Application of Artificial Neural Networks to a Study of Entrepreneurial Marketing on Business Environmental Sustainability

Abstract

The Artificial Neural Network (ANN) model was developed to predict the environmental sustainability of companies based on their entrepreneurial marketing parameters such as proactiveness, opportunities, risk-taking, and innovativeness. The model was trained on a dataset of 275 companies rated on these parameters, with 225 data used for training selected based on gathering prediction and 50 for testing selected randomly. The optimum ANN architecture achieved was 4-10-10-1. The adjusted R-squared value for testing was 0.985, and for training, it was 0.947. The proposed model outperformed traditional regression methods due to its ability to capture the complex relationships between the input parameters and the output variable. The model can produce environmental sustainability for unseen data of infinite combinations of entrepreneurial marketing variables. Sensitivity analysis on the ANN model helps map the nonlinearity between entrepreneurial marketing variables and environmental sustainability. In conclusion, the proposed ANN model has the potential to be an effective tool for predicting a company's environmental sustainability. Companies can use the model to identify areas where they need to improve their performance and take corrective actions to reduce their environmental impact. Therefore, marketer managers or policymakers realize that entrepreneurial marketing is the core factor to be sustainable management.