Cross‐National Governance Research: A Systematic Review and Assessment
Bibliographic record
Abstract
Abstract Manuscript Type Review Research Question/Issue Using a systematic literature review approach, we survey 192 cross‐national comparative studies published in 23 scholarly journals in the fields of accounting, economics, finance, and management for the period 2003 to 2014. The purpose is to synthesize and appraise the extant empirical research on the interplay between country‐ and firm‐level governance mechanisms and the effects on firm outcomes. Particular focus is placed on studies that examine firm economic performance. Research Findings/Results We identify and distinguish between two groups of cross‐national governance studies. The first type compares macro, country‐level outcomes and the second compares three different firm‐level outcomes: economic performance, governance mechanisms, and strategic decisions. We compare the theoretical frameworks used and further analyze the country‐level factors and firm‐level governance attributes that have been combined to investigate their interplay and the effects on firm outcomes. We find substantial variation in the use and measurement of country‐level factors as well as a variety of causal forms used to explain the combined effects of country‐ and firm‐level governance mechanisms. This wide variability precludes comparison, and consequently prevents identifying consistent patterns of influence between country‐level governance factors and firm‐level governance mechanisms and/or performance. We identify research gaps and provide fruitful directions for future research on this topic. Theoretical Implications The cross‐national governance research has been guided mainly by an economic perspective focusing on international differences in the effectiveness of specific governance mechanisms. Few comparative studies have integrated an institutional perspective or examined the external forces that drive the diffusion and use of specific governance mechanisms. Such integrative framework would improve the understanding of cross‐national differences in the salient dimensions of country‐level governance factors and how they mediate the effectiveness of firm‐level governance mechanisms. Practitioner Implications Our results reveal that firm‐ and country‐level governance mechanisms have been interacted and combined, either to address various agency problems or to compensate for a weak national environment. This calls for regulators and investors to consider national governance factors when assessing firm‐level governance practices.
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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 | 0.043 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.041 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".