Consumer-Driven E-Commerce: A Literature Review, Design Framework, And Research Agenda On Last-Mile Logistics Models

Purpose The purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML's diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics.Design/methodology/approach The systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a snowballing technique, a total of 47 articles form the basis of the review.Findings The literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions.Research limitations/implications To circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models.Practical implications The design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution.Social implications Improved LML performance would have positive societal impacts in terms of service and resource efficiency.Originality/value This paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions.