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Flooding is China’s most frequent and catastrophic natural hazard, causing extensive damage. The aim of this study is to develop a comprehensive assessment of urban flood risk in the Hubei Province of China, focusing on the following three issues: (1) What are the factors that cause floods? (2) To what extent do these factors affect flood risk management? (3) How to build an effective comprehensive assessment system that can be used to reduce flood risk?

This study combines expert opinion and evidence from the extent literature to identify flood risk indicators across four dimensions: disaster risk, susceptibility, exposure and prevention and mitigation. The Criteria Importance Through Intercriteria Correlation (CRITIC) and the Grey Relational Analysis (RA)-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach were applied to calculate the weighting of factors and develop a model of urban flood risk. Then, ArcGIS software visualizes risk levels and spatial distribution in the cities of Hubei Province; uncertainty analysis verified method accuracy.

The results show that there are significant differences in the level of urban flood risk in Hubei Province, with cities such as Tianmen, Qianjiang, Xiantao and Ezhou being at high risk, while cities such as Shiyan, Xiangyang, Shennongjia, Yichang, Wuhan and Huanggang are at lower flood risk.

The innovative method of combining CRITIC-GRA-TOPSIS reduces the presence of subjective bias found in many other flood risk assessment frameworks. Regional data extraction and uncertainty analysis enhance result reliability, supporting long-term decision-making and urban planning. Overall, the methodological approach developed provides an advanced, highly effective and efficient analysis and visualization of flood risk. This study deepens the understanding of flood risk assessment mechanisms and more broadly supports the development of resilient cities.

Based on the technology affordance theory, this study aims to explore the relationship among artificial intelligence (AI) orientation, global value chain collaboration (collaboration breadth and collaboration depth) and the international performance of entrepreneurial firms while considering the contingency of board international experience.

This study’s sample was selected using the Sci-Tech Innovation Board (STAR Market) of the Shanghai Stock Exchange in China from 2019 to 2023, from which 1,928 final usable observations from 570 entrepreneurial firms over five years were obtained.

The empirical results indicate that AI orientation positively affects both collaboration breadth and collaboration depth of the global value chain. In addition, both collaboration breadth and collaboration depth mediate the relationship between AI orientation and the international performance of entrepreneurial firms, and board international experience enhances the positive effect of AI orientation on collaboration breadth.

This study contributes to the literature on AI orientation, global value chain and board international experience by introducing the technology affordance theory into the international performance of entrepreneurial firms, and it provides managerial implications for entrepreneurial firms and government policymaking.