Trustworthiness evaluation in digital supply chains: a metric-based, stakeholder-driven approach

As digital supply chains (DSCs) evolve into highly interconnected, data-driven ecosystems, evaluating the trustworthiness of data assets has become critical for operational integrity, risk management, and strategic decision-making. Existing frameworks primarily focus on detecting threats and responding to risks, offering limited capabilities for proactive, pre-risk evaluation that considers stakeholders specific concerns. This paper introduces a modular Data Trustworthiness Evaluation (DTE) model as part of the broader Data Trustworthiness Threat and Risk Estimation Framework (DTTREF). The model defines six interdependent trustworthiness pillars — data quality, data privacy, data security, data interoperability, data heterogeneity, and data risk appetite — and applies the Analytic Hierarchy Process (AHP) to elicit and integrate stakeholder preferences into a quantifiable trustworthiness score. This approach advances the current state-of-the-art by embedding trustworthiness as a measurable, context-aware layer in digital supply chain governance, addressing gaps in stakeholder engagement and enabling adaptive, real-time evaluation strategies.