Bridging Evidence Gaps in Sustainable Development Goals Research: A Comprehensive Review and Forward Agenda

Abstract

Progress toward the Sustainable Development Goals (SDGs) remains uneven, with underdeveloped economies facing persistent institutional, data, and governance constraints. While artificial intelligence (AI) and business analytics are increasingly promoted as transformative tools for sustainable development, their impacts are highly contingent on contextual and institutional conditions. This paper develops an integrative conceptual framework that situates AI-enabled business analytics within institutional economics, data governance, and SDG interaction theory to explain how entrepreneurial innovation and policy effectiveness can be enhanced in resource-constrained settings. Synthesizing global empirical evidence and recent SDG assessments, the study identifies critical gaps in causal research, measurement standardization, and geographic representation. It advances theory-driven propositions linking AI adoption, institutional quality, and development outcomes, and outlines a policy roadmap emphasizing statistical capacity, regulatory coherence, and inclusive innovation ecosystems. By reframing AI as an institutionally embedded capability rather than a standalone solution, the paper contributes actionable insights for researchers, policymakers, and development practitioners seeking to accelerate SDG progress before 2030.

Keywords: Artificial intelligence; Business analytics; Sustainable Development Goals; Institutional quality; Entrepreneurial innovation; Underdeveloped economies