Production and Marketing Efficiencies of the U.S. Airline Industry: A Two-Stage Network DEA Approach

  • Wen-Min Lu
  • Shiu-Wan Hung
  • Qian Long Kweh
  • Wei-Kang Wang
  • En-Tzu Lu
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 208)

Abstract

This chapter presents an application of a two-stage network data envelopment analysis (DEA) for examining the performance of 30 U.S. airline companies. The airline industry is a subject of concern because the industry is a major contributor to a country’s or even global economic development. Although a number of studies have explored airline performance using DEA, relatively few studies have applied a two-stage DEA model. The current chapter examines production efficiency and marketing efficiency through an additive two-stage network DEA model. This approach allows the black-box of the performance measurement process to be assessed, thus, providing a new direction in measuring airline performance. The chapter includes a managerial decision-making matrix and makes suggestions to help airline managers improve performance for airlines. In addition, a regression analysis of the effect of corporate governance mechanisms on airlines performance is conducted. Given the volatility of growth in the airline industry, it is expected that we will see more research related to performance management in the industry.

Keywords

Two-stage data envelopment analysis (DEA) Truncated regression Corporate governance Managerial decision-making matrix 

ReferencesÎ

  1. Alam, I., Semenick, M., & Sickles, C. (1998). The relationship between stock market returns and technical efficiency innovations: Evidence from the US airline industry. Journal of Productivity Analysis, 9(1), 35–51.CrossRefGoogle Scholar
  2. Andres, P. D., & Vallelado, E. (2008). Corporate governance in banking: The role of the board of directors. Journal of Banking & Finance, 32(12), 2570–2580.CrossRefGoogle Scholar
  3. Avkiran, N. (2001). Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, 35(1), 57–80.CrossRefGoogle Scholar
  4. Aydogan, E. K. (2011). Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 38(4), 3992–3998.CrossRefGoogle Scholar
  5. Backx, M., Carney, M., & Gedajlovic, E. (2002). Public, private and mixed ownership and the performance of international airlines. Journal of Air Transport Management, 8(4), 213–220.CrossRefGoogle Scholar
  6. Baliga, B., Moyer, R., & Rao, R. (1996). CEO duality and firm performance: What’s the fuss? Strategic Management Journal, 17(1), 41–53.CrossRefGoogle Scholar
  7. Banker, R. D., Janakiraman, S., & Natarajan, R. (2004). Analysis of trends in technical and allocative efficiency: An application to Texas public school districts. European Journal of Operational Research, 154(2), 477–491.CrossRefGoogle Scholar
  8. Barbot, C., Costa, A., & Sochirca, E. (2008). Airlines performance in the new market context: A comparative productivity and efficiency analysis. Journal of Air Transport Management, 14(5), 270–274.CrossRefGoogle Scholar
  9. Barros, C. P., & Peypoch, N. (2009). An evaluation of European airlines’ operational performance. International Journal of Production Economics, 122(2), 525–533.CrossRefGoogle Scholar
  10. Bennedsen, M., Kongsted, H. C., & Nielsen, K. M. (2008). The causal effect of board size in the performance of small and medium-sized firms. Journal of Banking & Finance, 32(6), 1098–1109.CrossRefGoogle Scholar
  11. Bhagat, S., & Bolton, B. (2008). Corporate governance and firm performance. Journal of Corporate Finance, 14(3), 257–273.CrossRefGoogle Scholar
  12. Biener, C., & Eling, M. (2011). The performance of microinsurance programs: A data envelopment analysis. The Journal of Risk and Insurance, 78(1), 83–115.CrossRefGoogle Scholar
  13. Brockett, P. L., & Golany, B. (1996). Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science, 42(3), 466–472.CrossRefGoogle Scholar
  14. Carline, N. F., Linn, S. C., & Yadav, P. K. (2009). Operating performance changes associated with corporate mergers and the role of corporate governance. Journal of Banking & Finance, 33(10), 1829–1841.CrossRefGoogle Scholar
  15. Chang, Y.-H., & Yeh, C.-H. (2001). Evaluating airline competitiveness using multiattribute decision making. Omega International Journal of Management Sciences, 29(5), 405–415.CrossRefGoogle Scholar
  16. Chang, D. S., Kuo, Y. C., & Chen, T. Y. (2008). Productivity measurement of the manufacturing process for outsourcing decisions: The case of a Taiwanese printed circuit board manufacturer. International Journal of Production Research, 46(24), 6981–6995.CrossRefGoogle Scholar
  17. Charnes, A., & Cooper, W. W. (1962). Programming with fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186.CrossRefGoogle Scholar
