Quality of information sharing as a moderator: An investigation of the relationship between supply chain management strategies and competitive advantage in Saudi Arabian manufacturing companies
Bibliographic record
Abstract
The purpose of this research is to investigate the influence of supply chain management methods on competitive advantage in Saudi Arabian manufacturing firms, with a particular emphasis on the function of information sharing quality as a moderating variable. The data received from a sample of Saudi manufacturing enterprises were analyzed using a structural equation modelling technique. According to the findings, customer relationships, the level of information sharing, and strategic supplier partnerships all have strong beneficial impacts on competitive advantage. However, the influence of information sharing quality alone on competitive advantage was shown to be statistically negligible. These results emphasize the necessity of developing strong customer relationships, encouraging effective information exchange procedures, and creating strategic collaborations with suppliers to gain a competitive edge. The research adds to the current literature by providing insights unique to Saudi Arabian industrial firms. The results are important for managers and decision-makers developing competitive supply chain management strategies. Future studies may investigate other factors and dimensions, as well as perform longitudinal studies, to better understand the structures of supply chain management and competitive advantage in the Saudi context.
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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 | 0.018 |
| 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 | 0.003 | 0.001 |
| 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".