Network DEA and Its Applications (2017–2022): A Systematic Literature Review

Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider t...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Svetlana V. Ratner [verfasserIn] Artem M. Shaposhnikov [verfasserIn] Andrey V. Lychev [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	data envelopment analysis network models multi-stage models literature review

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 9, p 2141
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:9, p 2141

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11092141

Katalog-ID:	DOAJ09035320X

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ09035320X
003	DE-627
005	20240413035420.0
007	cr uuu---uuuuu
008	230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/math11092141 \|2 doi
035			\|a (DE-627)DOAJ09035320X
035			\|a (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a QA1-939
100	0		\|a Svetlana V. Ratner \|e verfasserin \|4 aut
245	1	0	\|a Network DEA and Its Applications (2017–2022): A Systematic Literature Review
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.
650		4	\|a data envelopment analysis
650		4	\|a network models
650		4	\|a multi-stage models
650		4	\|a literature review
653		0	\|a Mathematics
700	0		\|a Artem M. Shaposhnikov \|e verfasserin \|4 aut
700	0		\|a Andrey V. Lychev \|e verfasserin \|4 aut
773	0	8	\|i In \|t Mathematics \|d MDPI AG, 2013 \|g 11(2023), 9, p 2141 \|w (DE-627)737287764 \|w (DE-600)2704244-3 \|x 22277390 \|7 nnns
773	1	8	\|g volume:11 \|g year:2023 \|g number:9, p 2141
856	4	0	\|u https://doi.org/10.3390/math11092141 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2227-7390/11/9/2141 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2227-7390 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 11 \|j 2023 \|e 9, p 2141

