Business strategy, enterprise risk management and organizational performance
Bibliographic record
Abstract
Purpose This paper aims to identify the influence of business strategy on enterprise risk management (ERM) adoption and organizational performance (OP). In addition, the mediation effect of ERM on the relationship between business strategy and OP is assessed. Design/methodology/approach A cross-sectional analysis of primary data gathered from 174 public listed companies in Malaysian Bourse through survey was conducted. Findings Companies with cost leadership business strategy are more eager to implement ERM compared to companies with differentiation strategy. The results also indicate that ERM implementation has a significant positive impact on OP. Though ERM is a partial mediator of the relationship between cost leadership strategy and OP, it does not mediate the relationship between differentiation strategy and OP. Research limitations/implications One of the limitations of this study was the small number of respondents, comprising only 174 public listed companies. In addition, the manifest variables adopted from previous studies may not be the best indicators to measure latent variables. Nonetheless, this study fills the gaps in ERM studies by determining the impact of different kinds of strategy on ERM adoption and investigating the mediating effect of ERM on the relationship between business strategy and OP. Practical implications Although the trend in Malaysia seems to move toward ERM adoption, evidence shows that it is not widely practiced among Malaysian firms. Directors of Malaysian companies can understand better the impact of enterprise business strategy on the adoption of risk management and how ERM influences OP. The results of this study also provide valuable insights for the corporate governance regulatory authorities. Originality/value This paper is among the few to assess the impact of firm’s strategy on ERM adoption and to determine the mediation effect of ERM on the relationship between business strategy and OP.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".