Risk Transmission Mechanisms and Risk Mitigation Pathways in Cross-border Technology Investment: Evidence From the China–U.S. Market
DOI:
https://doi.org/10.70393/6a696574.333838ARK:
https://n2t.net/ark:/40704/JIET.v1n1a05Disciplines:
Computer ScienceSubjects:
Risk Transmission MechanismReferences:
25Keywords:
Cross-border Technology Investment, Risk Transmission Mechanism, Risk Mitigation Pathways, China–U.S. Market, Empirical Analysis, Institutional DistanceAbstract
Against the backdrop of accelerating cross-border mobility of global technological factors, the scale of China–U.S. cross-border technology investment has continued to expand, yet the investment failure rate reaches 37.2%, significantly higher than that of domestic technology investment (18.5%). Addressing key challenges such as multidimensional risk complexity, covert transmission pathways, and high difficulty of prevention and control, this study draws on risk transmission theory, technology diffusion theory, and institutional distance theory to develop a four-dimensional risk identification framework covering technology, market, institution, and finance. Using a dataset of 486 China–U.S. cross-border technology investment projects from 2018 to 2023, we empirically reveal a three-stage risk transmission mechanism characterized by “chain triggering–network diffusion–compound amplification.” The results show that institutional risk is the core initial risk (contribution rate 38.6%), transmitting through a chain of “institutional differences → impeded technological adaptation → market entry barriers → tightened funding chains.” Cross-transmission between technology risk and market risk increases the probability of investment failure by 2.3 times. Furthermore, the strength of intellectual property protection and the signing status of bilateral investment treaties exert significant moderating effects on risk transmission, with moderation coefficients of −0.27 and −0.31, respectively. Based on the empirical findings, this paper proposes targeted solutions from three perspectives—risk early warning, pathway blocking, and collaborative governance—providing theoretical support and practical guidance for reducing risk losses in China–U.S. cross-border technology investment.
References
[1] Bao, K., & Xia, Y. (2026). Country risk and investment efficiency in China’s overseas expansion: the buffering effect of digital transformation. Applied Economics Letters, 1-9.
[2] Kumar, P., & Singh, V. K. (2024). Cross-border sovereign risk transmission in BRICIT Nations: Unveiling asymmetries and the role of country risk premiums. Borsa Istanbul Review, 24(6), 1263-1274.
[3] Qi, Z. (2025). Root Cause Tracing Algorithm and One-Click Repair Mechanism for Medical Server Failures. Journal of Progress in Engineering and Physical Science, 4(5), 43-48.
[4] Goldberg, I. (2008). Globalization and technology absorption in Europe and Central Asia: the role of trade, FDI, and cross-border knowledge flows (No. 150). World Bank Publications.
[5] Yin, M. (2025). Predictive Maintenance of Semiconductor Equipment Using Stacking Classifiers and Explainable AI: A Synthetic Data Approach for Fault Detection and Severity Classification. Journal of Industrial Engineering and Applied Science, 3(6), 36-46.
[6] Wang, H., Li, Q., & Liu, Y. (2022). Regularized Buckley–James method for right‐censored outcomes with block‐missing multimodal covariates. Stat, 11(1), e515.
[7] Wang, H., Li, Q., & Liu, Y. (2023). Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.
[8] Sun, Y., & Ortiz, J. (2024). An ai-based system utilizing iot-enabled ambient sensors and llms for complex activity tracking. arXiv preprint arXiv:2407.02606.
[9] Chen, Y. (2025). Generative Diffusion Models for Option Pricing: A Novel Framework for Modeling Volatility Dynamics in US Financial Markets. Journal of Industrial Engineering and Applied Science, 3(6), 23-29.
[10] Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
[11] Yin, M. (2025). Physics-Regularized Self-Supervised Anomaly Detection for Semiconductor Tools with Digital Twin Guidance.
[12] Qi, Z. (2025). Design of a Medical IT Automated Auditing System Based on Multiple Compliance Standards. Innovation in Science and Technology, 4(9), 17-23.
[13] Chen, Y. (2025). Artificial Intelligence in Economic Applications: Stock Trading, Market Analysis, and Risk Management. Journal of Economic Theory and Business Management, 2(5), 7-14.
[14] Afzal, F., Pan, H., Afzal, F., & Gul, R. F. (2024). Analyzing risk contagion and volatility spillover across multi-market capital flow using EVT theory and C-vine Copula. Heliyon, 10(21).
[15] Yin, M., & Frank, L. F. (2026). Multi-Modal Fusion for Yield Optimization: Integrating Wafer Maps, Metrology, and Process Logs with Graph Models. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(1), 45-60.
[16] Lin, A. (2025). Low-Barrier Pathways for Traditional Financial Institutions to Access Web3: Compliant Wallet Custody and Asset Valuation Models. Frontiers in Management Science, 4(6), 80-86.
[17] Inkpen, A. C., Sundaram, A. K., & Rockwood, K. (2000). Cross-border acquisitions of US technology assets. California Management Review, 42(3), 50-71.
[18] Qi, Z. (2025). Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture. Journal of Innovations in Medical Research, 4(5), 13-18.
[19] Singal, A. K., & Ahsan, F. M. (2023). The relationship between cross-border acquisitions and R&D investments by Indian firms–substituting or complementing. International Journal of Emerging Markets, 18(9), 2680-2697.
[20] Sharma, D. N., Gautam, A. K., & Kumar, P. (2025). Investment-at-Risk under Geopolitical Uncertainty in BRICS Countries: An Integrated Theoretical Framework on Risk Transmission, Institutional Vulnerability, and Strategic Investment Behavior.
[21] Bertoni, F., & Groh, A. P. (2022). The benefit of cross-border investments in the chinese emerging venture capital and private equity market. Finance, 43(3), 3-36.
[22] Chen, Y., & Xu, J. (2026). Deep Learning for US Bond Yield Forecasting: An Enhanced LSTM–LagLasso Framework. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 61-75.
[23] Espagne, E., Oman, W., Mercure, J. F., Svartzman, R., Volz, U., Pollitt, H., ... & Campiglio, E. (2023). Cross-border risks of a global economy in mid-transition (Vol. 184). International Monetary Fund.
[24] Yoon, H., & Lee, J. J. (2016). Technology-acquiring cross-border M&As by emerging market firms: role of bilateral trade openness. Technology Analysis & Strategic Management, 28(3), 251-265.
[25] Clougherty, J. A., Kim, J. U., Skousen, B. R., & Szücs, F. (2017). The foundations of international business: Cross‐border investment activity and the balance between market‐power and efficiency effects. Journal of Management Studies, 54(3), 340-365.
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