A design science framework for ethical data-driven decision-making in higher education

Abstract This study addresses the growing need for structured and ethically grounded data-driven decision-making (DDDM) processes in higher education institutions, where increasing volumes of data and artificial intelligence (AI) applications are reshaping managerial practices. Despite the widespread use of analytics and decision support systems, existing models often treat ethical and governance considerations as secondary or implicit components, leading to a conceptual and operational gap in the literature. The primary aim of this study is to propose a conceptual framework that systematically structures data-driven decision-making processes by integrating analytical, managerial, and ethical dimensions. The study adopts a design science research (DSR) approach and develops a six-layered framework, namely DDDM-FRAME, based on a structured synthesis of recent literature on learning analytics, institutional analytics, and responsible AI. The proposed framework provides a conceptual structure that links data acquisition, analytical processing, decision support mechanisms, visualization, and institutional impact within a unified architecture while conceptualizing the Ethics and Governance Filter (EGF) as a cross-cutting governance mechanism operating across all six layers of DDDM-FRAME rather than as an independent layer. Compared with many existing models, the framework is conceptually designed to incorporate ethical evaluation as an operational governance mechanism embedded throughout the decision-making process rather than as a post hoc consideration. However, this study is limited by its conceptual nature, as the proposed framework has not yet been empirically validated in real institutional settings. Future research should focus on testing the applicability and effectiveness of DDDM-FRAME through case studies, simulations, and quantitative analyses across diverse higher education contexts.