The association between supply chain structure and transparency: A large‐scale empirical study
Bibliographic record
Abstract
Abstract An emerging body of work acknowledges the challenges focal firms face in gathering material information about their extended supply chains and begins to point to the role of supply chain structure in influencing supply chain transparency. Still, large‐scale empirical evidence on this complex association remains elusive, especially at the supply chain level of analysis. We begin to bridge this empirical gap by examining whether supply chain structure systematically associates to supply chain transparency in the context of the collective public environmental, social, and governance (ESG) disclosures made by a focal firm's customers, suppliers, and subsuppliers. To shed light on this underexplored empirical phenomenon, we gather Bloomberg SPLC data and Bloomberg ESG data about 4803 firms and 20,504 contractual ties organized in 187 extended supply chains. We find that supply chain density positively associates with supply chain transparency, whereas supply chain clustering holds a negative association. We also find that supply chain geographical heterogeneity positively associates with supply chain transparency. Our results significantly expand the literature on supply chain transparency and are relevant to supply chain professionals because they emphasize the central role of supply chain structure in enabling or constraining supply chain transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".