Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization

Corporate financial resilience and its integration with institutional reforms play a crucial role in promoting organizational sustainability in the digital economy. Previous research has predominantly focused on internal determinants of corporate financial resilience. However, it has paid limited attention to the role of external institutional factors. This gap is particularly evident in the context of data factor marketization (DFM). We addressed this gap by investigating the impact of DFM on corporate financial resilience, drawing on resource dependence theory (RDT) to highlight the importance of the external policy environment and inter-organizational resource exchange. We employed a double machine learning (DML) framework to assess corporate financial resilience using comprehensive panel data from Chinese listed firms. This approach overcomes the limitations of traditional econometric methods by allowing nonlinear interactions and high-dimensional controls. The results show that DFM significantly enhances corporate financial resilience, with its impact varying across different institutional contexts. Additionally, firm characteristics moderate this relationship. Specifically, ownership structure strengthens or weakens the positive effect of DFM, while industry competition and geographical location have varying effects on resilience outcomes. We offered novel theoretical insights and practical guidance for policymakers seeking to leverage institutional reforms to enhance financial resilience within an increasingly volatile and uncertain business landscape.