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Sustainable Supply Chain Management (SSCM) integrates economic, environmental, and social considerations across global supply networks. Despite extensive research, the field remains fragmented across strategic, tactical, and operational levels, with limited theoretical integration and inconsistent alignment of sustainability dimensions. To address this gap, this study develops a data-driven perspective on SSCM research using a hybrid analytical framework. A PRISMA-guided systematic review is combined with bibliometric science mapping with VOSviewer 1.6.16 and transformer-based topic modeling (BERTopic) with Python 3.10 to analyze literature published between 2011 and 2025. The analysis integrates two complementary datasets: highly cited studies to capture established research structures and recent publications to identify emerging trends. The findings reveal the growing prominence of digital technologies, including artificial intelligence, blockchain, and big data analytics, alongside the central role of collaboration and governance mechanisms in enabling sustainability. The topic modeling identifies eleven coherent research themes, highlighting both well-established areas, such as circular economy, and emerging directions, such as risk-oriented decision-making and digital traceability. By introducing a cross-method semantic correspondence approach that integrates citation-based and embedding-based analyses, this study advances a more coherent and multi-layered understanding of SSCM research. This integrated perspective reveals the field’s evolution, core thematic structures, and emerging gaps, while providing a robust foundation for future theoretical and practice-oriented developments.
Güngör et al. (Thu,) studied this question.