Investigating the Impact of Green Supply Chain on Economic Performance: Neural Network Models and Structural Equations Approach

The purpose of this study is to explain the impact of green supply chain on economic performance, emphasizing the mediating role of green innovation, environmental management and quality management in companies listed on the Tehran Stock Exchange.

In this research, the multivariate perceptron neural network approach and modeling of variance-based structural equations in SPSS26 and SmartPls3.3.3 software have been investigated.

The results show that green supply chain management affects economic performance and increases green innovation, environmental management and quality management of economic performance. The establishment of a green supply chain has led to the observance of environmental requirements, and by observing environmental requirements, labor productivity is improved through specialized training of employees.

 In previous studies have not considered the pros and cons of the relationship between environmental management and labor productivity. In this study, according to the selection of companies listed on the Tehran Stock Exchange during the covid-19 period and the statistical sample selected by systematic elimination method and available sampling, these views were examined. Also, in the research method, most researches have only fitted the model with structural equations and regression equations, while in this research, the proposed model fits with multilayer perceptron neural network method and variance-based structural equations and finally to evaluate the performance prediction comparison model. Economically, the root mean square error index is used