Research on Data Fusion-Driven Collaborative Logistics Development in the Bay Area

In this study the author evaluated a data fusion-driven logistics information platform in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The author aimed to overcome institutional and technical barriers to cross-border data sharing. Using a mixed-method design grounded in institutional theory and the technology-organization-environment framework, the author conducted a case study of the pilot platform (2021–2023) and benchmarked it against international bay areas. The author found that phased implementation cut customs clearance time by 48%, reduced documentation processing by 55%, improved system response by 42%, boosted cross-border data availability by 68%, and lowered error rates by 55%, despite persistent semantic interoperability gaps and adoption hurdles. These results indicate that federated architectures, coordinated governance bodies, and incremental rollouts can balance data sovereignty with integration needs, offering a replicable model for multi-jurisdictional logistics collaboration.