A review of <i>ex ante</i> and <i>ex post</i> materiality measures, and consequences and determinants of material disclosures in sustainability reporting
Bibliographic record
Abstract
Purpose The purpose of the review is to synthesize the research on materiality measures of sustainability reporting and highlight how preparers, users, auditors, regulators and other stakeholders assess or determine the materiality in sustainability reporting. The review further summarizes the findings on consequences and determinants of material disclosures in sustainability reporting. Several directions for future research are also discussed. Design/methodology/approach This study provides a systematic review of materiality measures developed in the context of sustainability reporting. This synthesis of the literature summarizes the existing methodologies of measuring materiality. It also evaluates the strength and limitations of existing methods and approaches of measuring materiality in sustainability disclosures. Findings We find that the ex post materiality measures are simplistic and unidirectional in nature and ex ante materiality measures lack external validity and are generally narrow in focus – for example, focused on single firms or industries. Another major limitation in the current literature is the absence of robust empirical investigation of double materiality in sustainability reporting and a vast majority of the measures are developed without stakeholder engagement. Lastly, we document that the findings on determinants of material disclosure are fragmented and inconclusive and that the literature on consequences of material disclosure is rather un-explored. Originality/value The study explains the connections and differences between the various materiality measures. We document that materiality is measured in two distinct ways, ex ante and ex post and often times without stakeholder engagement. Moreover, given that a vast majority of the measures rely on manual content analysis, we find that they suffer from reproducibility and scalability.
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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 | 0.029 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".