Leveraging AI and blockchain for GVC resilience: a case study approach
Bibliographic record
Abstract
The paradigm of efficiency-driven Global Value Chains (GVCs) is yielding to an era of profound geopolitical volatility, compelling firms to develop new resilience strategies. While firms adopt AI and blockchain in response, the literature offers limited insight into the strategic mechanisms that translate these digital tools into tangible resilience against non-market risks. This study addresses this gap through an in-depth, qualitative case study of a multinational retail corporation. The findings reveal three core mechanisms: the cultivation of a ‘digital duality’ where AI builds both operational agility and strategic foresight; the use of blockchain for ‘institutional navigation’ to enhance legitimacy and manage non-market pressures; and the pragmatic management of an ‘implementation paradox’ arising from deep-seated organisational and ecosystem frictions. The study contributes a novel, mechanism-based theory of digital GVC resilience, moving beyond a techno-centric view to explain how firms strategically enact technology to navigate a turbulent global landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".