Enterprise risk management and business strategy on firm performance: The role of mediating competitive advantage
Bibliographic record
Abstract
This research aims to know and analyze the contribution of enterprise risk management and business strategy to the competitive advantage and company performance of shipping companies listed on the Indonesia Stock Exchange. The company performance and competitive advantage of shipping companies listed on the Indonesia Stock Exchange fluctuate and are decreasing. The enterprise risk which is not well-managed can result in a decrease in competitive advantage and company performance. The shipping companies having no competitive advantage will have poor performance and lose in the competition with similar companies. This research uses Path Analysis with the analysis tool of Partial Least Square SmartPLS3 and its path coefficient significance test using a t-test. The total number of samples is 65 consisting of 13 companies with five years of observation taken using the purposive sampling technique. The result of this research states that shipping companies in Indonesia can increase their company performance through enterprise risk management. So, the companies must practice enterprise risk management properly where the aspect of risk management is integrated into the company strategy. Shipping companies in Indonesia can also increase their company performance through competitive advantage so that they can increase their revenue. Shipping companies can increase their company performance through business strategy, so they must implement a business strategy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".