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Productivity and efficiency measurement of airlines : data development analysis using R /

In today's competitive environment, airlines are doing everything they can to improve efficiency and productivity. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R identifies and explains sources of airline efficiency and helps achieve these goals through t...

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Bibliographic Details
Main Author: Lee, Boon (Author)
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA : Elsevier, [2023]
Subjects:
Airlines > Economic aspects.
Industrial efficiency.
Compagnies de transport aérien > Aspect économique.
Efficience dans l'industrie.
Airlines > Economic aspects
Industrial efficiency
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Productivity and Efficiency Measurement of Airlines
  • Productivity and Efficiency Measurement of Airlines:Data Envelopment Analysis using R
  • Copyright
  • Dedication
  • Contents
  • Preface
  • 1
  • Introduction
  • 1.1 Introduction
  • 1.2 Evolution and deregulation of the global airline industry-a brief comment
  • 1.3 A brief history of developments in data envelopment analysis
  • 1.4 Outline of chapters
  • References
  • 2
  • Literature on data envelopment analysis in airline efficiency and productivity
  • 2.1 Introduction
  • 2.2 Literature on airline efficiency using standard data envelopment analysis model
  • 2.3 Literature on airline cost efficiency, revenue efficiency and profit efficiency
  • 2.4 Literature on airline productivity change performance
  • 2.5 Literature on airline efficiency incorporating bad output
  • 2.6 Literature on airline performance based on network DEA or DEA linked by phases
  • 2.7 Literature on airline efficiency using other variations of DEA models
  • 2.8 Literature on airline efficiency incorporating second-stage regression analysis
  • 2.9 Conclusion
  • References
  • 3
  • Measuring airline performance: standard DEA
  • 3.1 Introduction
  • 3.2 Data issues
  • 3.2.1 Provision model
  • 3.2.2 Delivery model
  • 3.2.3 Cost and revenue efficiency model
  • 3.3 DEA models
  • 3.3.1 CCR model
  • 3.3.2 BCC model
  • 3.3.3 Cost minimization model
  • 3.3.4 Revenue maximization model
  • 3.4 R package
  • 3.5 R script for DEA, results and interpretation of results
  • 3.5.1 R script for DEA (Charnes et al. 1978) CCR model
  • 3.5.2 Interpretation of DEA (CCR) results for the 'provision' model
  • 3.5.2.1 Interpreting radial (proportionate) and slack movements
  • 3.5.2.2 Scale efficiency
  • 3.5.3 R script for DEA ('delivery' model)
  • 3.5.4 Interpretation of DEA results for the 'delivery' model.
  • 3.5.5 Cost and revenue efficiency model
  • 3.6 Reliability of results
  • 3.6.1 Bootstrapping DEA
  • 3.6.2 Bootstrap cost-efficiency
  • 3.6.3 Hypothesis test for returns to scale
  • 3.7 Conclusion
  • Appendix A
  • References
  • 4
  • Measuring airline productivity change
  • 4.1 Introduction
  • 4.2 Malmquist productivity index
  • 4.2.1 R script for Malmquist productivity index
  • 4.2.2 Interpretation of results
  • 4.2.3 Final remark
  • 4.3 Hicks-Moorsteen productivity index
  • 4.3.1 R script for Hicks-Moorsteen productivity index
  • 4.3.2 Interpretation of results
  • 4.3.3 Final remark
  • 4.4 Lowe productivity index
  • 4.4.1 R script for Lowe productivity index
  • 4.4.2 Interpretation of Lowe productivity and profitability change results
  • 4.4.3 Final remark
  • 4.5 Färe-Primont productivity index
  • 4.5.1 R script for FP to measure productivity and profitability change
  • 4.5.2 Interpretation of Färe-Primont productivity and profitability change results
  • 4.5.2.1 Productivity results
  • 4.5.2.2 Profitability results
  • 4.5.3 Final remark
  • 4.6 A comparisons of productivity indices
  • 4.7 Conclusion
  • Appendix B
  • References
  • 5
  • DEA variants in measuring airline performance
  • 5.1 Introduction
  • 5.2 Metafrontier DEA
  • 5.2.1 R script for metafrontier
  • 5.2.2 Interpretation of metafrontier results for the 'delivery' model
  • 5.3 Slacks-based measure
  • 5.3.1 R script for slacks-bases measure
  • 5.3.2 Interpretation of slacks-based measure results for the 'delivery' model
  • 5.4 Superefficiency DEA
  • 5.4.1 R script for Andersen and Petersen (1993) superefficiency DEA
  • 5.4.2 Interpretation of Andersen and Petersen (1993) superefficiency results for the 'delivery' model
  • 5.4.3 Cook et al. (2009) modified superefficiency DEA
  • 5.4.4 R script for Cook et al. (2009) modified superefficiency DEA.
