Inside the black box: How can gender diversity make a difference in the boardroom?
Bibliographic record
Abstract
Purpose Prior research shows that a board of directors' gender diversity positively influences organizations. However, little is known about how and why gender diversity influences the board of directors' functioning and decisions. The objective of this paper is to investigate the differences between women and men when fulfilling their role as directors. Design/methodology/approach This research uses a qualitative approach based on 29 in-depth semi-structured interviews with female and male board members. Findings The authors’ findings reveal that women are as involved as men in the board tasks and responsibilities. Also, women have the same understanding as men of their role and of the skills needed to be board members. However, women fulfil their role differently than men. Women come to board meetings more prepared, take more notes and do more follow-up, and they also dare to ask tough questions to top management. Women directors bring a different point of view — representing different interests — to board discussions, have a different communication style, are not a part of the boys' club and have a social upbringing that might explain gender differences in the boardroom. Research limitations/implications This study could help boards and policymakers introduce diversity measures and provide ways to better integrate women into top decision-making groups such as board of directors. Practical implications This study's findings can help organizations include females in key decision-making groups such as board of directors. Social implications This study reveals that in the same social setting, with the same role and expectations, and the same understanding of their role, both genders continue to perform differently. Originality/value Based on direct evidence from board members, this study highlights how and why women do their role in the boardroom differently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.038 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".