Global Sustainability Disclosure Standards and MNE’s Strategic Response
Bibliographic record
Abstract
When pursuing the global sustainability strategy, multinational enterprises (MNEs) are embedded in the sustainability-specific organizational field where global sustainability standards create strong institutional forces to shape MNEs’ sustainability behaviors. The purpose of our paper is to elaborate on the conditions under which MNEs are more likely to resort to decoupling as the strategic response to institutional complexity in the organizational field of global sustainability reporting. By conceptualizing the coexistence of multiple sustainability reporting standards from the lens of institutional complexity, our paper theorizes how field-level attributes – fragmentation, formalization, and centralization – shape MNEs’ strategic response. We argue that the current fragmented and informalized disclosure standards are more likely to trigger policy-practice and means-ends decoupling, in the form of either symbolic adoption or symbolic implementation. The recent harmonization efforts, such as the emergence of the International Sustainability Standards Board (ISSB) and its publication of International Financial Reporting Standards (IFRS) S1&S2, could enhance the centralization of global sustainability reporting and provide hope to achieve higher levels of compliance in reporting. Overall, with the collective efforts of all actors in the organizational field of global sustainability reporting, a positive feedback loop could be formed to achieve an alignment of transparency policy, consistent reporting practice, and the intended objective of sustainable development.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".