Environmental, Social and Governance Awareness and Organisational Risk Perception Amongst Accountants
Bibliographic record
Abstract
The relationships between accountants’ environmental, social and governance (ESG) awareness and their perceptions of organisational risk are examined in this study. The emphasis is on the operational, strategic, financial and compliance risks of business organisations. A total of 462 accountants in Hong Kong were included via stratified random sampling and snowball sampling to ensure population diversity. A stratified random approach was used to include factors such as age, gender, income and experience, and snowball sampling amongst professional networks was used to ensure representativeness. A significant positive relationship exists between ESG awareness and risk perception, with environmental and governance factors emerging as the strongest predictors. Accountants with deep ESG awareness, especially in the aforementioned areas, can successfully identify and manage nontraditional risks such as regulatory changes and environmental threats. The findings highlight the need for institutionalising ESG-focused education in accounting and corporate governance to improve risk management capabilities. Increased ESG awareness can ensure responsible and sustainable business behaviour. Future research can expand the sample of accountants to executives and use longitudinal designs to capture the dynamic nature of ESG awareness and risk perception.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".