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The impact of strategic management and fleet planning on airline efficiency - A random effects Tobit model based on DEA efficiency scores

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  • Merkert, Rico
  • Hensher, David A.

Abstract

As a result of the liberalisation of airline markets; the strong growth of low cost carriers; the high volatility in fuel prices; and the recent global financial crisis, the cost pressure that airlines face is very substantial. In order to survive in these very competitive environments, information on what factors impact on costs and efficiency of airlines is crucial in guiding strategic change. To evaluate key determinants of 58 passenger airlines' efficiency, this paper applies a two-stage Data Envelopment Analysis (DEA) approach, with partially bootstrapped random effects Tobit regressions in the second stage. Our results suggest that the effects of route optimisation, in the sense of average stage length of the fleet, are limited to airline technical efficiency. We show that airline size and key fleet mix characteristics, such as aircraft size and number of different aircraft families in the fleet, are more relevant to successful cost management of airlines since they have significant impacts on all three types of airline efficiency: technical, allocative and, ultimately, cost efficiency. Our results also show that despite the fuel saving benefits of younger aircraft, the age of an airline's fleet has no significant impact on its technical efficiency, but does have a positive impact on its allocative and cost efficiency.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

Volume (Year): 45 (2011)
Issue (Month): 7 (August)
Pages: 686-695

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Handle: RePEc:eee:transa:v:45:y:2011:i:7:p:686-695

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Keywords: Data Envelopment Analysis Airlines Efficiency Strategic management Fleet planning;

References

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  1. Barros, Carlos Pestana & Peypoch, Nicolas, 2009. "An evaluation of European airlines' operational performance," International Journal of Production Economics, Elsevier, vol. 122(2), pages 525-533, December.
  2. Simar, L. & Wilson, P.W., 1998. "A General Methodology for Bootstrapping in Nonparametric Frontier Models," Papers 9811, Catholique de Louvain - Institut de statistique.
  3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  4. Barbot, Cristina & Costa, Ãlvaro & Sochirca, Elena, 2008. "Airlines performance in the new market context: A comparative productivity and efficiency analysis," Journal of Air Transport Management, Elsevier, vol. 14(5), pages 270-274.
  5. Tsionas, Efthymios G. & Papadakis, Emmanuel N., 2010. "A Bayesian approach to statistical inference in stochastic DEA," Omega, Elsevier, vol. 38(5), pages 309-314, October.
  6. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
  7. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  9. Ouellette, Pierre & Petit, Patrick & Tessier-Parent, Louis-Philippe & Vigeant, Stéphane, 2010. "Introducing regulation in the measurement of efficiency, with an application to the Canadian air carriers industry," European Journal of Operational Research, Elsevier, vol. 200(1), pages 216-226, January.
  10. Bhadra, Dipasis, 2009. "Race to the bottom or swimming upstream: Performance analysis of US airlines," Journal of Air Transport Management, Elsevier, vol. 15(5), pages 227-235.
  11. Gillen David, 2006. "Airline Business Models and Networks: Regulation, Competition and Evolution in Aviation Markets," Review of Network Economics, De Gruyter, vol. 5(4), pages 1-20, December.
  12. Oum, Tae Hoon & Yu, Chunyan, 1998. "Cost competitiveness of major airlines: an international comparison," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(6), pages 407-422, August.
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Citations

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Cited by:
  1. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
  2. Aziz Kutlar & Ali Kabasakal & Murat Sarikaya, 2013. "Determination of the efficiency of the world railway companies by method of DEA and comparison of their efficiency by Tobit analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3575-3602, October.
  3. Zou, Bo & Elke, Matthew & Hansen, Mark & Kafle, Nabin, 2014. "Evaluating air carrier fuel efficiency in the US airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 306-330.
  4. Gramani, Maria Cristina N., 2011. "Impact of regional airline’s efficiency: a country-specific study over successive time periods," Insper Working Papers wpe_259, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
  5. Halkos, George & Tzeremes, Nickolaos, 2012. "Measuring seaports’ productivity: A Malmquist productivity index decomposition approach," MPRA Paper 40174, University Library of Munich, Germany.
  6. Friesner, Daniel & Mittelhammer, Ron & Rosenman, Robert, 2013. "Inferring the incidence of industry inefficiency from DEA estimates," European Journal of Operational Research, Elsevier, vol. 224(2), pages 414-424.
  7. Holmgren, Johan, 2013. "The efficiency of public transport operations – An evaluation using stochastic frontier analysis," Research in Transportation Economics, Elsevier, vol. 39(1), pages 50-57.
  8. Barros, Carlos P. & Liang, Qi Bin & Peypoch, Nicolas, 2013. "The technical efficiency of US Airlines," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 139-148.
  9. Merkert, Rico & Morrell, Peter S., 2012. "Mergers and acquisitions in aviation – Management and economic perspectives on the size of airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 853-862.
  10. Merkert, Rico & Hensher, David A., 2013. "Regulation, trust and contractual incentives around transport contracts – Is there anything bus operators can learn from public air service contracts?," Research in Transportation Economics, Elsevier, vol. 39(1), pages 67-78.
  11. Merkert, Rico & Williams, George, 2013. "Determinants of European PSO airline efficiency – Evidence from a semi-parametric approach," Journal of Air Transport Management, Elsevier, vol. 29(C), pages 11-16.
  12. Tavassoli, Mohammad & Faramarzi, Gholam Reza & Farzipoor Saen, Reza, 2014. "Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 146-153.
  13. Pearson, James & Merkert, Rico, 2014. "Airlines-within-airlines: A business model moving East," Journal of Air Transport Management, Elsevier, vol. 38(C), pages 21-26.
  14. Merkert, Rico & Ploix, Boris, 2014. "The impact of terminal re-organisation on belly-hold freight operation chains at airports," Journal of Air Transport Management, Elsevier, vol. 36(C), pages 78-84.
  15. Kappes, Jan Willem & Merkert, Rico, 2013. "Barriers to entry into European aviation markets revisited: A review and analysis of managerial perceptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 57(C), pages 58-69.

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