How do suppliers respond to institutional complexity? Examining voluntary public environmental disclosure in a global manufacturing supply network
Bibliographic record
Abstract
Abstract When making decisions about their commitments to environmental practices and performance, suppliers face heterogenous institutional logics and their diverse prescriptions for action. How do suppliers respond to such institutional complexity? We examine this question in the context of suppliers' voluntary public environmental disclosures (disclosure). Specifically, our study assembles a unique panel data set of global manufacturing suppliers and their annual contractual relationships with buyers. Building on the institutional logics perspective and the sustainable supply network literature, we hypothesize that suppliers selectively mimic the disclosure of their buyers by following market, corporate, and sustainability logics. Our study contributes to the institutional logics perspective and the sustainable supply network literature by indicating that in the context of disclosure, market and sustainability logics both actively shape suppliers' responses to institutional complexity. Furthermore, we find support for mimicry as a mechanism of buyer influence that can lead to disclosure heterogeneity across suppliers even when they follow the same logic, which opens new avenues for research. Our findings can be leveraged by buyers, policymakers, and other stakeholders interested in advancing transparency and sustainability in supply networks.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".