  18. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  19. Charnes, A., Galleegos, A., & Li, H. (1996). Robustly efficient parametric frontiers via multiplicative DEA for domestic and international operations of the Latin American airline industry. European Journal of Operational Research, 88(3), 525–536.CrossRefGoogle Scholar
  20. Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213(1), 147–155.CrossRefGoogle Scholar
  21. Chen, Y., & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology & Management Journal, 5(1–2), 9–22.CrossRefGoogle Scholar
  22. Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170–1176.CrossRefGoogle Scholar
  23. Chiang, M. H., & Lin, J. H. (2007). The relationship between corporate governance and firm productivity: Evidence from Taiwan’s manufacturing firms. Corporate Governance, 15(5), 768–779.CrossRefGoogle Scholar
  24. Chiou, Y. U., & Chen, Y. H. (2006). Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transportation Research Part E, 42(2), 116–127.CrossRefGoogle Scholar
  25. Chuang, I. Y., Chiu, Y.-C., & Edward Wang, C. (2008). The performance of Asian airlines in the recent financial turmoil based on VaR and modified Sharpe ratio. Journal of Air Transport Management, 14(5), 257–262.CrossRefGoogle Scholar
  26. Cook, W. D., Liang, L., & Zhu, J. (2009). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega International Journal of Management Sciences, 38(6), 423–430.CrossRefGoogle Scholar
  27. Cook, W. D., Zhu, J., Yang, F., & Bi, G.-B. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122–1129.CrossRefGoogle Scholar
  28. Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Data envelopment analysis: History, models and interpretations. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis. Boston: Kluwer Academic.Google Scholar
  29. Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: With DEA-solver software and references. New York: Springer.Google Scholar
  30. Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157.CrossRefGoogle Scholar
  31. Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. Journal of Law and Economics, 26(2), 301–324.CrossRefGoogle Scholar
  32. Fang, H.-H., Lee, H.-S., Hwang, S.-N., & Chung, C.-C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega International Journal of Management Sciences, 41(4), 731–734.CrossRefGoogle Scholar
  33. Fare, R., & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46(2), 201–213.CrossRefGoogle Scholar
  34. Feng, C.-M., & Wang, R.-T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133–142.CrossRefGoogle Scholar
  35. Feroz, E. H., Kim, S., & Raab, R. L. (2003). Financial statement analysis: A data envelopment analysis approach. Journal of the Operational Research Society, 54(1), 48–58.CrossRefGoogle Scholar
  36. Fielding, G. J., Glauthier, R. E., & Lave, C. A. (1978). Performance evaluation for bus transit. Transportation Research Part A, 19(1), 73–82.CrossRefGoogle Scholar
  37. Francis, G., Humphreys, I., & Fry, J. (2002). The benchmarking of airport performance. Journal of Air Transport Management, 8(4), 239–247.CrossRefGoogle Scholar
  38. Francis, G., Humphreys, I., & Fry, J. (2005). The nature and prevalence of the use of performance measurement techniques by airlines. Journal of Air Transport Management, 11(4), 207–217.CrossRefGoogle Scholar
  39. Franke, M., & John, F. (2011). What comes next after recession? – Airline industry scenarios and potential end games. Journal of Air Transport Management, 17(1), 19–26.CrossRefGoogle Scholar
  40. Gattoufi, S., Oral, M., & Reisman, A. (2004). Data Envelopment Analysis literature: A bibliography update (1996–2001). Socio-Economics Planning Sciences, 38(2–3), 122–159.Google Scholar
  41. Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega International Journal of Management Sciences, 17(3), 237–250.CrossRefGoogle Scholar
  42. Gompers, P. A., Ishii, J. L., & Metrick, A. (2003). Corporate governance and equity prices. Quarterly Journal of Economics, 118(1), 107–155.CrossRefGoogle Scholar
  43. Gorin, T., & Belobaba, P. (2004). Impacts of entry in airline markets: Effects of revenue management on traditional measures of airline performance. Journal of Air Transport Management, 10(4), 257–268.CrossRefGoogle Scholar
  44. Gramani, M. C. N. (2012). Efficiency decomposition approach: A cross-country airline analysis. Expert Systems with Applications, 39(5), 5815–5819.CrossRefGoogle Scholar
  45. Greer, M. R. (2008). Nothing focuses the mind on productivity quite like the fear of liquidation: Changes in airline productivity in the United States, 2000–2004. Transportation Research Part A, 42(2), 414–426.Google Scholar
  46. Harris, M., & Raviv, A. (2008). A theory of board control and size. Review of Financial Studies, 21(4), 1797–1832.CrossRefGoogle Scholar
