Does Board Cultural Diversity Contributed by Foreign Directors Improve Firm Performance? Evidence from Australia
Bibliographic record
Abstract
Australian firms hire an increasing number of foreign directors who bring various cultural perspectives to their boards’ conversations. We evaluate the effect of board cultural diversity contributed by foreign directors on firm performance for a sample of Australian companies, constituents of ASX200. We employ Hofstede’s six cultural dimensions to estimate board cultural diversity. We document a positive relationship between board cultural diversity and firm performance as measured by Tobin’s q and ROA after controlling for various board and firm characteristics. This suggests that more culturally diverse boards may bring benefits to their firms that outweigh the potential costs of conflict and miscommunication caused by cultural differences. Our finding holds after controlling for firm and time fixed effects, implementing an instrumental variable approach, controlling for a firm’s foreign operations and presence, and using alternative cultural diversity measures. We find that not all aspects of cultural differences matter, and it is the diversity in masculinity, uncertainty avoidance, and long-term orientation dimensions that positively determine firm performance. This finding on the positive effect of board cultural diversity for Australian firms contrasts with the evidence from other countries, highlighting that the value of cultural diversity can differ across countries and over time.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".