The role of company competitiveness as mediation variable the impact of supply chain practices on operational performance
Bibliographic record
Abstract
This study aimed to explain the role of supply chain practices on operational performance, and supply chain practices on company competitiveness and to analyze the impact of company competitiveness on operational performance. In addition, we also investigate the impact of supply chain practices on operational performance through role of company competitiveness as mediation variable. The study was conducted in South Sulawesi Province, Indonesia. Primary data were collected by questionnaire instrument from 108 food and beverage companies. Method of analysis was both descriptive statistical analysis, and Structural Equation Modelling (SEM). The study also used Sobel test to determine significance level of mediation role in the model. The results show that supply chain practices could give a positive impact on operational performance. Supply chain practices also gave a positive impact and significant on company competitiveness. Then, company competitiveness had a positive impact and significant on operational performance. Additionally, supply chain practices also had a positive impact on operational performance indirectly through the role of company competitiveness as mediation variable. Hence, supply chain practice was the most important variable to increase both company competitiveness and operational performance. Each company is recommended to implement this variable as competitive weapon in order to get a better operational performance and competitiveness as well.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".