Efficiency of US airlines: A strategic operating model

Journal of Air Transport Management

Volumn 43, Issue , 2015, Pages 46-56

  Mallikarjun, Sreekanth ^a  

^a USA   (United States)

Author keywords

Network Data Envelopment Analysis; Operating efficiency; US airlines

Indexed keywords

AIR TRANSPORTATION; DATA ENVELOPMENT ANALYSIS; DEMAND ANALYSIS; MODELING; STRATEGIC APPROACH;

UNITED STATES;

EID: 84922424157     PISSN: 09696997     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jairtraman.2014.12.004     Document Type: Article

References (80)

1. Abdullah M.A., Munisamy S., Satar N.M. ACritical Review of Literature on Performance of Airlines 2013.
2. Airlines for America U.S. Airlines Deliver Record-setting Year for Operational Performance in Key Service Areas 2013, National Airline Policy, Washington DC.
3. Alvarez R., Crespi G. Determinants of technical efficiency in small firms. Small Bus. Econ. 2003, 20:233-244.
4. Arjomandi A., Seufert J.H. An evaluation of the world's major airlines' technical and environmental performance. Econ. Model. 2014, 41:133-144.
5. Assaf A. Are U.S. airlines really in crisis?. Tour. Manag. 2009, 30:916-921.
6. Assaf A.G., Josiassen A. European vs. US airlines: performance comparison in a dynamic market. Tour. Manag. 2012, 33:317-326.
7. Baltagi B.H., Griffin J.M., Rich D.P. Airline deregulation: the cost pieces of the puzzle. Int. Econ. Rev. 1995, 245-258.
8. Banker R.D., Johnston H.H. Evaluating the Impacts of Operating Strategies on Efficiency in the US Airline Industry, Data Envelopment Analysis: Theory, Methodology, and Applications 1994, 97-128. Springer.
9. Barbot C., Costa, Sochirca E. Airlines performance in the new market context: a comparative productivity and efficiency analysis. J.Air Transp. Manag. 2008, 14:270-274.
10. Barros C.P., Couto E. Productivity analysis of European airlines, 2000-2011. J.Air Transp. Manag. 2013, 31:11-13.
11. Barros C.P., Liang Q.B., Peypoch N. The technical efficiency of US airlines. Transp. Res. Part A Policy Pract. 2013, 50:139-148.
12. Barros C.P., Peypoch N. An evaluation of European airlines' operational performance. Int. J. Prod. Econ. 2009, 122:525-533.
13. Bauer W. Decomposing TFP growth in presence of cost inefficiencies, non-constant returns to scale and technological progress. J.Prod. Anal. 1990, 1:287-299.
14. Behn B.K., Riley R.A. Using nonfinancial information to predict financial performance: the case of the US airline industry. J.Account. Audit. Finance 1999, 14:29-56.
15. Bhadra D. Race to the bottom or swimming upstream: performance analysis of US airlines. J.Air Transp. Manag. 2009, 15:227-235.
16. Bureau of Transportation Statistics Glossary 2013, Research and Innovative Technology Administration.
17. Bureau of Transportation Statistics T-100 Market Data 2013, Research and Innovative Technology Administration, Washington DC.
18. Capobianco H.M.P., Fernandes E. Capital structure in the world airline industry. Transp. Res. Part A Policy Pract. 2004, 38:421-434.
19. Caves D.W., Christensen L.R., Tretheway M.W. Economies of density versus economies of scale: why trunk and local service airline costs differ. Rand J. Econ. 1984, 471-489.
20. Cento A. The Airline Industry: Challenges in the 21st Century 2009, Springer, Italy.
21. Chang Y.-T., Park H.-S., Jeong J.-b., Lee J.-w Evaluating economic and environmental efficiency of global airlines: a SBM-DEA approach. Transp. Res. Part D Transp. Environ. 2014, 27:46-50.
22. Charnes A., Cooper W.W., Rhodes E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 1978, 2:429-444.
23. Charnes F., Cooper W.W., Lewin A.Y., Seiford L.M. Data Envelopment Analysis: Theory, Methodology, and Application 1994, Kluwer Academic.
24. Chiou Y.-C., Chen Y.-H. Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transp. Res. Part E Logist. Transp. Rev. 2006, 42:116-127.
25. Choi K., Lee D., Olson D.L. Service quality and productivity in the US airline industry: a service quality-adjusted DEA model. Serv. Bus. 2013, 1-24.
26. Coelli T., Perelman S., Romano E. Accounting for environmental influences in stochastic frontier models: with application to international airlines. J.Prod. Anal. 1999, 11:251-273.
