Analyzing the Financial Impact of Corporate Social Responsibility Using Quantum Neural Transformer Models

The importance of CSR and innovation in managerial decision-making and public policy is highlighted in this study, which assesses their financial effects on a company's bottom line. Business strategy and value generation have long been topics of academic and practitioner debate around CSR. Managers will be able to make better strategic decisions with the help of the empirical evidence provided by this research, and legislators will have an easier time incorporating CSR into legislative frameworks. The paper uses Graph QuaWalkTrans, a novel GNN design that generates node quantum states by quantum walks on attributed graphs, to assess the monetary benefits of different tactics. The methodology incorporates CFS to improve model performance, as well as important text preparation tasks like stemming, case normalisation, stop-word removal, lemmatisation, and rephrasing. The suggested QuaWalkTrans model performed exceptionally well, with a recall of 95.69%, a precision of 96.66%, and an accuracy of 99.86%. These results show that cutting-edge analytical methods work well for evaluating CSR and innovation plans. In the end, this study shows how effective advanced modelling tools are and how important CSR and innovation are for driving financial performance. It also provides empirical support for strategic decision-making in policy and commercial sectors.