Assessment and optimization of the port logistics data governance capacity based on enhanced MNA-SAA approach

Port logistics data governance is characterized by multi-subject, multi-dimensional and dynamic interaction, which brings significant challenges for enterprises to perform the strategic value of data information. This paper systematically assesses and optimizes port logistics data governance capabilities through an enhanced Meta-Network Analysis-Simulated Annealing Algorithm (MNA-SAA)-based approach to maximize the potential of data information. This approach first applies the MNA method to conceptualize port logistics data governance as “Data Information-Agent-Technology-Process” (I-A-T-P) meta-networks, which portray the dynamic interaction behavior of data information between different subjects. Then, multi-level meta-network metrics are used to measure the port logistics data governance capabilities. SAA is applied to optimize the data governance structure from the agent-data information interaction (AI) network. This proposed approach is validated by an illustrative application of generic port logistics data governance. Based on the optimization results of the generic port logistics data governance meta-networks, data governance capability improvement strategies and the advantages of the enhanced MNA-SAA approach are discussed. Overall, this enhanced MNA-SAA approach promotes understanding of port data governance by conceptualizing complex data governance structures and quantifying data governance capabilities. This study provides decision-makers with implementable support to advance stakeholder collaboration and knowledge sharing to improve future port logistics data governance capabilities.