The challenges of implementing enterprise risk management: a study on manufacturing companies in the Tehran Stock Exchange
Bibliographic record
Abstract
Implementing enterprise risk management (ERM) is one of the important solutions in reducing the uncertainty and survival of companies. The study aims to explore the challenges of implementing ERM and possible solutions that may address these challenges in the manufacturing companies listed in the Tehran Stock Exchange. In the study, semi-structured interviews with the ERM experts among selected Iranian manufacturing companies are used. The identified challenges related to implementing ERM include intra-organisational and extra-organisational challenges. Intra-organisational challenges include risk governance, risk culture, ERM process, and infrastructures. Besides, extra-organisational challenges include the roles of government and policymakers, political and economic conditions, international restrictions, and the lack of a competitive environment (exclusiveness). Our study found that establishment of a risk committee, strengthening risk culture through ERM training top management commitment to ERM and the provision of sufficient funds were the factors that may be used to mitigated ERM implementation challenges.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".