Year: 2025
Type: article
Abstract: Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda.
DOI: 10.3390/jmse13061093
Authors: Siqian Cheng, Jiankun Hu, Youfang Huang, Zhihua Hu
Institution: Shanghai Maritime University
Language: English
Topic: Maritime Ports and Logistics
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410898115