Affiliation:
1. 1 Xinxiang Vocational and Technical College , Xinxiang , Henan , , China .
Abstract
Abstract
In the digital economy era, digital transformation aligns with sustainable development and national policies. This research examines enterprise economic output through human and physical capital driven by digital transformation. Utilizing the TrAdaBoost algorithm in transfer learning, we predict economic risks for enterprises. We propose an optimization path for digital economic management systems. Furthermore, we empirically analyze the current financial management and challenges of digital transformation at Company X, laying the groundwork for identifying and analyzing digital transformation risks. Our findings indicate the significant influence of risk-free interest rate (0.1781), GDP (0.1732), and money supply (0.1668) on enterprise economic risk, guiding enterprises to mitigate these impacts during digital transformation.
Reference20 articles.
1. Ernest, & Hughes. (2017). Shaping the digital enterprise: trends and use cases in digital innovation and transformation. Computing Reviews.
2. Cheng, L. (2022). Decision modeling and evaluation of enterprise digital transformation using data mining. Mobile information systems(Pt.27), 2022.
3. Liu, C., & Yang, Z. (2022). Key factors identification and path selection of enterprise digital transformation under multicriteria interaction. Mathematical Problems in Engineering: Theory, Methods and Applications(Pt.44), 2022.
4. Li, R., Rao, J., & Wan, L. (2022). The digital economy, enterprise digital transformation, and enterprise innovation. Managerial and Decision Economics, 43.
5. Yiping, L. (2021). Innovation strategy driven by enterprise digital transformation. Francis Academic Press(9).