Board of director's diversity and earnings management : the moderating effect of the board's roles
Bibliographic record
Abstract
Purpose – This study aims to explore the influence of Board diversity on earnings management in two aspects: accruals earnings management (AEM) and real earnings management (REM). The study also examines the Board of director's roles – monitoring and advisory functions – as a moderator on the link between Board diversity and earnings management. Design/methodology/approach – This paper uses fixed-effect regression analysis of a sample of 13522 firm-year observations in six developed countries around the world: Australia, Singapore, Hong Kong, Canada, the UK, and the US, from 2016-2020. In this study, absolute values of abnormal discretionary accruals are employed as a proxy for AEM in the cross-sectional modified version of Jones (1991) and Dechow & Dichev (2002). We also employ Roychowdhury (2006) 's empirical models as a proxy for REM, representing the manipulation of real activities. Findings – The findings indicate that Board diversity has a negative relationship with AEM, but a positive relationship with REM. In this regard, there is evidence that sample companies may switch between earnings management strategies, shifting from AEM to REM and employing them as substitutes. Additionally, the research confirms that Board monitoring and advisory roles can reduce earnings management. However, the negative link between Board diversity and AEM is less pronounced when two Board roles are stronger. The findings imply that 'the substitutive role', which balances Board diversity and Board roles in these effects, has more influence on AEM. In contrast, dual Board roles enhance the link between Board diversity and REM, but this relationship is not statistically significant. The results suggest that Board roles play a complementary function in improving earnings quality as measured by REM. Originality/value - The authors add to the body of knowledge on accounting quality and corporate governance by pointing out how Board diversity is linked to better earnings quality and lower earnings management in publicly listed companies worldwide. Moreover, this paper adds to prior literature about the Board of directors and accounting quality by identifying additional substitutive and complementary aspects of the Board of directors' roles. In doing so, this study applies the multi-theoretical perspectives – human capital theory, agency theory, and resource dependence theory – to examine these effects.
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 | 0.003 | 0.011 |
| 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 | 0.001 |
| 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".