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This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.

We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.

The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.

The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.

This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.