  47. Homburg, C. (2001). Using data envelopment analysis to benchmark activities. International Journal of Production Economics, 73(1), 51–58.CrossRefGoogle Scholar
  48. Hong, S., & Zhang, A. (2010). An efficiency study of airlines and air cargo/passenger divisions: A DEA approach. World Review of Intermodal Transportation Research, 3(1–2), 137–149.CrossRefGoogle Scholar
  49. Jensen, M. C. (1993). The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance, 48(3), 831–880.CrossRefGoogle Scholar
  50. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics, 3(4), 305–360.CrossRefGoogle Scholar
  51. Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962.CrossRefGoogle Scholar
  52. Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.CrossRefGoogle Scholar
  53. Karlaftis, M. G. (2004). A DEA approach for evaluating the efficiency and effectiveness of urban transit systems. European Journal of Operational Research, 152(2), 354–364.CrossRefGoogle Scholar
  54. Keh, H. T., Chu, S., & Xu, J. (2006). Efficiency, effectiveness and productivity of marketing in service. European Journal of Operational Research, 170(1), 265–276.CrossRefGoogle Scholar
  55. Kiel, G. C., & Nicholson, G. J. (2003). Board composition and corporate performance: How the Australian experience informs contrasting theories of corporate governance. Corporate Governance, 11(3), 189–205.CrossRefGoogle Scholar
  56. Lee, S., Seo, K., & Sharma, A. (2013). Corporate social responsibility and firm performance in the airline industry: The moderating role of oil prices. Tourism Management, 38, 20–30.CrossRefGoogle Scholar
  57. Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643–653.CrossRefGoogle Scholar
  58. Lin, W.-C. (2012). Financial performance and customer service: An examination using activity-based costing of 38 international airlines. Journal of Air Transport Management, 19, 13–15.CrossRefGoogle Scholar
  59. Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013a). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega International Journal of Management Sciences, 41(1), 3–15.CrossRefGoogle Scholar
  60. Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013b). A survey of DEA applications. Omega International Journal of Management Sciences, 41(5), 893–902.CrossRefGoogle Scholar
  61. Lozano, S., & Gutiérrez, E. (2011). A multiobjective approach to fleet, fuel and operating cost efficiency of European airlines. Computers & Industrial Engineering, 61(3), 473–481.CrossRefGoogle Scholar
  62. Lu, W.-M., & Hung, S.-W. (2010). Assessing the performance of a vertically disintegrated chain by the DEA approach – A case study of Taiwanese semiconductor firms. International Journal of Production Research, 48(4), 1155–1170.CrossRefGoogle Scholar
  63. Lu, W.-M., & Hung, S.-W. (2011). Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective – A case study of Taiwan. Technovation, 31(8), 374–383.CrossRefGoogle Scholar
  64. Lu, W.-M., Wang, W.-K., & Kweh, Q. L. (2014). Intellectual capital and performance in the Chinese life insurance industry. Omega International Journal of Management Sciences, 42(1), 65–74.CrossRefGoogle Scholar
  65. Matthews, K. (2013). Risk management and managerial efficiency in Chinese banks: A network DEA framework. Omega International Journal of Management Sciences, 41(2), 207–215.CrossRefGoogle Scholar
  66. Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.CrossRefGoogle Scholar
  67. Merkert, R., & Hensher, D. A. (2011). The impact of strategic management and fleet planning on airline efficiency – A random effects Tobit model based on DEA efficiency scores. Transportation Research Part A: Policy and Practice, 45(7), 686–695.Google Scholar
  68. Merkert, R., & Williams, G. (2013). Determinants of European PSO airline efficiency – Evidence from a semi-parametric approach. Journal of Air Transport Management, 29, 11–16.CrossRefGoogle Scholar
  69. Neter, J., Wasserman, W., & Kutner, M. H. (1985). Applied linear statistical models: Regression, analysis of variance, and experimental designs. Homewood: R.D. Irwin.Google Scholar
  70. Premachandra, I. M., Chen, Y., & Watson, J. (2011). DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega International Journal of Management Sciences, 39(6), 620–626.CrossRefGoogle Scholar
  71. Provan, K. G. (1980). Board power and organizational effectiveness among human service agencies. The Academy of Management Journal, 23(2), 221–236.CrossRefGoogle Scholar
  72. Raghavan, S., & Rhoades, D. L. (2005). Revisiting the relationship between profitability and air carrier safety in the US airline industry. Journal of Air Transport Management, 11(4), 283–290.CrossRefGoogle Scholar
  73. Ray, S. C., & Hu, X. (1997). On the technically efficient organization of an industry: A study of U.S. airlines. Journal of Productivity Analysis, 8(1), 5–18.CrossRefGoogle Scholar
