The research on the mechanism of risk transmission in new energy vehicle supply chains from the perspective of hypernetworks

Abstract

Purpose The complex characteristics of the new energy vehicle supply chain – including its multistakeholder nature, high interdependence and group interactions – make it difficult for traditional risk analysis methods to effectively characterize the internal risk propagation processes within it. Design/methodology/approach This study uses hypernetwork theory to construct a three-layer UAU-NYN-SEIS risk propagation model, depicting information dissemination, behavioral decision-making and risk diffusion processes, respectively. Combined with microlevel Markov chain methods, numerical simulations are conducted. Based on actual network data from China’s new energy vehicle supply chain, the impact of key parameters on risk propagation is analyzed under different information network structures (BA scale-free and ER random hypernetworks). Findings Risk propagation is jointly influenced by multiple factors including information network structure, corporate decision-making behavior and herd mentality: Information dissemination significantly elevates the risk propagation threshold and suppresses infection scale, while information decay weakens the timeliness of prevention and control measures; herd mentality drives collective prevention in homogeneous information networks, whereas in heterogeneous networks it relies on the information dominance of core nodes; increased contagiousness during the incubation period exacerbates risk diffusion. Originality/value This study extends hypernetwork theory from the field of public health to the study of risk propagation in the new energy vehicle supply chain. By characterizing the interaction structures of higher-order groups – such as battery consortia and joint R&D alliances – it overcomes the limitation of existing multilayer coupling models, in which each layer is based on a pairwise structure and reveals the cross-layer coupling mechanisms among information, behavior and risk.

The authors' abstract, as published at the source. Journal of Modelling in Management, 2026 · DOI ↗