Author
Abstract
In the rapidly evolving landscape of the global economy, the advent of the digital era (DE) has ushered in unprecedented transformations, reshaping traditional business paradigms and challenging established governance models. One of the most revolutionary facets of this digital revolution is the integration of neural networks (NN), which have become effective instruments for deciphering complex patterns and optimizing decision-making processes (DMP). This quantitative study investigates the influence mechanism of the digital economy (IMDE), specifically the integration of NNs, on the corporate governance (CG) model. Employing a questionnaire survey, data was collected from 347 professionals to explore the impact of NN technologies on DMP, Digitalization Levels, Digital Capabilities, transparency, accountability, trust, and the role of regulatory frameworks in CG. The study establishes a significant enhancement in DMP by integrating NN technologies into CG models. The research reveals a favorable correlation between a business’s level of digitalization and its adoption of NN-based CG models. The novelty of this study lies in its comprehensive exploration of the multifaceted impact of NN technologies on CG in the digital economy (DE). By examining the technological aspects, Digitalization Levels, capabilities, and regulatory landscape, the study comprehensively comprehends the dynamic interplay involving the DE and CG. The findings offer valuable insights for businesses, policymakers, and researchers seeking to navigate the evolving landscape of digital technologies in CG. The study highlights that sectors driven by NN technology experience a more significant impact in the DE than traditional industries.
Suggested Citation
Zhen Wang & Haoyang Wu, 2025.
"Research on the Influence Mechanism of Digital Economy Based on Neural Networks on Corporate Governance Model,"
Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 10104-10135, June.
Handle:
RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02287-z
DOI: 10.1007/s13132-024-02287-z
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02287-z. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.