Governance and Performance in Insurance Companies: A Bibliometric Analysis and A Meta-Analysis
Bibliographic record
Abstract
This paper provides a review of theoretical contributions and empirical studies on the external and internal mechanisms of corporate governance of insurance companies and their effects on performance and/or risk. Thanks to the analysis of the studies published between 1985 and 2019 through bibliometric tools, we are able to illustrate the networks of scientific collaborations (co-authorship) and relationships between the most used terms, also highlighting the most significant groups of scholars and research strands. Additionally, the paper carries out a meta-analysis of around thirty quantitative articles that show a relationship between the quantitative-qualitative characteristics of the Board of Directors and the performance of the insurance company. The empirical studies show a consensus on the positive contribution of board size and the presence of independent directors on performance. Moreover, insurance research networks do not appear to be very interconnected, especially as regards to emerging markets. The paper also provides a useful starting point for future research aimed at defining the specificities of the governance-performance relationship of insurance companies within an evolving regulatory and market framework.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsMeta-epidemiology (broad) Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
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 | 0.023 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.120 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".