Research on the Long-Term Mechanism of Digital Transformation in High-End Equipment Manufacturing Based on a Four-Party Evolutionary Game

The digital transformation of high-end equipment is not only a critical means to enhance national core competitiveness, but also a necessary requirement within the framework of national development strategy. Major stakeholders in this transformation include local governments, high-end equipment manufacturers, financial institutions, and industrial technology platforms, all of whose interactions significantly influence the transformation process. This paper constructs a four-party evolutionary game model involving local governments, high-end equipment manufacturers, financial support institutions, and industrial technology platforms. Numerical simulations are conducted to analyze the stable strategies and evolutionary trends of these four players under various parameters, while also exploring the long-term mechanisms for the digital transformation of high-end equipment facilitated by government subsidies. The results indicate that in the initial stage of digital transformation, the government assumes a leading role by implementing high-subsidy policies to encourage participation from manufacturers, financial institutions, and technology platforms. As the transformation progresses into a stable promotion phase, the government gradually reduces subsidies to a normal level and increasingly relies on market mechanisms to foster active engagement. Both models represent ideal scenarios for the digital transformation of high-end equipment. Finally, this paper offers relevant policy recommendations aimed at enhancing policy guidance, stimulating the motivation of market entities, and improving the benefit linkage mechanism among all four stakeholders.

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Published
2026-05-19
DOI
https://doi.org/10.3390/info17050502
Primary Topic
Innovation Diffusion and Forecasting
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Research on the Long-Term Mechanism of Digital Transformation in High-End Equipment Manufacturing Based on a Four-Party Evolutionary Game

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Innovation Diffusion and Forecasting
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Research on the Long-Term Mechanism of Digital Transformation in High-End Equipment Manufacturing Based on a Four-Party Evolutionary Game

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Abstract

The digital transformation of high-end equipment is not only a critical means to enhance national core competitiveness, but also a necessary requirement within the framework of national development strategy. Major stakeholders in this transformation include local governments, high-end equipment manufacturers, financial institutions, and industrial technology platforms, all of whose interactions significantly influence the transformation process. This paper constructs a four-party evolutionary game model involving local governments, high-end equipment manufacturers, financial support institutions, and industrial technology platforms. Numerical simulations are conducted to analyze the stable strategies and evolutionary trends of these four players under various parameters, while also exploring the long-term mechanisms for the digital transformation of high-end equipment facilitated by government subsidies. The results indicate that in the initial stage of digital transformation, the government assumes a leading role by implementing high-subsidy policies to encourage participation from manufacturers, financial institutions, and technology platforms. As the transformation progresses into a stable promotion phase, the government gradually reduces subsidies to a normal level and increasingly relies on market mechanisms to foster active engagement. Both models represent ideal scenarios for the digital transformation of high-end equipment. Finally, this paper offers relevant policy recommendations aimed at enhancing policy guidance, stimulating the motivation of market entities, and improving the benefit linkage mechanism among all four stakeholders.

InformationVol. 17(5)
National Defense University (US), National University of Defense Technology (CN)
Partnerships for the goals
Openalex Percentile: Top 3%
Innovation Diffusion and Forecasting
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