Enterprise Risk Management and the Financial Reporting Process: The Experiences of Audit Committee Members, <scp>CFO</scp>s, and External Auditors
Bibliographic record
Abstract
Abstract The recent financial crisis has brought to the forefront the need for companies to effectively manage their risks. In this regard, one approach that has gained prominence is enterprise risk management ( ERM ). Importantly, little is known about the link between ERM and the financial reporting process. This link is critical, because it is imperative that financial reporting adequately depicts the financial status (e.g., valuations, estimates) and associated risks of a company as revealed by ERM . Additionally, from an auditing perspective, ERM affects the risks of misstatement, which should impact audit planning. Accordingly, the objective of this study is to examine the experiences of audit partners, CFO s, and audit committee ( AC ) members (“the governance triad”) on the link between ERM and the financial reporting process. To determine whether members of the governance triad focus on monitoring, strategy, or both, we also examine their definition of and experiences with ERM with respect to agency and/or resource dependence theory. To address these issues, we conduct semistructured interviews of experienced individuals that form the governance triads from 11 public companies. There are three major findings from our study. First, importantly, all three types of participants see a strong link between ERM and the financial reporting process. Second, despite recognition of the broad nature of ERM , the predominant experiences of the actual roles played by triad members center on agency theory, while resource dependence may be relatively underemphasized by all triad members. Finally, CFO s and AC members indicate that auditors may be especially underutilizing ERM in the audit process, suggesting an “expectations gap.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".