Freight Rate Decisions in Shipping Logistics Service Supply Chains Considering Blockchain Adoption Risk Preferences

Yujing Chen, Jiao Mo and Bin Yang ()
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Yujing Chen: School of Economics and Management, Ningbo University of Technology, Ningbo 315211, China
Jiao Mo: School of Economics and Management, Ningbo University of Technology, Ningbo 315211, China
Bin Yang: School of Economics and Management, Ningbo University of Technology, Ningbo 315211, China

Mathematics, 2025, vol. 13, issue 15, 1-31

Abstract: This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders.

Keywords: blockchain technology; technology trust; risk disposition; shipping logistics; supply chain strategy; mean-variance analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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