27. Cook W.D., Tone K., Zhu J. Data envelopment analysis: prior to choosing a model. Omega 2014, 44:1-4.
28. Cornwell C., Schmidt P., Sickles R.C. Production frontiers with cross-sectional and time-series variation in efficiency levels. J.Econ. 1990, 46:185-200.
29. Distexhe V., Perelman S. Technical efficiency and productivity growth in an era of deregulation: the case of airlines. Swiss J. Econ. Stat. 1994, 130:669-689.
30. Ehrlich I., Gallais-Hamonno G., Liu Z., Lutter R. Productivity growth and firm ownership: an analytical and empirical investigation. J.Polit. Econ. 1994, 1006-1038.
31. Fan T.P.C., Tan A.T.L., Geng X. Rapid capacity expansions and failure: a trap for new airline entrants?. Transp. Res. Part E Logist. Transp. Rev. 2014, 61:176-191.
32. Färe R., Grosskopf S. Network DEA. Socio-Econ. Plan. Sci. 2000, 34:35-49.
33. Finch J.H., Becherer R.C., Casavant R. An option-based approach for pricing perishable services assets. J.Serv. Mark. 1998, 12:473-483.
34. Forsyth P.J., Hill R., Trengove C. Measuring airline efficiency. Fisc. Stud. 1986, 7:61-81.
35. Fried H.O., Schmidt S.S., Yaisawarng S. Incorporating the operating environment into a nonparametric measure of technical efficiency. J.Prod. Anal. 1999, 12:249-267.
36. Gillen D.W., Oum T.H., Tretheway M.W. Airline cost structure and policy implications: a multi-product approach for Canadian airlines. J.Transp. Econ. Policy 1990, 9-34.
37. Good D.H., Nadiri M.I., Röller L.-H., Sickles R.C. Efficiency and Productivity Growth Comparisons of European and US Air Carriers: a First Look at the Data 1993, Springer.
38. Good D.H., Roller L.H., Sickles R.C. Airline efficiency differences between Europe and the United-States - implications for the pace of EC integration and domestic regulation. Eur. J. Oper. Res. 1995, 80:508-518.
39. Greer M. Is it the labor unions' fault?: dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States. Transp. Res. Part A Policy Pract. 2009, 43:779-789.
40. Greer M.R. Nothing focuses the mind on productivity quite like the fear of liquidation: changes in airline productivity in the United States, 2000-2004. Transp. Res. Part A Policy Pract. 2008, 42:414-426.
41. Grosche T. Computational Intelligence in Integrating Airline Scheduling 2009, Springer.
42. Guiliano G. The Effect of Environmental Factors on the Efficiency of Public Transit Service 1980, Institute of Transportation Studies, University of California, Irvine, California.
43. Heimlich J., Elwell D. U.S. Airline Industry Financial Results and Operations: an Early 2013 Review 2013, Airlines for America.
44. Hirst M. The Air Transport System 2008, Library of Flight, Virginia.
45. Hong S., Zhang A. An efficiency study of airlines and air cargo/passenger divisions: a DEA approach. World Rev. Intermodal Transp. Res. 2010, 3:137-149.
46. Inglada V., Rey B., Rodríguez-Alvarez A., Coto-Millan P. Liberalisation and efficiency in international air transport. Transp. Res. Part A Policy Pract. 2006, 40:95-105.
47. Khanna T., Palepu K. Is group affiliation profitable in emerging markets? An analysis of diversified Indian business groups. J.Financ. 2000, 55:867-891.
48. Kimes S.E. Yield management: a tool for capacity-considered service firms. J.Oper. Manag. 1989, 8:348-363.
49. Lee B.L., Worthington A.C. Technical efficiency of mainstream airlines and low-cost carriers: new evidence using bootstrap data envelopment analysis truncated regression. J.Air Transp. Manag. 2014, 38:15-20.
50. Lewis H.F., Mallikarjun S., Sexton T.R. Unoriented two-stage DEA: the case of the oscillating intermediate products. Eur. J. Oper. Res. 2013, 229:529-539.
51. Lewis H.F., Sexton T.R. Network DEA: efficiency analysis of organizations with complex internal structure. Comput. Oper. Res. 2004, 31:1365-1410.
52. Lu W.-M., Hung S.-W., Kweh Q.L., Wang W.-K., Lu E.-T. Production and marketing efficiencies of the U.S. airline industry: a two-stage network DEA approach. Data Envelopment Analysis 2014, 537-567. Springer, New York. W.D. Cook, J. Zhu (Eds.).
