摘要
从微观主体的预期学习出发,运用复杂网络演化博弈模型,剖析在智能化转型过程中制造企业间博弈的动态演化规律,并从政府和消费者两个维度探讨推动制造企业智能化转型的驱动效应。结果表明:制造企业间的耦合度越大越利于推动制造企业智能化转型,且智能制造技术越成熟越可提升制造企业智能化转型的推广率;政府制定动态平衡的补贴和税收政策较非平衡政策与静态平衡政策在促进制造企业智能化转型方面更具优势;消费者对智能产品的偏好对推动制造企业智能化转型产生了积极影响,而潜在消费者比例越高越利于推动制造企业智能化转型。因此,政府应完善有关制造企业智能化转型的政策法规,增强制造企业之间的网络拓扑关系,引领消费者的消费偏好,助力制造企业智能化转型。
Based on the expected learning of micro-subjects, this paper uses a complex network evolution game model to analyze the dynamic evolution of the game between manufacturing enterprises in the process of intelligent upgrade, and discusses the driving force for the intelligent upgrade of manufacturing enterprises from the two dimensions of government and consumers. Result shows that the greater the degree of coupling between manufacturing companies, the more beneficial it is to promote the intelligent upgrade of manufacturing companies, and the more mature the smart manufacturing technology, the more it can increase the promotion rate of manufacturing companies′ smart upgrades. Moreover, the government′s dynamic and balanced subsidy and taxation policies have more advantages in promoting the intelligent upgrading of manufacturing enterprises. In addition, consumer preference for smart products has a positive impact on promoting the intelligent upgrading of manufacturing enterprises, and the higher the proportion of potential consumers, the better the promotion of intelligent upgrading of manufacturing enterprises. Therefore, the government should improve the policies and regulations related to the intelligent upgrading of manufacturing enterprises, strengthen the network topology relationship between manufacturing enterprises, lead consumers′ consumption preferences, and assist the intelligent upgrading of manufacturing enterprises.
作者
李婉红
李娜
LI Wan-hong;LI Na(School of Economics&Management,Harbin Engineering University,Harbin 150001)
出处
《软科学》
CSSCI
北大核心
2022年第3期39-47,共9页
Soft Science
基金
国家社会科学基金项目(17BGL204)。
关键词
政府政策
消费者偏好
智能化转型
复杂网络
演化博弈
government policy
consumer preference
intelligent upgrade
complex network
Evolutionary Game
