Investigating the relationship between information quality, system quality, service quality, and supply chain performance in the manufacturing sector of Saudi Arabia: An empirical study
Bibliographic record
Abstract
This research investigates the links between information quality, service quality, system quality, and supply chain performance in Saudi Arabia's manufacturing sector. The goal is to provide insights into the elements that impact supply chain outcomes and to identify critical performance drivers. Data was acquired from a sample of manufacturing organizations, and the correlations were analyzed using a structural equation modeling technique. Information quality, service quality, and supply chain performance are all shown to have substantial positive correlations. Higher levels of information quality and service quality relate to enhanced supply chain performance in the manufacturing sector. However, system quality was shown to have a comparatively smaller influence on supply chain performance, suggesting that investments in information management systems and service delivery methods may offer higher returns. The reliability and validity of the measuring scales employed in this research were evaluated and determined to be strong, assuring the precision and consistency of the findings. These results add to the current research by identifying the unique elements that influence supply chain performance in Saudi Arabia's manufacturing sector. The study has many implications for practitioners in the manufacturing sector as it discusses the significance of investing in information management systems, providing high-quality services, and continually evaluating and improving supply chain performance. Organizations may improve their competitiveness and create better supply chain results by concentrating on these areas. Additional elements and possible moderating or mediating variables may be investigated in future studies to acquire a better knowledge of the dynamics that impact supply chain performance. Overall, this research offers significant insights for practitioners and decision-makers in the manufacturing industry, directing them towards more appropriate methods to maximize supply chain performance and achieve long-term success.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".