An integrated analytical approach to consumer trust and transparency in supply chains

This study addresses the transparency paradox in organic food supply chains, where substantial investments in traceability technologies often fail to yield linear increases in consumer trust. Moving beyond a purely behavioral lens, this research evaluates the downstream efficacy of two distinct supply chain visibility configurations: Data-Driven Transparency (derived from traceability infrastructure like Blockchain, IoT) versus Relational Transparency (derived from Short Food Supply Chain network designs). Employing a novel dual-analytic methodology of Partial Least Squares – Structural Equation Modelling (PLS-SEM) and eXplainable AI (XAI), we analyzed data from active organic consumers in India. The PLS-SEM results indicate that Relational Transparency and not Data-Driven Transparency, is the primary driver for collapsing the psychological distance between farm and fork. Conversely, the XAI analysis reveals that data-driven mechanisms function as a non-linear hygiene factor, essential for satisfying the validation thresholds of high-identity consumers but insufficient to drive purchase intention alone. The findings offer a strategic roadmap for behavioral supply chain management, suggesting that firms must decouple their transparency strategy, utilizing technological traceability for market entry (verification) while optimizing network design for relational proximity to drive market share (connection).