Research Area:  Big Data

Abstract:

The divergent evolution of e-commerce has complicated its correspondingly logistics management. However, few studies have explored e-commerce logistics business models via big data analytics. Hence, this investigation explores e-commerce logistics business models from unstructured big data. Specifically, this work develops a hybrid content analytical model to scrutinize essential knowledge of e-commerce logistics. The empirical results of the proposed model incorporate theories of resource dependence theory (RDT) and innovation diffusion theory (IDT) to generate logistical strategies. Ten critical themes of e-commerce logistics from topic mining are “Southeast Asias e-commerce logistics payments”, “E-commerce order management”, “E-commerce logistics cloud services”, “E-commerce logistics package management”, “Europe e-commerce trends”, “Indias e-commerce logistics”, “E-commerce distribution management”, “Tax policies”, “E-commerce logistics platforms”, and “E-commerce logistics networks”. Moreover, the fundamental rule of “cross-border e-commerce logistics” is uncovered by the association rules model. The proposed hybrid content analytics framework provides a research foundation for e-commerce logistics management. Furthermore, e-commerce logistics can be implemented by vital strategies: “Establish inter-organizational and technical collaboration to create positive operations performance” and “Comprehend law, policy, and cultural differences to customize appropriate technologies of e-commerce logistics”.

Keywords:  

Author(s) Name:  Pei-JuWu and Kun-Chen Lin

Publisher name:  ELSEVIER

DOI:  10.1016/j.tele.2017.11.004

Volume Information:  Volume 35, Issue 1, April 2018, Pages 237-244

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0736585317305397