Corporate compliance capability of EMNEs: a prerequisite for overcoming the liability of emergingness in advanced economies
Bibliographic record
Abstract
Purpose The literature on how emerging market multinational enterprises (EMNEs) overcome the liability of emergingness/origin has sidestepped a prerequisite for any efforts to overcome liability, namely, corporate compliance. The authors argue that EMNEs build corporate compliance capability as a knowledge-based firm-specific advantage (FSA) to adapt to institutional norms in advanced economies. In this study, the authors empirically examine the intricate relationships between corporate compliance capability and performance in the US subsidiaries of Chinese firms. Design/methodology/approach In this study, the authors use survey data to empirically examine the intricate relationships between corporate compliance capability and performance in the US subsidiaries of Chinese firms. Findings The findings reveal a positive relationship between corporate compliance capability and subsidiary performance, as mediated by local financing. Originality/value The study suggests that corporate compliance capability helps a subsidiary gain legitimacy, which leads to local resource acquisition and utilization. Corporate compliance capability thus serves as a source of a knowledge-based FSA for EMNEs in developed economies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".