Behavioral Mechanism Design for Trustworthy Capacity Sharing in Data-Driven Supply Chains
Keywords:
capacity sharing, behavioral mechanism design, trust, data-driven supply chains, governance, fairnessAbstract
Capacity sharing across organizational boundaries has emerged as a strategic imperative for improving utilization, resilience, and sustainability in modern supply chains. The proliferation of data-driven platforms enables real-time visibility into underutilized warehousing, transportation, and production assets, yet the full potential of such sharing remains unrealized due to persistent behavioral barriers rooted in mistrust, strategic misrepresentation, and fear of opportunistic exploitation. Traditional mechanism design, grounded in assumptions of rational self-interest, often fails to account for the social preferences, reciprocity inclinations, and bounded rationality that govern actual inter-firm decisions. This paper develops a conceptual framework for behavioral mechanism design that aligns economic incentives with psychological drivers of trust and cooperation within data-rich supply chain environments. It examines the structural trade-offs between centralized and decentralized capacity exchange architectures, the role of digital infrastructure in encoding trustworthy behaviors, and the governance arrangements necessary to sustain fair and robust sharing over time. Through an interdisciplinary synthesis of supply chain systems, behavioral economics, mechanism design, and platform governance, the paper articulates design principles that embed transparency, reputation, and participatory rule-making into the operational core of sharing institutions. Policy implications for regulating data-driven capacity markets and promoting long-term ecological sustainability are discussed. The framework offers a pathway toward institutionalizing trustworthy capacity sharing as a cornerstone of resilient, adaptive supply chain ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
