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

FM Song,

Yang Huang, Chengkun Liu

и другие.

Systems, Год журнала: 2025, Номер 13(4), С. 292 - 292

Опубликована: Апрель 16, 2025

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

Язык: Английский

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

FM Song,

Yang Huang, Chengkun Liu

и другие.

Systems, Год журнала: 2025, Номер 13(4), С. 292 - 292

Опубликована: Апрель 16, 2025

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

Язык: Английский

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