The sweet spot: supply chain concentration and innovation in manufacturing enterprises
Xue Lei & Xueguo Xu
Abstract
Purpose This study aims to investigate the relationship between supply chain concentration and firm innovation in manufacturing enterprises, examining whether this relationship follows a curvilinear pattern and how entrepreneurial orientation moderates these dynamics across supplier and customer contexts. Design/methodology/approach Using panel data from Chinese manufacturing listed companies (2013–2023), this research employs nonlinear regression models and the system generalized method of moments estimation to establish causal relationships. Entrepreneurial orientation dimensions are captured through natural language processing-based text analysis of annual reports. Findings Supply chain concentration exhibits an inverted U-shaped relationship with firm innovation, with moderate concentration optimizing innovation performance. Proactiveness positively moderates this relationship in supplier contexts but negatively in customer contexts, while risk-taking shows opposite patterns – enhancing innovation benefits in supplier relationships but constraining them in customer relationships. Practical implications Manufacturing managers should calibrate concentration levels strategically rather than pursuing simple maximization, aligning supply chain structure with entrepreneurial orientation to optimize innovation outcomes. Originality/value This study integrates the adaptive cycle framework with resource dependence theory and organizational learning theory to explain curvilinear concentration-innovation dynamics. It extends entrepreneurial orientation research by revealing asymmetric moderation effects across supply chain tiers, demonstrating that strategic orientations interact differently with upstream versus downstream relationships.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.