A Scientometric Analysis of Crowdsourced Delivery Logistics

Crowdsourced delivery logistics is a rapidly evolving field, capturing the attention of researchers as the home delivery industry booms and AI capabilities advance. This study provides a comprehensive landscape overview using the Scopus database, VOSviewer, and CiteSpace software. Our scientometric analysis, the first of its kind, examines 415 research articles published between 2013 and 2024 in crowdsourced delivery logistics. An in-depth analysis of citations, cocitations, authorship patterns, and keyword trends shows a notable increase in the publication rate following 2019. Moreover, recent citation trends highlight the growing interest in keywords such as “reinforcement learning”, “integer programming”, and “last mile” with these concepts increasingly applied to vehicle routing, order matching, and crowdsourced delivery systems. In addition, the emergence of the keywords “stochastic model” and “reinforcement learning” in 2022 and 2023 further underscores their increasing prominence in addressing uncertainty in logistics and delivery optimization. The field’s most influential journals, articles, and emerging themes were pinpointed while investigating collaborations among institutions and countries. Finally, the main contributing research sources, universities, and countries are mapped and provided along with potential prospective study subjects.