A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China. (November 2022) - Pan, Qiaohong; Luo, Wenping; Fu, Yi
Abstract: This study aims to explore the logistics service value creation using big data in the collaboration between logistics service companies and stakeholders. Based on the dynamic capability theory (DCT), this paper constructs a theoretical framework of value creation in logistics collaboration with six big data-driven factors, namely connection, interaction, integration, synergy, reconfiguration, and innovation. The clear set qualitative comparative analysis (csQCA) method examines the value creation paths of logistics service companies in China through combinations of big data-driven elements in collaboration with stakeholders (e.g., suppliers, manufacturers, retailers, and customers). The results show that combinations of six factors driven by big data form three paths to create value for logistics service companies and these factors play unequal roles in improving the value of logistics services. This study provides considerable insight for logistics service managers, practitioners, and scholars that organizations should attach importance to the role of big data for value creation in logistics collaboration. Highlights: The theoretical framework of big data-driven factors and value creation in logistics collaboration is constructed. The csQCA method is used to explore paths for big data to create value. The results show that logistics service companies form three value creation paths through the combination of big data-driven elements. This study explores the application of big data in the field of logistics services through Chinese data.