Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques
Bibliographic record
Abstract
Economic, social and governance (ESG) have become topical subjects amidst the deleterious effects of climate change, inequality and similar pressing challenges facing the people and the planet. The main objective of this study was to rank the importance of both the pillars within the ESG model and the five indicators beneath each pillar for the purposes of executive compensation plans through the Analytical hierarchical process (AHP). It is not known which pillar within the ESG model should be prioritised by companies operating in a developing economy context such as South Africa, and neither is it known which of the available indicators should be prioritised when designing executive compensation plans. AHP and pairwise comparison is employed in prioritising important pillars and indicators. The environmental pillar is identified to be the most important among the three pillars. Indicators that are prioritised mirror both the environmental and socio-economic challenges prevalent in South Africa as an emerging economy. Companies’ boards, remuneration committees, investors and policymakers can use the ESG-based indicators that have been prioritised in this study in designing the executive compensation plans. AHP and pairwise comparison are novel approaches used to prioritise the important pillars within the ESG model and the underlying indicators.
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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 | 0.077 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".