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Standards-related Regional Innovation and International Cooperation

Research on the Evolutionary Mechanism of Government-Enterprise Collaboration in AI-Empowered Digital Transformation of the Manufacturing Industry

  • Fulei Shi
    School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China; shifulei@cueb.edu.cn (F.S.); 22025213039@cueb.edu.cn (Z.Z.)
    Author
  • Zisha Zhou
    School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China; shifulei@cueb.edu.cn (F.S.); 22025213039@cueb.edu.cn (Z.Z.)
    Author
  • Ying Zheng
    Accounting School, Capital University of Economics and Business, Beijing 100070, China
    Author

DOI:

https://doi.org/10.63385/sriic.v1i3.101130

Keywords:

AI Technology Maturity,Digital Transformation of Manufacturing Industry,Government-Enterprise Collaboration,Evolutionary Game

Abstract

Digital transformation can effectively enhance the core competitiveness of manufacturing enterprises. Artificial Intelligence (AI) technology is essential to promote the digital, networked, and intelligent development of enterprises. So, understanding how it influences enterprises' transformation decisions matters greatly for building a modern industrial system. In this paper, we build a government-enterprise evolutionary game model that brings in AI technology maturity as a moderating variable, and systematically analyzes the dynamic interaction mechanism between government incentive policies and manufacturing enterprises' digital transformation decisions. Through theoretical reasoning and numerical simulations, we uncover how AI maturity reshapes the cost-benefit structure of transformation. Our results show that AI technology maturity acts as a game-changing parameter. When maturity stays low, the system tends to be locked in an ineffective equilibrium where enterprises wait and see while the government lacks leverage. When AI technology maturity exceeds the critical threshold, enterprises' willingness to undergo independent transformation is significantly enhanced, and the government's strategy can accordingly shift from "strong incentives" to "routine guidance". Sensitivity analysis further reveals that enterprise transformation cost, benefit elasticity coefficient, waiting loss and transformation benefit are also critical to enterprises' digital transformation. This paper focuses on process manufacturing such as chemicals, pharmaceuticals and food processing. For these sectors, AI helps reduce production downtime, optimize supply chain coordination, improve product quality and boost productivity. Our findings help to understand the mechanism and effects of AI technology maturity on digital transformation, and provide a reliable basis for optimizing policy tools and effectively boosting the digital transformation of the manufacturing industry.

References


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    Copyright (c) Copyright © 2025 Fulei Shi, Zisha Zhou, Ying Zheng

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