Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data

Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology...
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Autor*in:	Bansal, Pooja [verfasserIn] Mehra, Aparna Kumar, Sunil

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Dynamic network DEA Dynamic metafrontier ML-productivity index Heterogeneous production technology Directional distance function Indian banks

Anmerkung:	© Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Computational economics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 59(2021), 1 vom: 11. Jan., Seite 297-324
Übergeordnetes Werk:	volume:59 ; year:2021 ; number:1 ; day:11 ; month:01 ; pages:297-324

Links:	Volltext

DOI / URN:	10.1007/s10614-020-10071-9

Katalog-ID:	SPR046190945

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520			\|a Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period.
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allfields	10.1007/s10614-020-10071-9 doi (DE-627)SPR046190945 (SPR)s10614-020-10071-9-e DE-627 ger DE-627 rakwb eng Bansal, Pooja verfasserin aut Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213 Mehra, Aparna aut Kumar, Sunil aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 59(2021), 1 vom: 11. Jan., Seite 297-324 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:59 year:2021 number:1 day:11 month:01 pages:297-324 https://dx.doi.org/10.1007/s10614-020-10071-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 59 2021 1 11 01 297-324
spelling	10.1007/s10614-020-10071-9 doi (DE-627)SPR046190945 (SPR)s10614-020-10071-9-e DE-627 ger DE-627 rakwb eng Bansal, Pooja verfasserin aut Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213 Mehra, Aparna aut Kumar, Sunil aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 59(2021), 1 vom: 11. Jan., Seite 297-324 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:59 year:2021 number:1 day:11 month:01 pages:297-324 https://dx.doi.org/10.1007/s10614-020-10071-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 59 2021 1 11 01 297-324
allfields_unstemmed	10.1007/s10614-020-10071-9 doi (DE-627)SPR046190945 (SPR)s10614-020-10071-9-e DE-627 ger DE-627 rakwb eng Bansal, Pooja verfasserin aut Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213 Mehra, Aparna aut Kumar, Sunil aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 59(2021), 1 vom: 11. Jan., Seite 297-324 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:59 year:2021 number:1 day:11 month:01 pages:297-324 https://dx.doi.org/10.1007/s10614-020-10071-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 59 2021 1 11 01 297-324
allfieldsGer	10.1007/s10614-020-10071-9 doi (DE-627)SPR046190945 (SPR)s10614-020-10071-9-e DE-627 ger DE-627 rakwb eng Bansal, Pooja verfasserin aut Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213 Mehra, Aparna aut Kumar, Sunil aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 59(2021), 1 vom: 11. Jan., Seite 297-324 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:59 year:2021 number:1 day:11 month:01 pages:297-324 https://dx.doi.org/10.1007/s10614-020-10071-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 59 2021 1 11 01 297-324
allfieldsSound	10.1007/s10614-020-10071-9 doi (DE-627)SPR046190945 (SPR)s10614-020-10071-9-e DE-627 ger DE-627 rakwb eng Bansal, Pooja verfasserin aut Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213 Mehra, Aparna aut Kumar, Sunil aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 59(2021), 1 vom: 11. Jan., Seite 297-324 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:59 year:2021 number:1 day:11 month:01 pages:297-324 https://dx.doi.org/10.1007/s10614-020-10071-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 59 2021 1 11 01 297-324
language	English
source	Enthalten in Computational economics 59(2021), 1 vom: 11. Jan., Seite 297-324 volume:59 year:2021 number:1 day:11 month:01 pages:297-324
sourceStr	Enthalten in Computational economics 59(2021), 1 vom: 11. Jan., Seite 297-324 volume:59 year:2021 number:1 day:11 month:01 pages:297-324
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dynamic network DEA Dynamic metafrontier ML-productivity index Heterogeneous production technology Directional distance function Indian banks
isfreeaccess_bool	false
container_title	Computational economics
authorswithroles_txt_mv	Bansal, Pooja @@aut@@ Mehra, Aparna @@aut@@ Kumar, Sunil @@aut@@
publishDateDaySort_date	2021-01-11T00:00:00Z
hierarchy_top_id	270427546
id	SPR046190945
language_de	englisch
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author	Bansal, Pooja
spellingShingle	Bansal, Pooja misc Dynamic network DEA misc Dynamic metafrontier ML-productivity index misc Heterogeneous production technology misc Directional distance function misc Indian banks Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data
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topic_title	Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data Dynamic network DEA (dpeaa)DE-He213 Dynamic metafrontier ML-productivity index (dpeaa)DE-He213 Heterogeneous production technology (dpeaa)DE-He213 Directional distance function (dpeaa)DE-He213 Indian banks (dpeaa)DE-He213
topic	misc Dynamic network DEA misc Dynamic metafrontier ML-productivity index misc Heterogeneous production technology misc Directional distance function misc Indian banks
topic_unstemmed	misc Dynamic network DEA misc Dynamic metafrontier ML-productivity index misc Heterogeneous production technology misc Directional distance function misc Indian banks
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title	Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data
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title_full	Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data
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author_browse	Bansal, Pooja Mehra, Aparna Kumar, Sunil
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title_sort	dynamic metafrontier malmquist–luenberger productivity index in network dea: an application to banking data
title_auth	Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data
abstract	Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. © Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. © Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract Recent advances in the study of dynamic network data envelopment analysis (DNDEA) have shown to provide better insight into the system to improve the efficiency and productivity of a decision-making unit (DMU). A network structure of a DMU takes a holistic view of the production technology that connects several divisions internally by intermediate products and uses the carryovers flow over time to add a temporal dimension to it. In this paper, we propose a dynamic metafrontier Malmquist–Luenberger productivity index (DMMLPI) in the DNDEA framework to measure the total factor productivity change of a DMU when the data involves negative values and undesirable features. The DMMLPI decomposes the productivity change index into three indices: efficiency change, best-practice change, and technology gap change. To demonstrate the capability of the proposed index, we work on a balanced panel data of sixty Indian banks from 2013 to 2017. The sample banks are grouped into three categories: public banks, private banks, and foreign banks. The banks across the categories face heterogeneous production technology, business strategies, and operating environments. We assume that the underlying production architecture of each bank is a three-stage dynamic network. Our empirical analysis shows that foreign banks outperform their counterparts in terms of productivity change measured by the DMMLPI in the considered period. © Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data
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Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data

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