Risk governance, institutional uncertainty and the choice of investment modes: a behavioural decision analysis of the East coast rail link project in Malaysia

Cross-border infrastructure investment is a critical pillar of Belt and Road cooperation, yet its success is often constrained by the host country’s complex institutional environment, policy uncertainties, and multidimensional risks. How to make a systematic choice of investment mode under such uncertainties constitutes a crucial problem of risk governance. Taking the Malaysia East Coast Rail Link (ECRL) project as a case study, this research develops a behavioural decision-analysis framework by integrating Prospect Theory (PT), Interval-Valued T-Spherical Fuzzy Sets (IVTSFS), and the VIKOR compromise-ranking method. This framework is designed to capture decision-makers’ psychological preferences under risk and to handle the inherent fuzziness in expert evaluations. We assess four feasible investment modes: joint venture, wholly owned subsidiary, acquisition, and state-owned participation. The results suggest that the joint venture arrangement performs most favourably under the proposed framework and shows the lowest overall risk exposure, while the wholly owned subsidiary remains an acceptable compromise solution under alternative emphases on group utility and individual regret. The state-owned participation mode shows the highest risk exposure among the four alternatives. Using a combined subjective-objective weighting method (G1-CRITIC), household purchasing power, corruption and administrative integrity, and environmental regulation stringency are identified as the three most influential risk drivers in this case. These findings suggest that, in the ECRL case, governance models characterised by risk-sharing and local embeddedness may offer greater resilience under institutional uncertainty. The study provides a structured decision-support tool for investors in complex institutional settings and offers risk-governance implications for improving cooperation models, sustainability, and accountability in Belt and Road infrastructure projects.