Effects of ‘social’ environmental stability and entrepreneurial parameters in assessing the relationship among entrepreneurship, green innovation, and strategic orientation: an entrepreneur development of documentary

Sustainable entrepreneurship is essential for solving the issue of economic and social stability. This paper is about the determinants of entrepreneurship among green innovation and strategic innovation. The research studies the communication and R&D variables that impact entrepreneurship development. The literature shows that green innovation is vital in developing strategic orientation. Entrepreneurship must work on R&D to gain economic and social stability. Entrepreneurship development is a dependent variable, and communication and R&D are independent variables. This paper evaluates the performance of Multiple Linear Regression (MLR) and the Artificial Neural Network Regression (ANN) approach for estimating entrepreneurship development (ED) in China from 2000 to 2021. For this work, nine independent variables related to entrepreneurial development were selected, which include CO2 emissions (CO2), GDP growth (GDP), Total greenhouse gas emissions (GHG), Computer, communications, and other services (CCS), Nitrous oxide emissions (NO2), Personal remittances, received (PRR), Research and development expenditure (R&D), New businesses registered (NBR), and patents in environment-related technologies (PERT). Data were gathered from the World Bank (WB) official data bank portal. The highest correlation (−0.85 and −0.71) with ED was observed with GHG, followed by a strong correlation with R & D. The MLR model generated 0.78 R2 (error = 0.26) whereas the ANN model produces 0.83 R2 (error = 0.01). The results of the ANN showed that the association among parameters is strong. Ten input variables and 15 hidden neurons were used to estimate the dependent variable. The process took 87 steps, and the optimum result had a Sum of Square Error of 0.001.The top 5 essential variables are NO, NBR, GHG, R&D, and CO2. The least essential variables are PPR and CCS, respectively, with the lowest variable importance (VI) score. This work is unique in the sense that it covers the research gap with new advanced techniques (ANN) applied to time series data. We believe the idea that we developed is a novel approach for social scientists, entrepreneurial development, social stability, and a sustainable environment. The findings of this research can benefit entrepreneurs in many different ways. By understanding the effects of social environmental stability on their success, entrepreneurs can better tailor their strategies to ensure they get the best possible results.