Impact of Network Structures and Deep Learning on Financial Performance in Buyer-Supplier Networks

被引:0
|
作者
Song, Seokwoo [1 ]
Kim, Jongeun [2 ]
Kim, Kwanho [3 ]
Kim, Jae-Gon [4 ]
Lee, Donghun [5 ,6 ]
机构
[1] Weber State Univ, Management Informat Syst, Ogden, UT USA
[2] Incheon Natl Univ, Ind & Management Engn, Incheon, South Korea
[3] Dongguk Univ, Dept Ind & Syst Engn, Seoul, South Korea
[4] Incheon Natl Univ, Dept Ind & Management Engn, Incheon, South Korea
[5] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON, Canada
[6] Adv Inst Convergence Technol, Suwon, South Korea
基金
新加坡国家研究基金会;
关键词
Buyer-Supplier Network; Deep Learning; Financial Performance; Network Analysis; CREDIT RATINGS; CHAIN FINANCE; PARTNER SELECTION; NEURAL-NETWORK; COLLABORATION; MANAGEMENT; CENTRALITY; INNOVATION; CRITERIA; CREATION;
D O I
10.4018/JGIM.370963
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
This study investigates the impact of network capabilities and deep learning techniques on companies' financial performance within buyer-supplier networks. It broadens the scope by incorporating network measures such as closeness and network constraint, whereas previous studies have primarily focused on suitable buyer-supplier relationships. The analysis evaluates the effects of these network measures on companies' financial performance metrics, including asset growth rate, return on assets, and more. In addition, this study explores the impact of extended networks on company performance using deep learning techniques. The results show that companies' network capabilities are positively associated with their financial performance, highlighting the critical role of network positions in driving success. Furthermore, the findings suggest that extending the network through deep learning offers significant benefits for companies.
引用
收藏
页数:25
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