Big Data Pilot Zones and Supply Chain Resilience—Quasi‐Experimental Evidence From China
Bibliographic record
Abstract
ABSTRACT The establishment of big data pilot zones (BDPZs) has generated substantial scholarly and practical focus, yet there remains limited understanding of their effects on supply chain resilience (SCR) at the microenterprise level. This study utilizes China's BDPZ as a quasi‐experimental framework and analyzes firm‐level panel data from the second quarter of 2009 to the fourth quarter of 2023 to evaluate the effects of BDPZ on supply chain resilience (SCR) as well as the underlying mechanisms involved. BDPZ's effects are analyzed using a staggered difference‐in‐differences approach, and SCR at the firm level is assessed using the entropy weight method. The findings indicate that China's BDPZ significantly enhance firms' SCR. In addition, mechanism testing reveals that this enhancement is primarily achieved through improved product competitiveness, alleviated financing constraints, and accelerated digital transformation. Furthermore, a heterogeneity analysis demonstrates that the impact of BDPZ on SCR varies according to factors such as firms' growth stages, industry competitiveness, and transportation convenience. Finally, theoretical and practical implications are provided based on the findings.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".