Sustainable logistics: innovative orientations and global trends in scientific research

Purpose The purpose of this study is to identify key topics in the scientific literature on sustainable logistics, reveal important interdisciplinary relationships and identify leading researchers in the sector. The relationship between these emerging structures and also the correct understanding of the structures that will evolve in the future is of great importance for the strategic decision-making processes of companies. Design/methodology/approach In the study prepared for this purpose, the Web of Science (WoS) data have been used. It addresses the use of advanced methods such as social network analysis (SNA) and techmining in sustainable transportation and logistics. While SNA focuses on identifying interdisciplinary collaborations and key actors in the scientific world by examining the relationships and interactions between scientific publications, technology mining is used to understand innovative trends and technological developments in this field by extracting technological terms and concepts in scientific texts. SNA highlights the connections between these topics, whereas technology mining is used to uncover new technological trends and innovative solutions. Findings In this study, the researchers focused on a wide range of issues in the fields of logistics and transportation. The findings on logistics, sustainability, supply chain management, machine learning, COVID-19 impacts, the role of China and India, artificial intelligence, innovation, the Internet of Things (IoT), blockchain, uncertainty and case studies reveal a multi-faceted structure. Also, it can be seen that significant emphasis is placed on the complexity of logistics processes. While the association of high aggregate constraints appears to concentrate a significant cumulative importance on certain aspects of logistics, it can be said that these concepts point to potentially critical operational elements. Research limitations/implications The authors did use only WoS records to reach their aim. This study can be extended with other major databases such as Scopus. Practical implications SNA mode and structural hole analysis enable clear visualization of cluster separations and uncover gaps in networks where connections are sparse. By identifying structural holes, researchers can bridge disconnected clusters, fostering collaboration and innovation through the integration of diverse perspectives. Low aggregate constraint values highlight niche topics, supporting deeper exploration and specialized research. Structural hole analysis further identifies key nodes that act as brokers, enhancing connectivity and resource flow. Together, these methods optimize networks, uncover untapped opportunities and contribute to a detailed understanding of research landscapes, driving targeted innovations and impactful advancements in specialized fields. Social implications The study’s findings highlight significant social implications by emphasizing sustainability, collaboration and technological innovation in transportation and logistics. Insights into global collaborations and contributions of key players like China and India underscore the importance of international partnerships in addressing complex challenges. With the integration of machine learning, IoT and blockchain enhances efficiency, transparency and resilience in logistics systems, benefiting societies by improving supply chain reliability. In addition, the focus on sustainability and support strategies for crisis adaptation and long-term resilience, fostering equitable development. Originality/value The authors combined SNA, bibliometrics and machine learning to see collaborations and technological innovations in transportation and logistics. The authors can identify the global collaborations and contributions of key players to underscore the importance of international partnerships in addressing complex challenges.