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@article{192644,
        author = {ABU ISSA MONNIE},
        title = {Using Artificial Intelligence and Machine Learning for Corporate Social Responsibility: An Empirical Study on Financial Performance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2950-2964},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192644},
        abstract = {Technologies associated with Industry 4.0 are changing the strategic landscape of corporate governance and sustainability. We are studying the implications of Artificial Intelligence (AI) and Machine Learning (ML) for Corporate Social Responsibility (CSR) performance and Corporate Financial Performance (CFP). Theories employed include the Resource-Based View (RBV) and Stakeholder Theory. This study suggests that AI should be viewed as a high-order dynamic capability, allowing firms to shift from "reactionary compliance" to "proactive social value creation". The study draws on a novel multi-sourced dataset of firm-level AI adoption rates combined with Refinitiv ESG scores to perform panel data regression and mediation testing. Initial findings indicated that increased AI adoption leads to better CSR performance through resource allocation, transparency in supply chains, and improved Stakeholder Engagement. Further, the relationship with Corporate Financial Performance is mediated through CSR performance levels, leading to improved Return on Assets (ROA) and firm valuations. The paper builds on the emerging literature on "CSR 4.0", demonstrating that algorithmic efficiencies reduce information asymmetries to more easily align corporations with broader sustainable development goals.},
        keywords = {Corporate Social Responsibility, Artificial Intelligence, Financial Analysis, Business, Automation, Machine Learning, Financial Performance, Operational efficiency},
        month = {February},
        }