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 at...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/4/292 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850154779253145600 |
|---|---|
| author | Fangzhou Song Yang Huang Chengkun Liu |
| author_facet | Fangzhou Song Yang Huang Chengkun Liu |
| author_sort | Fangzhou Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d0ee3411d897420cb892451d90a507a0 |
| institution | OA Journals |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-d0ee3411d897420cb892451d90a507a02025-08-20T02:25:12ZengMDPI AGSystems2079-89542025-04-0113429210.3390/systems13040292Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor MarketizationFangzhou Song0Yang Huang1Chengkun Liu2The Institute for Sustainable Development, Macau University of Science and Technology, Macao 999078, ChinaThe Institute for Sustainable Development, Macau University of Science and Technology, Macao 999078, ChinaThe Institute for Sustainable Development, Macau University of Science and Technology, Macao 999078, ChinaCorporate 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.https://www.mdpi.com/2079-8954/13/4/292corporate financial resiliencedata factor marketizationdouble machine learninginstitutional environmentresource dependence |
| spellingShingle | Fangzhou Song Yang Huang Chengkun Liu Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization Systems corporate financial resilience data factor marketization double machine learning institutional environment resource dependence |
| title | Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization |
| title_full | Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization |
| title_fullStr | Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization |
| title_full_unstemmed | Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization |
| title_short | Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization |
| title_sort | extended application of double machine learning in corporate financial resilience research based on data factor marketization |
| topic | corporate financial resilience data factor marketization double machine learning institutional environment resource dependence |
| url | https://www.mdpi.com/2079-8954/13/4/292 |
| work_keys_str_mv | AT fangzhousong extendedapplicationofdoublemachinelearningincorporatefinancialresilienceresearchbasedondatafactormarketization AT yanghuang extendedapplicationofdoublemachinelearningincorporatefinancialresilienceresearchbasedondatafactormarketization AT chengkunliu extendedapplicationofdoublemachinelearningincorporatefinancialresilienceresearchbasedondatafactormarketization |