Discovering Sustainable Business Partnerships through a Deep Learning Approach to Maximize Potential Value

• Published in: Sustainability Vol. 15; no. 22; p. 15885
• Main Authors: Lee, Donghun, Kim, Jongeun, Song, Seokwoo, Kim, Kwanho
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Data envelopment analysis; Decision trees; Deep learning; Machine learning; Neural networks; Partnership; Partnerships; Social network analysis; Social networks; Suppliers; Supply chains; Support vector machines; Sustainability; South Korea
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su152215885

Discovering sustainable business partnerships is crucial for small and medium-sized companies, where they can realize potential value through operational resources and abilities. Prior studies have mostly focused on predicting and developing new business partners using various machine learning techniques or social network analyses. However, effectively estimating potential benefits from business partnerships is much more valuable to companies. Therefore, this study proposes a method which combines deep learning and network analyses to estimate the potential value of business partnerships for companies. To demonstrate the effectiveness of the proposed method, we expand business partnerships between companies and assess potential value derived from the parenthesis using business transaction data collected from the Republic of Korea. The results suggest that companies can gain more potential value from extended networks when compared to previous ones. Furthermore, potential value results show clear distinctions between industries. Our findings provide evidence that small and medium-sized companies can experience significant benefits by establishing adequate business partnerships.