Analyzing the international connectivity of the major container ports in Northeast Asia

Gespeichert in:

Autor*in:	Phong Nha Nguyen [verfasserIn] Kim, Hwayoung [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Container port FCM Northeast Asia SNA TOPSIS

Übergeordnetes Werk:	Enthalten in: Maritime business review - Bingley : Emerald Publishing, 2016, 7(2022), 4, Seite 332-350
Übergeordnetes Werk:	volume:7 ; year:2022 ; number:4 ; pages:332-350

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1108/MABR-01-2022-0004

Katalog-ID:	1831435985

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982			\|2 26 \|1 00 \|x DE-206 \|b Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

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spelling	10.1108/MABR-01-2022-0004 doi (DE-627)1831435985 (DE-599)KXP1831435985 DE-627 ger DE-627 rda eng Phong Nha Nguyen verfasserin (DE-588)1270540033 (DE-627)181913136X aut Analyzing the international connectivity of the major container ports in Northeast Asia Phong Nha Nguyen and Hwayoung Kim 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Container port (dpeaa)DE-206 FCM (dpeaa)DE-206 Northeast Asia (dpeaa)DE-206 SNA (dpeaa)DE-206 TOPSIS (dpeaa)DE-206 Kim, Hwayoung verfasserin (DE-588)1210083167 (DE-627)169814380X aut Enthalten in Maritime business review Bingley : Emerald Publishing, 2016 7(2022), 4, Seite 332-350 Online-Ressource (DE-627)1005542163 (DE-600)2912394-X (DE-576)495690074 2397-3757 nnns volume:7 year:2022 number:4 pages:332-350 https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia Verlag kostenfrei https://doi.org/10.1108/MABR-01-2022-0004 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 4 332-350 26 01 0206 4249568660 x1z 18-01-23 2403 01 DE-LFER 4268087087 00 --%%-- --%%-- n --%%-- l01 07-02-23 2403 01 DE-LFER https://doi.org/10.1108/MABR-01-2022-0004 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia 26 00 DE-206 Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.
allfields_unstemmed	10.1108/MABR-01-2022-0004 doi (DE-627)1831435985 (DE-599)KXP1831435985 DE-627 ger DE-627 rda eng Phong Nha Nguyen verfasserin (DE-588)1270540033 (DE-627)181913136X aut Analyzing the international connectivity of the major container ports in Northeast Asia Phong Nha Nguyen and Hwayoung Kim 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Container port (dpeaa)DE-206 FCM (dpeaa)DE-206 Northeast Asia (dpeaa)DE-206 SNA (dpeaa)DE-206 TOPSIS (dpeaa)DE-206 Kim, Hwayoung verfasserin (DE-588)1210083167 (DE-627)169814380X aut Enthalten in Maritime business review Bingley : Emerald Publishing, 2016 7(2022), 4, Seite 332-350 Online-Ressource (DE-627)1005542163 (DE-600)2912394-X (DE-576)495690074 2397-3757 nnns volume:7 year:2022 number:4 pages:332-350 https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia Verlag kostenfrei https://doi.org/10.1108/MABR-01-2022-0004 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 4 332-350 26 01 0206 4249568660 x1z 18-01-23 2403 01 DE-LFER 4268087087 00 --%%-- --%%-- n --%%-- l01 07-02-23 2403 01 DE-LFER https://doi.org/10.1108/MABR-01-2022-0004 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia 26 00 DE-206 Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.
allfieldsGer	10.1108/MABR-01-2022-0004 doi (DE-627)1831435985 (DE-599)KXP1831435985 DE-627 ger DE-627 rda eng Phong Nha Nguyen verfasserin (DE-588)1270540033 (DE-627)181913136X aut Analyzing the international connectivity of the major container ports in Northeast Asia Phong Nha Nguyen and Hwayoung Kim 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Container port (dpeaa)DE-206 FCM (dpeaa)DE-206 Northeast Asia (dpeaa)DE-206 SNA (dpeaa)DE-206 TOPSIS (dpeaa)DE-206 Kim, Hwayoung verfasserin (DE-588)1210083167 (DE-627)169814380X aut Enthalten in Maritime business review Bingley : Emerald Publishing, 2016 7(2022), 4, Seite 332-350 Online-Ressource (DE-627)1005542163 (DE-600)2912394-X (DE-576)495690074 2397-3757 nnns volume:7 year:2022 number:4 pages:332-350 https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia Verlag kostenfrei https://doi.org/10.1108/MABR-01-2022-0004 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 4 332-350 26 01 0206 4249568660 x1z 18-01-23 2403 01 DE-LFER 4268087087 00 --%%-- --%%-- n --%%-- l01 07-02-23 2403 01 DE-LFER https://doi.org/10.1108/MABR-01-2022-0004 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia 26 00 DE-206 Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.
allfieldsSound	10.1108/MABR-01-2022-0004 doi (DE-627)1831435985 (DE-599)KXP1831435985 DE-627 ger DE-627 rda eng Phong Nha Nguyen verfasserin (DE-588)1270540033 (DE-627)181913136X aut Analyzing the international connectivity of the major container ports in Northeast Asia Phong Nha Nguyen and Hwayoung Kim 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Container port (dpeaa)DE-206 FCM (dpeaa)DE-206 Northeast Asia (dpeaa)DE-206 SNA (dpeaa)DE-206 TOPSIS (dpeaa)DE-206 Kim, Hwayoung verfasserin (DE-588)1210083167 (DE-627)169814380X aut Enthalten in Maritime business review Bingley : Emerald Publishing, 2016 7(2022), 4, Seite 332-350 Online-Ressource (DE-627)1005542163 (DE-600)2912394-X (DE-576)495690074 2397-3757 nnns volume:7 year:2022 number:4 pages:332-350 https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia Verlag kostenfrei https://doi.org/10.1108/MABR-01-2022-0004 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 4 332-350 26 01 0206 4249568660 x1z 18-01-23 2403 01 DE-LFER 4268087087 00 --%%-- --%%-- n --%%-- l01 07-02-23 2403 01 DE-LFER https://doi.org/10.1108/MABR-01-2022-0004 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/MABR-01-2022-0004/full/pdf?title=analyzing-the-international-connectivity-of-the-major-container-ports-in-northeast-asia 26 00 DE-206 Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.
language	English
source	Enthalten in Maritime business review 7(2022), 4, Seite 332-350 volume:7 year:2022 number:4 pages:332-350
sourceStr	Enthalten in Maritime business review 7(2022), 4, Seite 332-350 volume:7 year:2022 number:4 pages:332-350
format_phy_str_mv	Article
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institution	findex.gbv.de
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topic_facet	Container port FCM Northeast Asia SNA TOPSIS
sw_local_iln_str_mv	26: DE-206:
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container_title	Maritime business review
authorswithroles_txt_mv	Phong Nha Nguyen @@aut@@ Kim, Hwayoung @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.</subfield></datafield></record></collection>
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topic_title	26 00 DE-206 Purpose This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index. Design/methodology/approach This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM). Findings The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2. Originality/value The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM Analyzing the international connectivity of the major container ports in Northeast Asia Phong Nha Nguyen and Hwayoung Kim Container port (dpeaa)DE-206 FCM (dpeaa)DE-206 Northeast Asia (dpeaa)DE-206 SNA (dpeaa)DE-206 TOPSIS (dpeaa)DE-206
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title_short	Analyzing the international connectivity of the major container ports in Northeast Asia
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Analyzing the international connectivity of the major container ports in Northeast Asia

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Zugang & Verfügbarkeit

Vorhandene Bände

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