Indexfelder

author_variant	s v r svr a m s ams a v l avl
matchkey_str	article:22277390:2023----::ewrdanisplctos0702ssea
hierarchy_sort_str	2023
callnumber-subject-code	QA
publishDate	2023
allfields	10.3390/math11092141 doi (DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f DE-627 ger DE-627 rakwb eng QA1-939 Svetlana V. Ratner verfasserin aut Network DEA and Its Applications (2017–2022): A Systematic Literature Review 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. data envelopment analysis network models multi-stage models literature review Mathematics Artem M. Shaposhnikov verfasserin aut Andrey V. Lychev verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 9, p 2141 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:9, p 2141 https://doi.org/10.3390/math11092141 kostenfrei https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f kostenfrei https://www.mdpi.com/2227-7390/11/9/2141 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9, p 2141
spelling	10.3390/math11092141 doi (DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f DE-627 ger DE-627 rakwb eng QA1-939 Svetlana V. Ratner verfasserin aut Network DEA and Its Applications (2017–2022): A Systematic Literature Review 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. data envelopment analysis network models multi-stage models literature review Mathematics Artem M. Shaposhnikov verfasserin aut Andrey V. Lychev verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 9, p 2141 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:9, p 2141 https://doi.org/10.3390/math11092141 kostenfrei https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f kostenfrei https://www.mdpi.com/2227-7390/11/9/2141 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9, p 2141
allfields_unstemmed	10.3390/math11092141 doi (DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f DE-627 ger DE-627 rakwb eng QA1-939 Svetlana V. Ratner verfasserin aut Network DEA and Its Applications (2017–2022): A Systematic Literature Review 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. data envelopment analysis network models multi-stage models literature review Mathematics Artem M. Shaposhnikov verfasserin aut Andrey V. Lychev verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 9, p 2141 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:9, p 2141 https://doi.org/10.3390/math11092141 kostenfrei https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f kostenfrei https://www.mdpi.com/2227-7390/11/9/2141 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9, p 2141
allfieldsGer	10.3390/math11092141 doi (DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f DE-627 ger DE-627 rakwb eng QA1-939 Svetlana V. Ratner verfasserin aut Network DEA and Its Applications (2017–2022): A Systematic Literature Review 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. data envelopment analysis network models multi-stage models literature review Mathematics Artem M. Shaposhnikov verfasserin aut Andrey V. Lychev verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 9, p 2141 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:9, p 2141 https://doi.org/10.3390/math11092141 kostenfrei https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f kostenfrei https://www.mdpi.com/2227-7390/11/9/2141 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9, p 2141
allfieldsSound	10.3390/math11092141 doi (DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f DE-627 ger DE-627 rakwb eng QA1-939 Svetlana V. Ratner verfasserin aut Network DEA and Its Applications (2017–2022): A Systematic Literature Review 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. data envelopment analysis network models multi-stage models literature review Mathematics Artem M. Shaposhnikov verfasserin aut Andrey V. Lychev verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 9, p 2141 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:9, p 2141 https://doi.org/10.3390/math11092141 kostenfrei https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f kostenfrei https://www.mdpi.com/2227-7390/11/9/2141 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 9, p 2141
language	English
source	In Mathematics 11(2023), 9, p 2141 volume:11 year:2023 number:9, p 2141
sourceStr	In Mathematics 11(2023), 9, p 2141 volume:11 year:2023 number:9, p 2141
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	data envelopment analysis network models multi-stage models literature review Mathematics
isfreeaccess_bool	true
container_title	Mathematics
authorswithroles_txt_mv	Svetlana V. Ratner @@aut@@ Artem M. Shaposhnikov @@aut@@ Andrey V. Lychev @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	737287764
id	DOAJ09035320X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ09035320X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413035420.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/math11092141</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09035320X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA1-939</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Svetlana V. Ratner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Network DEA and Its Applications (2017–2022): A Systematic Literature Review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data envelopment analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">network models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-stage models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">literature review</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Artem M. Shaposhnikov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Andrey V. Lychev</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Mathematics</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">11(2023), 9, p 2141</subfield><subfield code="w">(DE-627)737287764</subfield><subfield code="w">(DE-600)2704244-3</subfield><subfield code="x">22277390</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:9, p 2141</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/math11092141</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2227-7390/11/9/2141</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2227-7390</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">9, p 2141</subfield></datafield></record></collection>
callnumber-first	Q - Science
author	Svetlana V. Ratner
spellingShingle	Svetlana V. Ratner misc QA1-939 misc data envelopment analysis misc network models misc multi-stage models misc literature review misc Mathematics Network DEA and Its Applications (2017–2022): A Systematic Literature Review
authorStr	Svetlana V. Ratner
ppnlink_with_tag_str_mv	@@773@@(DE-627)737287764
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	QA1-939
illustrated	Not Illustrated
issn	22277390
topic_title	QA1-939 Network DEA and Its Applications (2017–2022): A Systematic Literature Review data envelopment analysis network models multi-stage models literature review
topic	misc QA1-939 misc data envelopment analysis misc network models misc multi-stage models misc literature review misc Mathematics
topic_unstemmed	misc QA1-939 misc data envelopment analysis misc network models misc multi-stage models misc literature review misc Mathematics
topic_browse	misc QA1-939 misc data envelopment analysis misc network models misc multi-stage models misc literature review misc Mathematics
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Mathematics
hierarchy_parent_id	737287764
hierarchy_top_title	Mathematics
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)737287764 (DE-600)2704244-3
title	Network DEA and Its Applications (2017–2022): A Systematic Literature Review
ctrlnum	(DE-627)DOAJ09035320X (DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f
title_full	Network DEA and Its Applications (2017–2022): A Systematic Literature Review
author_sort	Svetlana V. Ratner
journal	Mathematics
journalStr	Mathematics
callnumber-first-code	Q
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Svetlana V. Ratner Artem M. Shaposhnikov Andrey V. Lychev
container_volume	11
class	QA1-939
format_se	Elektronische Aufsätze
author-letter	Svetlana V. Ratner
doi_str_mv	10.3390/math11092141
author2-role	verfasserin
title_sort	network dea and its applications (2017–2022): a systematic literature review
callnumber	QA1-939
title_auth	Network DEA and Its Applications (2017–2022): A Systematic Literature Review
abstract	Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.
abstractGer	Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.
abstract_unstemmed	Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	9, p 2141
title_short	Network DEA and Its Applications (2017–2022): A Systematic Literature Review
url	https://doi.org/10.3390/math11092141 https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f https://www.mdpi.com/2227-7390/11/9/2141 https://doaj.org/toc/2227-7390
remote_bool	true
author2	Artem M. Shaposhnikov Andrey V. Lychev
author2Str	Artem M. Shaposhnikov Andrey V. Lychev
ppnlink	737287764
callnumber-subject	QA - Mathematics
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/math11092141
callnumber-a	QA1-939
up_date	2024-07-03T14:16:04.135Z
_version_	1803567669325070336
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ09035320X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413035420.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/math11092141</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ09035320X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5fc2044ec5fc4610bd68f25ec3130c7f</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA1-939</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Svetlana V. Ratner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Network DEA and Its Applications (2017–2022): A Systematic Literature Review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data envelopment analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">network models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-stage models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">literature review</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Artem M. Shaposhnikov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Andrey V. Lychev</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Mathematics</subfield><subfield code="d">MDPI AG, 2013</subfield><subfield code="g">11(2023), 9, p 2141</subfield><subfield code="w">(DE-627)737287764</subfield><subfield code="w">(DE-600)2704244-3</subfield><subfield code="x">22277390</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:9, p 2141</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/math11092141</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5fc2044ec5fc4610bd68f25ec3130c7f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2227-7390/11/9/2141</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2227-7390</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">9, p 2141</subfield></datafield></record></collection>
score	7.3990517

Nicht das Richtige dabei?

Schreiben Sie uns!

Network DEA and Its Applications (2017–2022): A Systematic Literature Review

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?