Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText##

Research Article

Vol. 1 No. 3 (2025): International Journal of Multidisciplinary Research

Research on the Threshold Effect of Digital Transformation on Corporate Performance in Manufacturing Enterprises

DOI
https://doi.org/10.65231/ijmr.v1i3.109
Submitted
January 10, 2026
Published
2025-12-30

Abstract

In the digital economy era, as a pillar of China's economy, the manufacturing sector must pursue digital transformation, which is crucial to high-quality development and modernization goals. Using Shanghai and Shenzhen A-share manufacturing enterprises from 2015 to 2023 as samples, this paper explores the threshold effect of digital transformation on corporate performance, analyzes heterogeneity, relevant moderating and mediating roles, and provides suggestions for enterprises' transformation.

References

  1. Chen, X., Jiang, Y., & Xiong, Y. (2023). Research on the Impact Mechanism and Path of Digital Transformation on Corporate Performance. Reform of Economic System, (02), 112-120.
  2. Fu, Y., Xu, Q., & Lin, S. (2021). The Impact of Incumbent Firms’ Process Digitalization on Innovation Performance: The Moderating Role of Organizational Inertia. R&D Management, 33(01), 78-89. https://doi.org/10. 13581/j.cnki.r d m .2 0202204
  3. Zhao, S. J., Yi, L. F., & Lian, Y. L. (2021). Entrepreneurial Leadership, Organizational Resilience, and New Venture Performance. Foreign Economics & Management, 43(03), 42-56. https://doi.org/10.16538/j.cnki.fem.20201229.101
  4. Jiang, L., Ling, Y. P., Zhang, J. C., et al. (2022). How Does Digital Transformation Affect Corporate Resilience? From the Perspective of Ambidextrous Innovation. Technology Economics, 41(01), 1-11.
  5. Fan, H. J., & Pan, N. N. (2024). Digital Transformation, Agile Responsiveness, and Corporate Resilience. Economic Management, 46(07), 36 -54. https://doi.or g/10.19616/j.cnki. bmj.2024.07.003
  6. Dai, X. Y., & Cheng, L. W. (2013). A Threshold Effect Study on the Impact of R&D Investment Intensity on Corporate Performance. Studies in Science of Science, 31(11), 1708-1716+1735. https://doi.org/10.16192/j.cnki.1003-2053.2013.11.015
  7. Han, X. F., & Dong, M. F. (2018). The Threshold Effect of R&D Investment on Corporate Performance. Journal of Beijing Institute of Technology (Social Sciences Edition), 20(02),95-101+116. https://doi.org/10.15918/j.jbitss1009-3370.2018.1185
  8. Qi, R. (2025). AUBIQ: A Generative AI-Powered Framework for Automating Business Intelligence Requirements in Resource-Constrained Enterprises. Frontiers in Business and Finance, 2(01), 86-88.
  9. Qi, R. (2025). Enterprise Financial Distress Prediction Based on Machine Learning and SHAP Interpretability Analysis.
  10. Qi, R. (2025, July). DecisionFlow for SMEs: A Lightweight Visual Framework for Multi-Task Joint Prediction and Anomaly Detection. In Proceedings of the 2025 International Conference on Economic Management and Big Data Application (pp. 899-903).
  11. Zhang, T. (2025). A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement. Frontiers in Business and Finance, 2(02), 247-261.
  12. Zhang, T. (2025). From Black Box to Actionable Insights: An Adaptive Explainable AI Framework for Proactive Tax Risk Mitigation in Small and Medium Enterprises.
  13. Yi, X. (2025). Federated Incentive Learning: A Privacy-Preserving Framework for Ad Monetization and Creator Rewards in High-Concurrency Environments. American Journal Of Big Data, 6(3), 60-88.
  14. Li, B. (2025). From Maps to Decisions: A GeoAI Framework for Multi-Hazard Infrastructure Resilience and Equitable Emergency Management. American Journal Of Big Data, 6(3), 139-153.
  15. Tian, Y., Xu, S., Cao, Y., Wang, Z., & Wei, Z. (2025). An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection. Mathematics, 13(13), 2088.
  16. Xu, S., Cao, Y., Wang, Z., & Tian, Y. (2025, June). Fraud Detection in Online Transactions: Toward Hybrid Supervised-Unsupervised Learning Pipelines. In Proceedings of the 2025 6th International Conference on Electronic Communication and Artificial Intelligence.
  17. Qi, R. (2025). DecisionFlow for SMEs: A Lightweight Visual Framework for Multi-Task Joint Prediction and Anomaly Detection.