Text analysis demonstrates how supply chain risks inhibit global expansion in manufacturing firms, suggesting mitigation strategies.
Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected supply chain vulnerabilities, mitigating the adverse impact of supply chain risks on firms’ overseas market expansion has become a critical research and practical issue in the field of operational and supply chain risk management. Based on the textual analysis of annual reports of listed firms, this study constructs a systematic supply chain risk measurement indicator system through standardized text preprocessing, multi-dimensional feature keyword lexicon construction, context co-occurrence frequency calculation and so on. We further validate the effectiveness of the indicator system by comparing its trend with the global economic uncertainty index, confirming that it can capture firm-specific supply chain risk information effectively. Employing text analysis, this study constructs a systematic supply chain risk measurement indicator system for A-share manufacturing firms and empirically verifies that elevated supply chain risks significantly constrain their overseas market expansion. Three interrelated operational mechanisms, namely surging operating costs, tightened financing constraints, and slumping R&D investments, drive this inhibitory effect. Notably, firms can effectively offset this negative effect by broadening overseas operational scope and intensifying overseas digital and technological innovation. Heterogeneity analyses further reveal that the inhibitory effect is more pronounced for five types of firms: those with lower overseas revenue, located in less market-oriented regions, operating in upstream value chain sectors, with lower current liabilities, and with a lower degree of digital transformation.
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Wang et al. (2026) studied this question.
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