Risk Management in Small‐ and Medium‐Sized Businesses and How Accountants Contribute*
Bibliographic record
Abstract
ABSTRACT We investigate how owners of small‐ and medium‐sized enterprises (SMEs) perceive, make sense of, and practice risk management. Drawing on Schatzki's practice theory, we theorize on how and why risk management happens in SMEs. Thus, we fill a gap in the extant literature, which focuses almost exclusively on risk management within large organizations. We interview entrepreneurs and conduct site observations to gain insight into their risk management activities, the drivers that lead to the adoption of said activities, their attitudes toward risk management, and how their accountants may shape and contribute to risk management in SMEs. We find that rather than a specific set of formal processes, entrepreneurs view risk management as a mindset that emphasizes the preservation of key assets, creation of competitive advantages, and development of local talent and expertise. We observe practices that are mainly informal yet planned, deliberate, and fully integrated within the fabric of organizations that align with ideal forms of risk management. We also find that full‐time, in‐house accountants do help entrepreneurs with risk management, while external accountants, whose main activities relate to financial statement preparation and tax filings, do not systematically help entrepreneurs manage risk. We contribute to both the theory and practice of risk management by sharing empirical insights into how SME owners perceive, make sense of, and manage risk.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 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".