A Systematic Literature Review on Transparency in Executive Remuneration Disclosures and Their Determinants
Bibliographic record
Abstract
There are ongoing debates globally regarding excessive executive compensation, the perceived weak link between pay and performance, and the widening inequality gap. The South African corporate governance code King IV’s Principle 14 addresses the need for fair, responsible, and transparent remuneration. At the same time, the newly enacted Companies Amendment Act No. 16 of 2024 in South Africa emphasizes transparency in compensation, shareholder voting, and responding to shareholder feedback. This study conducts a systematic literature review of 30 articles on the transparency of executive remuneration disclosures and their determinants by analyzing Scopus-indexed articles published between 2010 and 2023, selected through specific keyword searches. The findings suggest an increasing focus on research regarding the disclosure of executive compensation, predominantly conducted in the Global North and primarily framed through agency theory. Studies exploring the factors influencing executive remuneration and the relationship between pay and performance are prevalent, with mixed results generally indicating a positive connection. Firm size emerges as a key factor in transparency, and many studies employ binary scoring to evaluate whether executive compensation disclosure is present. This paper provides valuable insights for investors, analysts, and policymakers and adds to the current understanding of executive remuneration transparency.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".