Blockchain for compliance: an information processing case study of mandatory supply chain transparency in conflict minerals sourcing
Bibliographic record
Abstract
Purpose Firms are increasingly pressured to comply with mandatory supply chain transparency (SCT) regulations. Drawing on information processing theory (IPT), this study aims to show how blockchain technology can address information uncertainty and equivocality in assuring regulatory compliance in an interorganizational network (ION). Design/methodology/approach IPT is applied in a single case study of an ION in the mining industry that aimed to implement blockchain to address mandatory SCT regulations. The authors build on a rich proprietary data set consisting of interviews and substantial secondary material from actors along the supply chain. Findings The case shows that blockchain creates equality between actors, enables compliance and enhances efficiency in an ION, reducing information uncertainty and equivocality arising from conflict minerals regulation. The system promotes engagement and data sharing between parties while protecting commercial sensitive information. The lack of central authority prevents larger partners from taking control. The system provides mineral provenance and a regulation-compliant record. System cost analysis shows that the system is efficient as it is inexpensive relative to volumes and values of metals transacted. Issues were identified related to collecting richer human rights data for assurance and compliance with due diligence regulations. Originality/value The authors provide some of the first evidence in the operations and supply chain management literature of the specific architecture, costs and limitations of using blockchain for SCT. Using an IPT lens in an ION setting, the authors demonstrate how blockchain-based systems can address two key IPT challenges: environmental uncertainty and equivocality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".