The Consideration of Diversity in the Accounting Literature: A Systematic Literature Review
Bibliographic record
Abstract
This paper presents the findings of a systematic literature review to identify research trends and future research opportunities focusing on how diversity is considered in the accounting literature. We retrieved 428 studies that consider diversity published in accounting journals over the period 1979–2021 from the Scopus database. Our analysis shows that research about diversity in accounting has significantly increased over the last five years, with the majority focusing on gender and on questions related to corporate governance. We identify four major themes on the topic of diversity: (i) diversity in the accounting profession; (ii) diversity in corporate governance; (iii) diversity in audit and accounting processes; and (iv) the influence of preparing and reporting organizational information on diversity. The results of these studies highlight that having more organizational diversity and reporting on diversity have positive implications for organizational performance; however, at the same time, this often creates or perpetuates power imbalances. A question remains as to whether this increasing focus on diversity in the literature reflects increasing attention to diversity in accounting to promote equity and social justice. This paper represents the first systematic review of diversity considerations in accounting, and helps scholars understand the main topics explored so far and, where research should focus next.
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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 | 0.044 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.070 | 0.053 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".