Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis Using R.
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
San Diego
Elsevier
2023
|
| Online Access: | HWR01 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (276 Seiten) |
| ISBN: | 9780128126974 |
Staff View
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| 100 | 1 | |a Lee, Boon L. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Productivity and Efficiency Measurement of Airlines |b Data Envelopment Analysis Using R. |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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| 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 |
| ctrlnum | (ZDB-30-PQE)EBC7205689 (ZDB-30-PAD)EBC7205689 (ZDB-89-EBL)EBL7205689 (OCoLC)1371754477 (DE-599)BVBBV048921466 |
| dewey-full | 338.45613877 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 338 - Production |
| dewey-raw | 338.45613877 |
| dewey-search | 338.45613877 |
| dewey-sort | 3338.45613877 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Lee, Boon L.</subfield><subfield code="t">Productivity and Efficiency Measurement of Airlines</subfield><subfield code="d">San Diego : Elsevier,c2023</subfield><subfield code="z">9780128126967</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034185557</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7205689</subfield><subfield code="l">HWR01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048921466 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:55:17Z |
| indexdate | 2024-07-10T09:49:55Z |
| institution | BVB |
| isbn | 9780128126974 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034185557 |
| oclc_num | 1371754477 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (276 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Lee, Boon L. Verfasser aut Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. San Diego Elsevier 2023 ©2023 1 Online-Ressource (276 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Erscheint auch als Druck-Ausgabe Lee, Boon L. Productivity and Efficiency Measurement of Airlines San Diego : Elsevier,c2023 9780128126967 |
| spellingShingle | Lee, Boon L. Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. 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 |
| title | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_auth | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_exact_search | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_exact_search_txtP | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_full | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_fullStr | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_full_unstemmed | Productivity and Efficiency Measurement of Airlines Data Envelopment Analysis Using R. |
| title_short | Productivity and Efficiency Measurement of Airlines |
| title_sort | productivity and efficiency measurement of airlines data envelopment analysis using r |
| title_sub | Data Envelopment Analysis Using R. |
| work_keys_str_mv | AT leeboonl productivityandefficiencymeasurementofairlinesdataenvelopmentanalysisusingr |