Enterprise Risk Management: A Literature Review and Agenda for Future Research
Bibliographic record
Abstract
The Enterprise Risk Management (ERM) process has heterogeneously developed across the world, although it represents a leading paradigm, supporting organizations to identify, evaluate, and manage risks at the enterprise level. Academics have studied the process, but there is no complete picture of the determinants and implications of such an integrated risk management process. Therefore, we present a systematic empirical literature review on ERM, based on a research protocol. The review highlights that the ERM literature can be divided into four general lines of research: the ERM adoption, the determinants of the ERM implementation, the effects of ERM adoption, and other aspects. In contrast to the richness of studies devoted to ERM engagement in small and medium-sized enterprises (SMEs), studies exploring ERM adoption in banks or insurance are relatively few. The literature review has revealed that the most frequently investigated effect of ERM is on firm performance. Little effort has been dedicated to the analysis of the effectiveness of ERM by its components and to institutional, individual, and organizational factors that affect ERM adoption. The study can serve as a starting point for scholars to explore research gaps related to ERM, while the practitioners can rely on the presented findings to identify the effects of the ERM implementation.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".