Abstract:

[Purpose/Significance] In the digital age, data elements have become a key factor in production, while insufficient bargaining power in the supply chain poses significant operational risks to enterprises. How to leverage the opportunities of the digital economy, maximize the role of data elements, and avoid operational risks caused by insufficient discourse power in the supply chain has become a key issue that enterprises urgently need to address. Investigating how data utilization enhances this power is vital for building resilient supply chains and informing governance decisions. This method is also effective for further utilizing data elements. It provides micro evidence that helps us understand how data elements can optimize resource allocation and empower organizational decision making. [Method/Process] This study employs a rigorous, empirical approach using panel data from China's A-share listed companies from 2003 to 2022. A two-way fixed effects model serves as the primary estimator to control for unobserved heterogeneity. To credibly address potential endogeneity issues, such as reverse causality and sample selection bias, we implemented a comprehensive identification strategy. This methodology incorporates the use of instrumental variables, Heckman's two-stage correction model, and a series of robustness checks including alternative variable constructions and sub-sample analyses. Furthermore, we conducted mechanism analysis to elucidate the transmission channels and heterogeneity analysis to examine conditional effects across different types of firms. [Results/Conclusions] The empirical results demonstrate that the improvement of data element utilization level can effectively strengthen a firm's supply chain bargaining power and reduce the dependence of enterprises on large suppliers and customers, enhance their bargaining power and influence in the supply chain. This conclusion still holds true after robustness tests such as replacing the regression model, adding control variables, and adjusting the sample period. Mechanism analysis results indicate that the utilization level of data elements primarily empowers supply chain discourse through two channels: improving supply chain efficiency and alleviating financing constraints. Firstly, data elements optimize the inventory management, logistics scheduling, and supply chain collaboration of enterprises, improving operational efficiency and reducing dependence on key suppliers and customers. Secondly, data elements improve the information transparency of enterprises, reduce external financing costs, enhance the liquidity of funds, and make them more autonomous and bargaining power in supply chain transactions. A heterogeneity analysis revealed significant differences in the empowering effects of data elements among different types of enterprises. Among them, data elements have a more significant effect on enhancing the discourse power of supply chain for non-labor-intensive and non-asset-intensive enterprises, as well as a stronger promotional effect on non-technology-intensive and non-high-tech industry enterprises. This suggests that companies that rely less on traditional physical resources are better able to use data to gain a competitive advantage. This study establishes a robust theoretical basis for data-driven supply chain management and presents significant policy implications. One limitation is its focus on listed companies. Future research could expand this inquiry to include small and medium-sized enterprises and global supply chain contexts.

Key words: data element, supply chain discourse power, supply chain efficiency, financing constraints, supply chain resilience