The Effect of Enterprise Risk Management Practice on SME Performance
Bibliographic record
Abstract
"Research Aims - This study aims to identify the effect of Enterprise Risk Management (ERM) on Small and Medium Enterprise (SME) performance. Design/methodology/approach - This study employed a multiple regression analysis. SME performance was treated as dependent variable, whereas ERM was the independent variable. Research Findings - Multiple regression analysis indicated that ERM has a significant effect to- wards firm performance. However, only one of the ERM elements namely objective determination has a significant effectt on SME performance. Theoretical Contribution/Originality - This study contributes to the body of knowledge from the standpoint of ERM by testing the effect of each element of ERM described under the Committee of Sponsoring Organizations of the Treadway Commissions (COSO) towards firm performance. Per- haps, each element of the ERM might has different effect towards an organization. Thus, Resource Based View (RBV) Theory was supported which hold that the organisational resources are the main factor to influence the organisational performance. Managerial Implication in the South East Asian context - ERM conducted in SMEs are expected to be able to develop strategies in minimising the risks that may or may not be faced by SME firms. In fact, an effective risk management can assist SME managers and owners in achieving their de- fined business objectives. Thus, risk management enhances the firm’s value, maximise profitability, and consequently improve SME performance. Research limitation & implications - This study has improved the measurement of ERM practices among SMEs and identified ERM elements that affect SME performance in particular."
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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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".