare, R. S., Lindgren , B. and Roos, P.（1994）, “Productivity Development in Swedish Hospitals: A Malmquist Output Index Approach” in Charnes, W.W., Cooper, A. Y. L. and Seiford (eds), L.M., Data Envelopment analysis: Theory, Methodology and Applications. Boston, Mass : Kluwer Academic Publishers, Boston, pp. 253-272.
(22)Farrell, M.J. (1957),“The Measurement of Productive Efficiency.”, Journal of Royal Statistical Society－Series A, Vol. 120, No. 3, pp. 253-290.
(23)Feng, C.M., and Wang, R.T. (2000), “Performance Evaluation for Airlines Including the Consideration of Financial Ratios.”, Journal of Air Transport Management, Vol. 6, pp. 133-142.
(24)Fielding, G.J., Glauthier, R.E.,and Lave, C.A., （1978）,“Performance Indication for Transit Management”, Transportation, Vol. 7,No. 4, pp. 365-379.
(25)Forsyth, P. (2001), “Total Factor Productivity in Australian Domestic Aviation”, Transport Policy, Vol. 8, pp. 201-207.
(26)Good, D.H., Roller, L.H., and Sickles, R.C.（1995）, “Airline Efficiency Differences between Europe and the US: Implications for the Pace of EC Integration and Domestic Regulation”, European Journal of Operational Research, Vol.80, pp. 508-518
(27)Hansen, M. M. Gillen, D., and Djafarian-Tehrani, R. (2001),“Aviation Infrastructure Performance and Airline Cost: A Statistical Cost Estimation Approach”, Transportation Research, Part E Vol. 37, pp. 1-23.
(28)International Air Transport Association（1999）, World Air Transport Statistics, Number 43.
(29)International Air Transport Association（2000）, World Air Transport Statistics, Number 44.
(30)International Air Transport Association（2001）, World Air Transport Statistics, Number 45.
(31)Lovell, C.A.K., and Pastor, J.T （1999）, “Radial DEA models without Input or Output”, Eurpean Journal of Operation Research, Vol. 118, pp. 46-51.
(32)Nishimizu, M., and Page, J.M. （1982）, “Total Factor Producitivity Growth, Technological Progress and Technical Efficiency Chang: Dimenisions of Productivity Chang in Yagoslavia”, The Economic Journal, pp. 920-936.
(33)Ray, C.S., and Hu, X.（1997）, “On the Technically Efficient Organization of an Industry：A Study of U.S. Airlines”, Journal of Productivity Analysis, Vol. 8, pp. 5-18.
(34)Roll, Y., Golany, B., and Seroussy, D.（1989）, “Measureing the Efficiency of Maintance Unites in Isvaeli Airforce”, Vol. 43, No. 2, pp. 136-142.
(35)Saitech-Inc. (2000), DEA-Solver Software Professional Version 2.0, New jersey USA.
(36)Schefczyk, M. (1993), “Operation Performance of Airlines: An Extension of Traditional Measurement Paradigms”, Straegic Management Journal,Vol. 14, pp. 301-317.
Sengupta, J.K. (1999), “A Dynamic Efficiency Model Using Data Envelopment Analysis”, Internationl Journal Production Economics,Vol. 62, pp. 209-218.
(37)Sexton, T.R., Leiken, A.M., Nolan, A.H., Liss S., Hogan, A. and Silkman, R.H. (1989), “Evaluating Managerial Efficiency of Veterans Administration Medical Centers Using Data Envelopment Analysis.”Medical Care, Vol.27,No.12, pp.1175-1188.
(38)Thomppson, R.G., Singleton, F.D., Thrall, R.M., and Smith, B.A. (1986), “Comparative Site Evaluations for Locating High Energy Lab in Texas” Interfaces, Vol.16, pp.1380-1395.
(39)Tofallis, C.（1997）, “Input Efficiency Profiling: An Application to Airlines”, Computers and Opertions Research,Vol. 24,No. 3, pp. 253-258.
(40)Tone, K., (1993) Data Envelopment analysis (in Japan), JUSE Press, Ltd publisher, Tokyo.
(41)Tulkens, H.（1993）,“On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts, and Urban Transit”, The Journal of Productivity Analysis, Vol. 4, pp. 183-210.