Impact of Network Structures and Deep Learning on Financial Performance in Buyer-Supplier Networksopen access
- Authors
- Song, Seokwoo; Kim, Jongeun; Kim, Kwanho; Kim, Jae-Gon; Lee, Donghun
- Issue Date
- 2025
- Publisher
- IGI Global Scientific Publishing
- Keywords
- Buyer-Supplier Network; Deep Learning; Financial Performance; Network Analysis
- Citation
- Journal of Global Information Management, v.33, no.1, pp 1 - 25
- Pages
- 25
- Indexed
- SSCI
SCOPUS
- Journal Title
- Journal of Global Information Management
- Volume
- 33
- Number
- 1
- Start Page
- 1
- End Page
- 25
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/58096
- DOI
- 10.4018/JGIM.370963
- ISSN
- 1062-7375
1533-7995
- Abstract
- 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. © 2025 IGI Global. All rights reserved.
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- Appears in
Collections - College of Engineering > Department of Industrial and Systems Engineering > 1. Journal Articles

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