An analysis on strategy evolution of research & development in cooperative innovation network of new energy vehicle within policy transition period

•A novel framework for using optimal research & development (R&D) investment to derive the innovation motivation of the new energy vehicle (NEV) industry is constructed.•An industry game model is established to explore dynamic influence of policy transition imposed on the R&D strategy evolution of the innovation entities.•Considering the difference of R&D preference, seven decision-making scenarios of innovative entities are proposed.•The rate of subsidy reduction should be regulated, and dual-credit policy should be promoted steadily and actively.•The cooperative innovation network exists an optimal connectivity that maximizes the proportion of the suppliers' R&D strategy. Insufficient motivation and investment in research and development (R&D) will hamper further developing of the new energy vehicle (NEV) industry. In response to this dilemma, the Chinese government has successively issued the subsidy and dual-credit policies. This study aims at exploring the influence mechanism of the policy transition imposed on the R&D strategy evolution in NEV cooperative innovation network, thus constructing a two-level game model accordingly. Specifically, the inversion method is used a posteriori in the first level to address suppliers' optimal R&D investment. Then, the optimal R&D investment and patent data are used in the second level to construct the network game model, thereby analyzing the influences of the policy transition and of network characteristics on suppliers' R&D strategy evolution. Simulation results suggest that within the subsidy reduction stage, suppliers prefer to choose independent R&D strategy. To summarize, the implementation of the dual-credit policy can reduce the heavy dependence of suppliers' technology R&D upon subsidies while increasing the proportion of suppliers choosing cooperative R&D. In the NEV cooperative innovation network, the evolution of suppliers' R&D strategy can be effectively promoted via not only the controlling over the optimal network clustering coefficient but also the influence of the key nodes within.