53. Mallikarjun S., Lewis H.F., Sexton T.R. Operational performance of US public rail transit and implications for public policy. Socio-Econ. Plan. Sci. 2014, 48:74-88.
54. Mann H.B., Whitney D.R. On a test of whether one of 2 random variables is stochastically larger than the other. Ann. Math. Stat. 1947, 18:50-60.
55. Merkert R., Hensher D.A. The impact of strategic management and fleet planning on airline efficiency-a random effects Tobit model based on DEA efficiency scores. Transp. Res. Part A Policy Pract. 2011, 45:686-695.
56. MIT Airline Industry Overview 2013, MIT Global Airline Industry Program, Massachusetts.
57. Norton R.N., Sexton T.R., Silkman R. Accounting for site characteristics in DEA: leveling the playing field. Int. Trans. Oper. Res. 2007, 14:231-244.
58. Ott J., Neidl R. Airline Odyssey: the Airline Industry's Turbulent Flight into the Future 1995, McGraw-Hill, Inc., New York.
59. Ouellette P., Petit P., Tessier-Parent L.-P., Vigeant S. Introducing regulation in the measurement of efficiency, with an application to the Canadian air carriers industry. Eur. J. Oper. Res. 2010, 200:216-226.
60. Oum T.H., Yu C. Winning Airlines: Productivity and Cost Competitiveness of the World's Major Airlines 1998, 6.
61. Pearson J., Merkert R. Airlines-within-airlines: a business model moving East. J.Air Transp. Manag. 2014, 38:21-26.
62. Pitfield D.E., Caves R.E., Quddus M.A. Airline strategies for aircraft size and airline frequency with changing demand and competition: a simultaneous-equations approach for traffic on the north Atlantic. J.Air Transp. Manag. 2010, 16:151-158.
63. Ray S.C., Mukherjee K. Decomposition of the Fisher ideal index of productivity: a non-parametric dual analysis of US airlines data. Econ. J. 1996, 1659-1678.
64. Sarkis J. Preparing Your Data for DEA, Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis 2007, 305-320. Springer.
65. Schefczyk M. Operational performance of airlines: an extension of traditional measurement paradigms. Strateg. Manag. J. 1993, 14:301-317.
66. Scheraga C.A. The relationship between operational efficiency and customer service: a global study of thirty-eight large international airlines. Transp. J. 2004, 43:48-58.
67. Schmidt P., Sickles R.C. Production frontiers and panel data. J.Bus. Econ. Stat. 1984, 2:367-374.
68. Semenick Alam I.M., Sickles R.C. Time series analysis of deregulatory dynamics and technical efficiency: the case of the US airline industry. Int. Econ. Rev. 2000, 41:203-218.
69. Sengupta J.K. Adynamic efficiency model using data envelopment analysis. Int. J. Prod. Econ. 1999, 62:209-218.
70. Sickles R.C., Good D., Johnson R.L. Allocative distortions and the regulatory transition of the US airline industry. J.Econ. 1986, 33:143-163.
71. Sickles R.C., Good D.H., Getachew L. Specification of distance functions using semi- and nonparametric methods with an application to the dynamic performance of Eastern and Western European air carriers. J.Prod. Anal. 2002, 17:133-155.
72. Tavassoli M., Faramarzi G.R., Farzipoor Saen R. Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input. J.Air Transp. Manag. 2014, 34:146-153.
73. Tone K., Tsutsui M. Network DEA: a slacks-based measure approach. Eur. J. Oper. Res. 2009, 197:243-252.
74. Tone K., Tsutsui M. Dynamic DEA: a slacks-based measure approach. Omega-Int. J. Manage. S 2010, 38:145-156.
75. Tsai W.-H., Kuo L. Operating costs and capacity in the airline industry. J.Air Transp. Manag. 2004, 10:269-275.
76. Tsikriktsis N., Heineke J. The impact of process variation on customer dissatisfaction: evidence from the U.S. domestic airline industry. Decis. Sci. 2004, 35:129-141.
77. Wilson J.G., Anderson C.K., Kim S.W. Optimal booking limits in the presence of strategic consumer behavior. Int. Trans. Oper. Res. 2006, 13:99-110.
78. Wu W.-Y., Liao Y.-K. Abalanced scorecard envelopment approach to assess airlines' performance. Indust. Manag. Data Syst. 2014, 114:123-143.
79. Zhu J. Super-efficiency and DEA sensitivity analysis. Eur. J. Oper. Res. 2001, 129:443-455.
80. Zhu J. Airlines performance via two-stage network DEA approach. J.CENTRUM Cathedra 2011, 4:260-269.