The Determinants Factors of an Effective Risk-Aware Culture of Firms in Implementing and Maintaining Risk Management Program
Bibliographic record
Abstract
The borderless and intense global competition has contributed much to the dynamics of the business world. This dynamic business environment contributes significantly to the existence of risks. In general, risk is the effect of uncertainty, which can normally deviate a firm from achieving its planned goals and objectives. However, deviation is not necessarily bad as besides than affecting a firm negatively, it could also lead to a positive outcome for the firm. From the perspective of implementing and maintaining an effective risk management program, developing a risk-aware culture is one of the important factors for consideration. The importance of culture has been mentioned in the risk management standard, ISO 31000, and numerously, in published studies and articles. However, there are no definite indications tools for firms to develop and embed the risk-aware culture into their organisational culture. According to Chugh (2013), risk-aware culture is a set of values shared within the organisation. It entails acceptance and knowledge about the risks surrounding the organisation. As such, to ensure an effective implementation and maintenance of a risk management program, it is crucial for an organisation to have a risk-aware culture. Fundamentally, culture relates to human behaviour and this requires identification of human-related determinants relevant to develop a risk-aware culture. Hence, this study shall look into the determinants for developing and effective risk-aware culture within firms. This study review the literature on the determinants factors of effective risk-aware culture of the various published articles, online articles, and surveys done by reputed experts in this field. The finding of this study shall facilitate better understanding for firms to develop and maintain their own risk-aware culture, leading to an effective implementation and maintenance of their risk management program.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".