Global multi-tier supply chains face persistent social compliance risks, such as child labour and unsafe working conditions, because upstream supplier data is fragmented and difficult to verify. This paper proposes a socio-technical Digital Product Passport extension that encodes multi-tier social compliance attributes while explicitly supporting suppliers with low digital maturity through lightweight, evidence-based onboarding. The architecture integrates DPP data structures with ERP-triggered workflows and rule-based Robotic Process Automation to automate screening, documentation cheques, and escalation logic. For integrity and non-repudiation, a permissioned blockchain logs compliance decisions and anchors cryptographic commitments of off-chain evidence under access control. The framework is assessed through analytical and structural evaluation of decision determinism, rule consistency, and inter-layer coherence. Validation focuses on verifying that rule-based screening outcomes remain logically stable under incomplete, ambiguous, and heterogeneous supplier data conditions. The developed model illustrates that DPPs can be modelled as active compliance workflow components, not only traceability artefacts, by enabling structured evidence acquisition, rule-based classification, and auditable decision support across multi-tier supplier ecosystems.

Supply chain disputes constitute a major operational challenge for modern organizations because of the increasing complexity, globalization and interdependence of supply networks. Blockchain-based dispute resolution has been conceptually presented as a promising solution able to improve transparency, traceability, trust and automation. However, empirical evidence regarding managerial perceptions and organizational readiness remains limited. This paper addresses this gap through 19 semi-structured interviews with supply chain professionals. The findings show that adoption of blockchain-based dispute resolution remains extremely limited. Supply Chain managers, however, recognize its potential for standardized data-intensive disputes, especially billing, freight audit and delivery verification, but underline technological, legal, organizational and relational barriers. The paper argues that hybrid human-centered mechanisms are more realistic than fully autonomous blockchain arbitration in the short term.

Data-based technologies are increasingly embedded in supply chain decision-making, where they transform distributed operational, environmental, and organisational data into forecasts, summaries, recommendations, and decision-support outputs. While these technologies can improve visibility, responsiveness, sustainability performance, and resilience, they may also amplify risks when outputs are inaccurate, opaque, weakly validated, or used without clear accountability. This paper develops a governance-oriented framework for data-based technologies in sustainable and resilient supply chain decision-making, with large language models used as an illustrative case. The framework is structured around four governance checkpoints: application context, regulatory sensitivity, model validation, and impact and feedback monitoring. The paper contributes a conceptual basis for integrating governance into data-supported decision processes and highlights the importance of human oversight, validation, auditability, and feedback learning in preventing risk escalation.

Transparency is a central requirement in personal data governance, yet users are frequently exposed to legal disclosures they do not understand. This paper conceptualises transparency as a dual construct: a structural dimension, covering the formal conditions of information provision, and a cognitive dimension, addressing observable conditions for comprehensibility. Within the GDPR and the WP260 guidelines, a conceptual evaluation model is operationalised through a generic grid of 24 observable criteria organised around these two dimensions. It is applied to an exploratory audit of 20 e-commerce websites across five sectors in France, conducted between January and March 2026. The audit yields an overall transparency score of 90.7%, with a structural score of 95.4% and a cognitive score of 86.0%. This gap indicates that legal compliance alone is insufficient to guarantee genuine intelligibility. The framework is designed to support reproducible assessment through explicit criteria and scoring rules, and to be transferable beyond e-commerce to any digital environment where personal data are collected and disclosed.