Bibliographic record
Abstract
This study conducts a systematic review and provides a comprehensive up-to-date review of the literature about diversity on corporate boards. Unlike previous studies, we do not restrict our search to a specific type of diversity (e.g., gender diversity) or limited firm outcomes (e.g., firm performance). Our aim is to review, evaluate, synthesize, and summarize the literature and extend our knowledge on five key areas: 1) the theoretical approach (going beyond the theoretical analysis of each article by exploring how the theoretical perspective informs their focus); 2) dominant framing and theorizing (single theory vs multi-theories); 3) determinants and consequences; 4) how board diversity is defined and operationalized; and 5) the outcomes of board diversity. In reviewing the research from 2010 to February 2021 and using Saint Mary’s University Business Source Premier (SMU EBSCO) database, we identify 46 articles. Our findings reveal that agency theory no longer dominates board diversity research and has given way to institutional theory. The increasing use of institutional theory, which considers the effect of social structure on organizational outcomes, may be caused by most of the literature (based on our findings) using cross-country data. At the same time, there is a tendency to use a more multi-theoretical approach rather than a single theory one, and there are methodological limitations, including a paucity of rich data collection methods (e.g., surveys, questionnaires, and interviews). In addition, the current literature, according to the findings, focuses more on the consequences than the determinants of board diversity. Finally, our study intends to highlight and outline crucial research gaps that invite future investigation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".