Sustainability Regulation and Multinational Enterprise Behaviour
Bibliographic record
Abstract
Mandated corporate social responsibility (CSR) is an increasingly important fixture in our institutional environment, as governments re-assert their role in establishing and policing transnational responsibility boundaries, defined as the border where firms must accept responsibility for their and their suppliers’ social and environmental actions. Sustainability policies such as the European Union’s recently passed Corporate Sustainability Due Diligence Directive and Canada’s Modern Slavery Act put multinational enterprises (MNE) in their crosshairs, as both pieces of legislation demand MNEs accept responsibility for the social and environmental actions of suppliers with whom they only have indirect contact. In three papers in this dissertation, I explore this conundrum, showing why responsibility boundaries emerge as they do, as well as how MNEs can respond to these new regulatory imperatives, and thereby contribute to addressing grand challenges (e.g. climate change, modern slavery). Chapter two takes the regulatory environment as its core focus and I build on rhetoric and logics scholarship to explain the dynamics around responsibility boundary construction. I show how the contours of responsibility boundaries emerge from rhetorical contests in which a regulatory agency’s need for ongoing legitimacy is paramount. Chapter three turns to how MNEs respond once this new regulatory environment is established, and I build a conceptual framework based on new internalization theory that reveals how MNEs can better “cascade compliance” across their global value chains (GVCs). In the fourth chapter, which draws on convention theory, I discuss one common strategy MNEs have used in the past to improve social and environmental outcomes: sustainability certifications. To reveal how sustainability certifications can remain legitimate even while diffusing norms across different institutional environments and national contexts, I generate a model based on both interviews and document analysis that shows how legitimate certifications successfully mitigate many differently perceived sustainability risks. My work has important implications for both theory and practice. In the broadest terms, I add to our understanding of new internalization theory, rhetorical legitimation, and CSR. Practically, across these chapters, I help MNEs see what capabilities to build to respond to our new regulatory environment. I also show how policymakers and MNEs can maintain their legitimacy with diverse stakeholders when communicating their ideas about sustainability.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".