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

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Data Envelopment Analysis

Part of the book series: International Series in Operations Research & Management Science ((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.

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ReferencesÎ

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Avkiran, N. (2001). Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, 35(1), 57–80.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Baliga, B., Moyer, R., & Rao, R. (1996). CEO duality and firm performance: What’s the fuss? Strategic Management Journal, 17(1), 41–53.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Barros, C. P., & Peypoch, N. (2009). An evaluation of European airlines’ operational performance. International Journal of Production Economics, 122(2), 525–533.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bhagat, S., & Bolton, B. (2008). Corporate governance and firm performance. Journal of Corporate Finance, 14(3), 257–273.

    Article  Google Scholar 

  • Biener, C., & Eling, M. (2011). The performance of microinsurance programs: A data envelopment analysis. The Journal of Risk and Insurance, 78(1), 83–115.

    Article  Google Scholar 

  • Brockett, P. L., & Golany, B. (1996). Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science, 42(3), 466–472.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chang, Y.-H., & Yeh, C.-H. (2001). Evaluating airline competitiveness using multiattribute decision making. Omega International Journal of Management Sciences, 29(5), 405–415.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1962). Programming with fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213(1), 147–155.

    Article  Google Scholar 

  • Chen, Y., & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology & Management Journal, 5(1–2), 9–22.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. Journal of Law and Economics, 26(2), 301–324.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fare, R., & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46(2), 201–213.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fielding, G. J., Glauthier, R. E., & Lave, C. A. (1978). Performance evaluation for bus transit. Transportation Research Part A, 19(1), 73–82.

    Article  Google Scholar 

  • Francis, G., Humphreys, I., & Fry, J. (2002). The benchmarking of airport performance. Journal of Air Transport Management, 8(4), 239–247.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega International Journal of Management Sciences, 17(3), 237–250.

    Article  Google Scholar 

  • Gompers, P. A., Ishii, J. L., & Metrick, A. (2003). Corporate governance and equity prices. Quarterly Journal of Economics, 118(1), 107–155.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gramani, M. C. N. (2012). Efficiency decomposition approach: A cross-country airline analysis. Expert Systems with Applications, 39(5), 5815–5819.

    Article  Google Scholar 

  • 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 

  • Harris, M., & Raviv, A. (2008). A theory of board control and size. Review of Financial Studies, 21(4), 1797–1832.

    Article  Google Scholar 

  • Homburg, C. (2001). Using data envelopment analysis to benchmark activities. International Journal of Production Economics, 73(1), 51–58.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jensen, M. C. (1993). The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance, 48(3), 831–880.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Provan, K. G. (1980). Board power and organizational effectiveness among human service agencies. The Academy of Management Journal, 23(2), 221–236.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Schefczyk, M. (1993). Operational performance of airlines: An extension of traditional measurement paradigms. Strategic Management Journal, 14(4), 301–317.

    Article  Google Scholar 

  • 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 

  • Seiford, L. M. (1997). A bibliography for Data Envelopment Analysis (1978–1996). Annals of Operations Research, 73, 393–438.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management Science, 45(9), 1270–1288.

    Article  Google Scholar 

  • Sengupta, J. K. (1999). A dynamic efficiency model using data envelopment analysis. International Journal of Production Economics, 62(3), 209–218.

    Article  Google Scholar 

  • Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.

    Article  Google Scholar 

  • Sonnenfeld, J. A. (2002). What makes great boards great. Harvard Business Review, RO209H.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Tan, K. H., & Rae, R. H. (2009). Uncovering the links between regulation and performance measurement. International Journal of Production Economics, 122(1), 449–457.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.

    Article  Google Scholar 

  • Vafeas, N. (1999). Board meeting frequency and firm performance. Journal of Financial Economics, 53(1), 113–142.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and direct test for heteroskedasticity. Econometrica, 4, 817–838.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics, 40(1), 185–211.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.

    Article  Google Scholar 

  • Zhu, J. (2011). Airlines performance via two-stage network DEA approach. The Business and Economics Research Journal, 4(2), 260–269.

    Google Scholar 

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Authors and Affiliations

  1. Department of Financial Management, National Defense University, No. 70, Section 2, Zhongyang North Road, Beitou, Taipei, 112, Taiwan

    Wen-Min Lu

  2. Department of Business Administration, National Central University, No. 300, Jung-Da Road, Jung-Li City, Tao-Yuan, 320, Taiwan

    Shiu-Wan Hung

  3. Department of Accounting, College of Business Management and Accounting, Universiti Tenaga Nasional, Sultan Haji Ahmad Shah Campus, Muadzam Shah, Pahang, 26700, Malaysia

    Qian Long Kweh

  4. Department of Accounting, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taiwan

    Wei-Kang Wang & En-Tzu Lu

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  1. Wen-Min Lu
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  2. Shiu-Wan Hung
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Correspondence to Shiu-Wan Hung .

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Editors and Affiliations

  1. Schulich School of Business, York University, Toronto, Ontario, Canada

    Wade D. Cook

  2. School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Joe Zhu

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Lu, WM., Hung, SW., Kweh, Q.L., Wang, WK., Lu, ET. (2014). Production and Marketing Efficiencies of the U.S. Airline Industry: A Two-Stage Network DEA Approach. In: Cook, W., Zhu, J. (eds) Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 208. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-8068-7_21

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