A Data-Driven Framework for Measuring CSR Effectiveness and Advancing Sustainable Business Processes

The increasing focus on sustainability has heightened recognition of the importance of data-driven approaches to accurately assess and enhance the effectiveness of “Corporate Social Responsibility” (CSR). The research examines the application of data-centric technology to assess the efficacy of CSR. This research reviewed the current literature on the concepts of “Artificial Intelligence” (AI), “Machine Learning” (ML), “Business Intelligence” (BI), “Business Process Management” (BPM), advanced analytics and the incorporation of ESG into organizations to understand how a data-driven approach can enhance CSR evaluation and the sustainability of organizations. According to the literature examined, AI decision models can be used to prioritize CSR programs, while advanced analytics and BI tools can support the evaluation and reporting of ESG performance. Data-driven BPM and Industry 4.0 technologies aid in process optimization and sustainability. Nevertheless, numerous obstacles have been recognized in the literature, including a lack of standardized CSR metrics, poor data quality and availability, fragmented data, inconsistent disclosure practices, governance limitations, regulatory issues, and greenwashing. The critique highlights the importance of addressing the gap in combining the elements of CSR evaluation, data analytics, transparency, stakeholders, and sustainable business operations. Thus, the proposed conceptual framework seeks to integrate reliable CSR information, metrics, analytics, disclosure practices, and stakeholders' concerns to enhance productivity and organizational effectiveness. This document establishes a basis for entities aiming to adopt more transparent, quantifiable, and data-driven CSR initiatives