Unlocking the Path to Sustainability: A Hierarchical Model for Understanding Corporate Barriers to ESG Reporting Adoption
Bibliographic record
Abstract
Environmental, social, and governance (ESG) reporting is a vital force behind the advancement of sustainable corporate practices and goes beyond simple compliance. In order to better understand the elements influencing this process, this study looks at the obstacles that prevent corporations from adopting ESG reporting. Using total interpretive structural modeling (TISM), an empirical model was created to show the hierarchical relationships between the main obstacles found by a literature research and expert survey. We identified barriers at the strategic level, such as resource shortages, unclear stakeholder demand, and structural limits; at the functional level, such as governance issues and cultural resistance; and at the efficiency level, which directly impacted adoption. Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis clarified the driving and dependence relationships among these barriers. The findings contribute to refining theoretical perspectives on ESG adoption and offer practical insights for corporate managers, policymakers, and organizations striving for effective sustainability practices. Recommendations aim to enhance sustainability policy formulation, operational practices, and governance frameworks, ultimately supporting organizations in their efforts to adopt ESG reporting sustainably and resiliently.
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".