  • 5.4.5 Interpretation of Cook et al. (2009) modified superefficiency results for the 'delivery' model
  • 5.4.6 Tone (2002) superefficiency SBM
  • 5.4.7 R script for Tone (2002) superefficiency SBM
  • 5.4.8 Interpretation of Tone (2002) super SBM results for the 'delivery' model
  • 5.5 Potential gains DEA
  • 5.5.1 R script for Bogetoft and Wang (2005) merger DEA
  • 5.5.2 Interpretation of PGDEA results
  • 5.6 Directional distance function-Chambers et al. (1996)
  • 5.6.1 R script for Chambers et al. (1998) directional distance function
  • 5.6.2 Interpretation of directional distance function results
  • 5.7 Conclusion
  • Appendix C
  • References
  • 6
  • Measuring airline performance: incorporating bad outputs
  • 6.1 Introduction
  • 6.2 Environmental DEA technology model
  • 6.3 Seiford and Zhu (2002) transformation approach
  • 6.3.1 R script for Seiford and Zhu (2002) model
  • 6.3.2 Interpretation of Seiford and Zhu (2002) results
  • 6.4 Zhou et al. (2008) environmental DEA model
  • 6.4.1 Pure environmental performance index (EPICRS)
  • 6.4.2 NIRS environmental performance index (EPINIRS)
  • 6.4.3 VRS environmental performance index (EPIVRS)
  • 6.4.4 Mixed environmental performance index
  • 6.4.5 R script for Zhou et al. (2008) environmental DEA model
  • 6.4.6 Discussion of results
  • 6.5 Tone's SBM with bad outputs in Cooper et al. (2007)
  • 6.5.1 R script for Tone's SBM with bad output
  • 6.5.2 Interpretation of Tone's SBM with bad output results
  • 6.6 Chung et al. (1997) Malmquist-Luenberger
  • 6.6.1 R script for Malmquist-Luenberger model
  • 6.6.2 Interpretation of Malmquist-Luenberger results
  • 6.7 Conclusions
  • Appendix D
  • References
  • 7
  • Measuring airline performance: Network DEA
  • 7.1 Introduction
  • 7.2 A basic two-node network DEA
  • 7.3 Kao and Hwang (2008) and Liang et al. (2008) network DEA centralized model.
  • 7.3.1 R script for Kao and Hwang (2008) and Liang et al. (2008)
  • 7.3.2 Interpretation of results
  • 7.4 Network DEA (Farrell efficiency model)-network technical efficiency
  • 7.4.1 NTE input-oriented VRS model
  • 7.4.2 NTE output-oriented VRS model
  • 7.4.3 R script for NTE input- and output-oriented VRS
  • 7.4.4 Results for the NTE input- and output-oriented VRS and CRS model
  • 7.5 Network cost efficiency model (Fukuyama and Matousek, 2011)
  • 7.5.1 R script for NCE VRS model
  • 7.5.2 Results for the NCE VRS model
  • 7.6 Network revenue efficiency model (Fukuyama and Matousek, 2017)
  • 7.6.1 R script for NRE VRS model
  • 7.6.2 Results for the NRE VRS model
  • 7.7 Network DEA directional distance function inefficiency model (Fukuyama and Weber, 2012)
  • 7.7.1 R script for NDEA-DDF VRS model
  • 7.7.2 Results for the NDEA-DDF VRS model
  • 7.8 Network slacks-based inefficiency model
  • 7.8.1 R script for the NSBI model
  • 7.8.2 Results for the NSBI model
  • 7.9 A general network technology model to depict the airline provision-delivery model
  • 7.9.1 R script for the NT model
  • 7.9.2 Results for the NT model
  • 7.10 Conclusion
  • Appendix E
  • References
  • 8
  • Sources of airline performance
  • 8.1 Introduction
  • 8.2 Data for second-stage regression
  • 8.3 Multicollinearity test and separability test
  • 8.3.1 R script for multicollinearity test
  • 8.3.2 Interpretation of the multicollinearity test results
  • 8.3.3 R script for separability test
  • 8.3.4 Interpretation of the separability test results
  • 8.4 Ordinary least squares regression model
  • 8.4.1 R script for ordinary least squares
  • 8.4.2 Interpretation of results
  • 8.5 Generalized least squares regression model
  • 8.5.1 R script for generalized least squares
  • 8.5.2 Interpretation of results
  • 8.6 Tobit regression model
  • 8.6.1 R script for the Tobit regression.
  • 8.6.2 Interpretation of Tobit results for the 'delivery model'
  • 8.7 Simar and Wilson (2007) regression model
  • 8.7.1 R script for Simar and Wilson (2007) double-bootstrap truncated regression
  • 8.7.2 Interpretation of Simar and Wilson's (2007) double-bootstrap truncated regression results
  • 8.8 Conclusion
  • Appendix F
  • References
  • 9
  • Conclusion
  • References
  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • L
  • M
  • N
  • O
  • P
  • R
  • S
  • T
  • V
  • W
  • Back Cover.