Dynamic capabilities and environmental sustainability for emerging economies’ multinational enterprises
Bibliographic record
Abstract
The purpose of this study is to enhance our understanding of how macro (country)—level dynamic capabilities (DC), such as government environmental policies, legal and market requirements, and technological advances, and micro (firm)—level DC, such as organizational size, culture, and managerial characteristics, are related to emerging economies multinationals’ environmental sustainability policies and practices. Limited studies explore linkages between macro-and micro-level DC and environmental sustainability, which urge emerging economies’ multinationals to reconsider their environmental policies and practices in order to compete with enterprises from developed countries. We develop a theoretical framework and offer propositions about the fundamental links between macro and micro DC and emerging economies environmental sustainability efforts. The propositions can be empirically tested in subsequent studies using country-level and firm-level data to examine the interactions between macro-and micro-level capabilities, in relation to sustainable policies and procedures, for multinationals in emerging economies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".