Committee Diversity Effect on Corporate Investment Risk Practices
Bibliographic record
Abstract
Background: This study examines how diversifying committees influence corporate investment risk practices, specifically in decision-making and resource allocation strategies. Previously, board diversity was commonly used in studies, but committee diversity was often overlooked, even though committees are delegated with providing recommendations for board decisions. Methods: Using information on committee presence, size, gender representation, and independent and non-executive members, we build a detailed diversity composite index. We capture this information from various sources such as corporate official disclosures, corporate websites, and other relevant disclosures. We combine this data with financial and investment information collected through secondary data, including Bloomberg and Refinitiv databases about companies listed on the ASX 300 in the Australian equity market from 2018 to 2020. Results: Our findings show that diversity plays a much more critical role in enhancing long-term strategic investment decisions than in driving short-term operational gains. Conclusions: Additional investigations have shown that increased diversity enhances corporate resource allocation, generating optimal investment and investment efficiency levels. These findings highlight the strategic importance of diversity as a contributor to good governance and better financial performance.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".