Predicting Consumer Purchase Behavior Influenced by Corporate Social Responsibility Using Deep Convolutional Neural Network Models

One of the many societal and economic variables that affect CPB is CSR. Businesses that want to increase consumer engagement and sales must understand these impacts. Furthermore, both developed and emerging countries, especially those in Asia, have seen a consistent increase in their foreign debt over the last 30 years, making external borrowing an absolute necessity for economic stability. The two main types of external debt are private debts that are not guaranteed by the government and public guaranteed debts that are backed by public institutions. The research suggests a three-step procedure for CPB classification models, including preprocessing data, selecting features, and training the model. Before feature selection is carried out using a Modified LeNet approach inspired by factor analysis, the dataset is cleaned, transformed, and organised as part of data pretreatment. A CNN architecture known as Modified LeNet is used to carry out the CPB classification. A 95.23% success rate in experiments on the dataset proves the model's efficiency. The findings validate the method's potential for precise CPB prediction. While recognising the wider economic effects of foreign debt on financial decision-making, these results highlight the significance of sophisticated classification methods for companies.