Enterprise risk management and supply chain management: The mediating role of competitive advantage and decision making in improving firms performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The complexity of risk management and supply chain optimization in the business context, especially in financial institutions such as banking, highlights several factors that require special attention. In the banking sector, where risk and operational smoothness are crucial, risk management and supply chain optimization play pivotal roles in maintaining stability and competitiveness. The objective of this research is to explore the extent to which the implementation of ERM (Enterprise Risk Management) and SCM (Supply Chain Management) can create a competitive advantage, influence decision-making, and ultimately impact company performance. The research methodology employed is quantitative. Data collection was conducted through the distribution of Likert-scale questionnaires with a score range from 1 to 5. The sample selection process utilized random sampling techniques, involving managers and staff working in State-Owned Enterprises (SOE/BUMN) in Indonesia. The study analyzed 263 samples, with data collected from February 2023 to June 2023. Structural Equation Modeling (SEM) with SmartPLS software facilitated data analysis. The results indicate that ERM significantly influences competitive advantage and decision-making, but it does not directly impact company performance. Similarly, SCM has a significant positive impact on competitive advantage and decision-making but does not directly affect company performance. Competitive advantage, in this study, did not prove to enhance firm performance or act as a mediator connecting ERM and SCM to company performance. However, decision-making significantly influences company performance and serves as a significant mediator in the relationship between ERM and SCM concerning company performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it