Revealing the Dual Impact of Data Elements on Supply Chain Risk via Supply Chain Configuration
研究数据要素与供应链风险之间的U型关系，发现过度依赖数据要素反而增加风险，并通过供应链配置、企业创新和人工智能采用等路径产生异质性影响，为企业风险管理提供实证依据。
Data elements (DE) refer to economic resources owned or controlled by firms that can generate economic value. China is the first country to formally recognize data as a factor of production and has actively encouraged firms to leverage DE to enhance their supply chain risk (SCR) management capabilities. However, whether DE can consistently and effectively mitigate SCR remains contentious, and it remains unclear whether the strategic choices of core firms toward suppliers and customers can continuously play a role in data-driven risk management. To address these questions, this study develops a nonlinear analytical framework that links DE, supply chain configuration (SCC), and SCR. By constructing a panel simultaneous equation model, this study systematically investigates the heterogeneous transmission pathways through which DE influences SCR from the holistic supply chain and unilateral perspectives. The results reveal a significant U-shaped relationship between DE and SCR, indicating a “too much of a good thing” effect when firms excessively rely on DE. This finding remains robust across a series of rigorous robustness checks. Moreover, as DE indirectly affects SCR by influencing SCC, the factors of enterprise innovation and artificial intelligence adoption act as “shock absorbers” that moderate this indirect pathway, albeit with notable differences between the holistic supply chain perspective and the unilateral perspective. Finally, the study finds that state-owned enterprises and firms located in cities with data trading platforms can achieve earlier optimization of their supply chain structures, thereby influencing risk transmission. However, data trading platforms also amplify the intensity of risk transmission under highly concentrated SCC. Overall, this study extends theoretical understanding of DE in the field of supply chain management, uncovers the nonlinear transmission mechanism through which DE affects SCR, and provides empirical evidence to guide firms in leveraging DE for SCR management.