Exploring the drivers of environmental, social, and governance (ESG) disclosure in an emerging market context using a mixed methods approach
Bibliographic record
Abstract
Abstract With the economy evolving, business landscapes shifting, and regulations tightening, companies are increasingly integrating ESG criteria into their strategies and more transparent in their disclosures. The aim of this study is to explore the current state of ESG disclosure in an emerging economy (Morocco) and to identify the main motives and challenges faced by Moroccan companies and their impact on ESG disclosure practices. We used a mixed methods approach, based on a quantitative survey conducted among 66 experts, distributed equally between men and women and analyzed by PLS-SEM approach, as well as a qualitative method based on a series of semi-structured interviews with 19 experts in the field. We concluded that ESG reporting motives and challenges impact positively and significantly on ESG disclosure practices. Further, gender is moderating and strengthening the relationship between ESG reporting motives and practices. Indeed, ESG disclosure level is improving in the context of Moroccan companies and regulatory mechanisms provide effective framework for developing ESG disclosure practices. This study has important implications for policymakers, regulators, and companies operating in an emerging country context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".