Board gender diversity and firms' social engagement in the Gulf Cooperation Council (GCC) countries
Bibliographic record
Abstract
Purpose The Gulf Cooperation Council (GCC) countries form a unique socioeconomic environment that makes the conclusions of the prior literature not likely to be applicable. GCC countries have huge oil reserves, yet they are aiming at reducing oil dependency through enhancing transparency, increasing foreign direct investments and reforming their governance structure. Their firms are mainly family owned and have low female representation in leadership positions. The study seeks to fill a literature gap by providing a business case supporting the call for gender diverse boards for better governance. Design/methodology/approach The study examines a sample of GCC-listed firms for the years 2009–2018. Three measures are used to proxy for firm social engagement, namely, CSR strategy score, environmental, social and governance (ESG) disclosure score and social pillar score. To ensure whether the presence of women on board or the number of women on board is influential on social engagements, the authors use the existence of women on board and the percentage of women on board variables. Data are collected using Thomson Reuters, and generalized least squares (GLS) panel data regression is used to estimate relationships. Findings The authors find that female representation on GCC corporate boards is increasing, yet in a slow path. The reported results support the role of women on boards in prompting firms' social agenda and enhancing the level of sustainability reporting. The results also show that female board representation supports the implementation of climate change policy, business ethics policy and health and safety policy. Originality/value The paper evidence the add value of women participation on GCC corporate boards in enhancing boards' functionality and governance. The empirical findings encourage firms and policymakers in the GCC countries to increase the share of females on corporate boards to improve firms' citizenship and facilitate attracting foreign investors.
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 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 | 0.007 | 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 | 0.034 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".