  74. Schefczyk, M. (1993). Operational performance of airlines: An extension of traditional measurement paradigms. Strategic Management Journal, 14(4), 301–317.CrossRefGoogle Scholar
  75. Scheraga, C. A. (2004). Operational efficiency versus financial mobility in the global airline industry: A data envelopment and Tobit analysis. Transportation Research Part A, 38(5), 383–404.Google Scholar
  76. Seiford, L. M. (1997). A bibliography for Data Envelopment Analysis (1978–1996). Annals of Operations Research, 73, 393–438.CrossRefGoogle Scholar
  77. Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management Science, 45(9), 1270–1288.CrossRefGoogle Scholar
  78. Sengupta, J. K. (1999). A dynamic efficiency model using data envelopment analysis. International Journal of Production Economics, 62(3), 209–218.CrossRefGoogle Scholar
  79. Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.CrossRefGoogle Scholar
  80. Sonnenfeld, J. A. (2002). What makes great boards great. Harvard Business Review, RO209H.Google Scholar
  81. Stathopoulos, K., Espenlaub, S., & Walker, M. (2004). U.K. executive compensation practices: New economy versus old economy. Journal of Management Accounting Research, 16, 57–92.CrossRefGoogle Scholar
  82. Sueyoshi, T., & Goto, M. (2010). Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: A use of data envelopment analysis with strong complementary slackness condition. European Journal of Operational Research, 207(3), 1742–1753.CrossRefGoogle Scholar
  83. Sueyoshi, T., Goto, M., & Omi, Y. (2010). Corporate governance and firm performance: Evidence from Japanese manufacturing industries after the lost decade. European Journal of Operational Research, 203(3), 724–736.CrossRefGoogle Scholar
  84. Tan, K. H., & Rae, R. H. (2009). Uncovering the links between regulation and performance measurement. International Journal of Production Economics, 122(1), 449–457.CrossRefGoogle Scholar
  85. Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.CrossRefGoogle Scholar
  86. Vafeas, N. (1999). Board meeting frequency and firm performance. Journal of Financial Economics, 53(1), 113–142.CrossRefGoogle Scholar
  87. Wang, W.-K. (2005). A knowledge-based decision support system for measuring the performance of government real estate investment. Expert Systems with Applications, 29(4), 901–912.CrossRefGoogle Scholar
  88. Wang, W.-K., Lu, W.-M., & Tsai, C.-J. (2011). The relationship between airline performance and corporate governance amongst US Listed companies. Journal of Air Transport Management, 17(2), 148–152.CrossRefGoogle Scholar
  89. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and direct test for heteroskedasticity. Econometrica, 4, 817–838.CrossRefGoogle Scholar
  90. Xie, B., Davidson, W. N., & DaDalt, P. J. (2003). Earnings management and corporate governance: The role of the board and the audit committee. Journal of Corporate Finance, 9(3), 295–316.CrossRefGoogle Scholar
  91. Yang, X., & Morita, H. (2013). Efficiency improvement from multiple perspectives: An application to Japanese banking industry. Omega International Journal of Management Sciences, 41(3), 501–509.CrossRefGoogle Scholar
  92. Yeh, Q. J. (1996). The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. The Journal of the Operational Research Society, 47(8), 980–988.Google Scholar
  93. Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics, 40(1), 185–211.CrossRefGoogle Scholar
  94. Yu, M. M. (2008). Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world’s railways through NDEA analysis. Transportation Research Part A, 42(10), 1283–1294.Google Scholar
  95. Yu, M. M., & Lin, E. T. J. (2008). Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega International Journal of Management Sciences, 36(6), 1005–1017.CrossRefGoogle Scholar
  96. Zahra, S. A., & Pearce, J. A. (1989). Boards of directors and corporate financial performance: A review and Integrated model. Journal of Management, 15(2), 291–334.CrossRefGoogle Scholar
  97. Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.CrossRefGoogle Scholar
  98. Zhu, J. (2011). Airlines performance via two-stage network DEA approach. The Business and Economics Research Journal, 4(2), 260–269.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Wen-Min Lu
    • 1
  • Shiu-Wan Hung
    • 2
  • Qian Long Kweh
    • 3
  • Wei-Kang Wang
    • 4
  • En-Tzu Lu
    • 4
  1. 1.Department of Financial ManagementNational Defense UniversityBeitouTaiwan
  2. 2.Department of Business AdministrationNational Central UniversityJung-Li CityTaiwan
  3. 3.Department of AccountingCollege of Business Management and Accounting, Universiti Tenaga NasionalMuadzam ShahMalaysia
  4. 4.Department of AccountingYuan Ze UniversityChung-LiTaiwan

Personalised recommendations