Multi-level analysis on determinants of sustainability disclosure: a survey of academic literature
Bibliographic record
Abstract
Purpose This study uses a multi-level framework to systematically summarize and synthesize the empirical literature on determinants of sustainability disclosure. Design/methodology/approach This review study is based on 159 empirical studies examining determinants of sustainability disclosure and published in Charted Association of Business Schools (CABS) ranked journals over the last 40 years. Findings Companies are experiencing multi-level pressures for sustainability disclosure. Macro-level variables include political, legal, social-cultural and international pressures. Meso-level factors include customers' concerns, shareholders’ and investors' demands, industry-level variables and media coverage. Micro-level factors include the firm-level governance mechanisms, executives' reporting attitude and role of sustainability promoting institutions. Unlike in developed markets, companies in developing markets feel minimal public pressure for sustainability disclosure but rather are influenced by international NGOs, the media and international buyers. Multi-level and multitude of pressures for sustainability disclosure explains the widely observed differences between studies. Originality/value This research presents the most extensive systematic review of the extant sustainability disclosure literature and is the first study to group determinants into micro-, meso- and macro-level components using multi-level analysis.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".