Green behaviors adopted by supply chain companies are conducive to resource conservation and environmental protection and enhancing their core competitive advantages. By constructing a game model of green behavior of supply chain companies, this research deeply analyzes the main influencing factors of green behaviors adopted by supply chain companies. It uses dynamic evolution game analysis and simulation experiment methods to explore the path evolution direction and dynamic convergence process of green behavior strategy choices of these companies, so as to provide reference value for green behavior decision-making of supply chain enterprises. The research results show that the probability of supply chain enterprises choosing green behavior strategies is related to factors such as enterprise green investment income and costs, co-benefits, spillover benefits, greenness and output of raw materials or products, government green subsidy coefficients, and fines. Supply chain enterprises should reduce the cost of green investment, maximize the profit of green investment, and increase the greenness of raw materials or products; the government should increase the coefficient of green subsidies and encourage supply chain enterprises to actively participate in the collaborative management of the green supply chain.
Based on social network theory and dynamic capability theory, a theoretical model is constructed that specifies the process through which relational embeddedness affects enterprise value. The authors selected 612 Shenzhen and Shanghai A-share manufacturing enterprises in the CSMAR database as sample enterprises, and a multiple regression analysis method was used to test hypotheses. The results show that (1) there is an inverted “U” relationship between relational embeddedness and enterprise value; that is, the enterprises’ relational embeddedness has an “inflection point” effect on the enterprise value; (2) the enterprises’ resource integration capability plays an intermediate role in the impact of relational embeddedness on enterprise value; (3) the market environmental dynamics and technology environmental dynamics play negative regulatory roles in the inverted “U” relationship between relational embeddedness and enterprise value, and they play different regulatory roles before and after the enterprise relational embeddedness reaches the “inflection point”; that is, before the relational embeddedness reaches the inflection point, the technology environmental dynamics plays a major negative regulatory role, while after the relational embeddedness reaches the inflection point, the market environmental dynamics plays a major negative regulatory role. Compared with the market environmental dynamics, the impact of technology environmental dynamics on the relationship between relational embeddedness and enterprise value is more significant; that is, the effect of relational embeddedness on enterprises value is more sensitive to the technology environmental dynamics.
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