Abstract
Increasing stakeholder pressure to hold firms accountable for the sustainability performance of their supply networks creates a need to select sustainable suppliers and minimize associated risks. Firms increasingly use third-party sustainability performance information to screen and assess potential and existing suppliers. However, such third-party assessments are structurally limited and often unavailable for some suppliers, leaving firms without external sustainability information for them. Supply network-level characteristics are an overlooked factor for predicting supplier sustainability performance. Using a large-scale dataset of 792 firms embedded in 291 supply networks up to tier three, we develop a predictive model to estimate the sustainability performance based on firm- and supply network-level characteristics. Our analysis indicates that including a set of supply network-level characteristics significantly improves predictive performance, reducing the prediction error by 8.83%. We further show that considering a firm’s embeddedness within the supply network significantly reduces the prediction error, while surprisingly, its control within the supply network has no significant influence. Our study adds to the literature on supply networks, sustainability, and sustainability performance prediction by offering new insights regarding the predictive power of supply network-level characteristics and the relative importance of firm-level characteristics. We also provide managerial implications for supplier selection, monitoring, and risk management in supply networks.
| Original language | English |
|---|---|
| Article number | 110110 |
| Journal | International Journal of Production Economics |
| Volume | 299 |
| Early online date | 17 Jun 2026 |
| DOIs | |
| Publication status | E-pub ahead of print - 17 Jun 2026 |
Data Availability Statement
The authors do not have permission to share data.Acknowledgements
The authors would like to thank Jérémie Mugnier for his valuable research assistance. He assisted with the data collection and the development of an early version of the predictive model. We gratefully acknowledge his support.Keywords
- ESG scores
- Predictive analytics
- Supply networks
- Sustainability performance
- XGBoost
ASJC Scopus subject areas
- General Business,Management and Accounting
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
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