Asian Journal of Shipping and Logistics | 2026
Authors: Haralambides H.; Čišić D.; Drezgić S.
DOI: 10.1016/j.ajsl.2026.05.003
Journal: Asian Journal of Shipping and Logistics
Year: 2026
Publisher:
Document Type:
Open Access: Yes
Cited by: 0
Over the past two decades, maritime logistics research has expanded rapidly alongside globalisation, port system transformation, and the increasing integration of shipping into global supply chains. The discipline has evolved into a broad and heterogeneous research field spanning shipping, ports, supply chains, digitalisation, sustainability, and resilience. While this expansion has enriched the literature, it has also led to conceptual fragmentation and overlapping research agendas. Thus, the objective of this paper is to provide a systematic and reproducible mapping of the intellectual structure of maritime logistics research using a semantic-network approach based on text embeddings and community detection algorithms. Drawing on a comprehensive corpus of peer-reviewed publications, we identify eight major thematic domains, further decomposed into eleven clusters and fifteen sub-clusters, capturing both established and emerging research streams. Unlike traditional bibliometric reviews relying on keywords or citations, the proposed approach captures semantic proximity between concepts, allowing for a more nuanced representation of interdisciplinary linkages within maritime logistics. The findings clarify how research on shipping operations, port systems, maritime supply chains, digital technologies, environmental regulation, and resilience has co-evolved, while also highlighting underexplored intersections and future research opportunities. By offering a transparent taxonomy and research roadmap, the paper contributes to a clearer conceptual understanding of maritime logistics and provides practical guidance for scholars, doctoral researchers, and policy-oriented maritime institutions. © 2026 The Authors.
Bibliometric mapping; Large language model embeddings; Louvain community detection; Maritime logistics; Maritime supply chains; Semantic network analysis