Optimizing manufacturing firms' operational performance through supply chain integration: Moderating effect of supply chain complexity
Bibliographic record
Abstract
This study investigates the relationship between integration, complexity, and operational performance in the industrial sector of Saudi Arabia. The sample comprised manufacturing firms in Saudi Arabia, and data were collected through the distribution of questionnaires. Supplier integration, customer integration and internal integration were examined as factors influencing operational performance, with supply chain complexity considered as a moderating variable. The findings highlight the positive impact of integration on operational performance in the Saudi Arabian industrial sector. The measurement scales used in the study demonstrated high reliability and internal consistency. Discriminant validity analysis confirmed the distinctiveness of the constructs. Structural model analysis revealed significant positive relationships between customer integration, internal integration, supplier integration, supply chain complexity and operational performance. The results emphasize the importance of fostering integration within and outside the organization to enhance operational performance. Furthermore, the moderating effect of supply chain complexity suggests that the relationship between integration and operational performance varies according to the complexity of the supply chain. Overall, this study contributes to the understanding of integration, complexity, and operational performance in the context of the Saudi Arabian industrial sector. The findings have practical implications for industrial companies, providing insights into strategies for improving operational performance through integration initiatives and consideration of the unique characteristics of the supply chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".