Research that rescues: investigating policy impact of humanitarian logistics and disaster supply chain management research through machine learning

Purpose This study aims to address a critical gap in Humanitarian Logistics and Disaster Supply Chain Management (HLDSCM) scholarship by examining how academic research informs real-world policymaking. This study investigates “reverse dynamic” where scientific outputs support policy decisions worldwide and prioritize relevance to Sustainable Development Goals (SDG-3, SDG-11), thereby advancing broader science-policy dialogue. Design/methodology/approach An advanced methodological framework was used to identify and evaluate 2,132 Scopus-indexed articles and were systematically linked with policy documents in Overton database based on their citations coverage, density and intensity. The author identified most influential journal (Journal of Humanitarian Logistics and Supply Chain Management), author (Gyöngyi Kovács), institution (Hanken School of Economics, Finland) and country (United States). A machine learning-based Latent Dirichlet Allocation topic modeling approach was applied to detect core themes in the policy-cited research. This recent methodological advancement provides a more robust and scalable means to identify emergent themes and their policy relevance by enhancing the objectivity and depth of relevance assessment compared to conventional qualitative methods applied in HLDSCM research. Findings In total, 389 articles have been referenced in global policy documents, revealing an 18.24% policy citation rate. Analysis highlights key intermediaries and five dominant themes ranging from cross-sector collaboration to pandemic-driven adaptations that together contribute significantly to achieving SDGs. The study underscores growing appeal of HLDSCM research among policymakers seeking evidence-based guidance from academia. Research limitations/implications Policy citations capture visible traces of research in public policy documents but do not measure implementation or causal influence, and Overton coverage varies across regions and languages. Within these boundaries, the findings provide a benchmark for HLDSCM’s policy-document visibility; a theory-informed interpretation of why some HLDSCM research is more policy-visible than others; and actionable guidance for designing HLDSCM research and decision-support tools that are more usable for policy and operational planning aligned with SDG-3 and SDG-11. Originality/value The study combines policy-citation analysis with topic modeling to map and explain HLDSCM’s policy visibility, offering a replicable method and a theory-grounded set of recommendations for increasing the policy relevance of HLDSCM scholarship.