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.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".