The Effect of Corporate Governance on the Use of Enterprise Risk Management: Evidence From Canada
Bibliographic record
Abstract
Abstract This article examines the use of enterprise risk management (ERM) by companies in Canada, the characteristics that are associated with the use of ERM, what obstacles companies face in implementing ERM, and what role, if any, corporate governance guidelines have played in the decision to adopt ERM. We obtained our data from the responses to a mail survey sent to Canadian Risk and Insurance Management Society members as well as telephone interviews with 19 of the respondents. The results indicate that 31 percent of the sample had adopted ERM and that reasons for adopting ERM include the influence of the risk manager (61 percent), encouragement from the board of directors (51 percent), and compliance with Toronto Stock Exchange (TSE) guidelines (37 percent). The major deterrents to ERM were an organizational structure that discourages ERM and an overall resistance to change. Although only about one‐third of companies indicated that they had adopted an ERM approach, evidence was clear that a larger portion of the sample was moving in that direction, as indicated by what changes they had observed in their companies in the past three years. These include the development of company‐wide guidelines for risk management (45 percent), an increased awareness of nonoperational risks by operational risk management personnel and an increased awareness of operational risks by nonoperational risk management personnel (49 percent), more coordination with different areas responsible for risk management (64 percent), and more involvement and interaction in the decision making of other departments. Contrary to what we expected, there was not a significant difference between firms that are listed on the TSE versus those that are not in terms of the propensity to use ERM. However, the fact that 37 percent of firms indicated that the TSE guidelines were influential in their decision to adopt ERM provides some evidence that the guidelines are influencing companies’ risk management strategies.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".