The impact of SCM integration on business performance through information sharing, quality integration and innovation system
Bibliographic record
Abstract
Integration with external supply chain partners can reduce the risk of process and product development disruptions. Hence, the companies should anticipate and prepare for any risk that could emerge in the supply chain network. This study aims to analyze the role of supply chain integration, information sharing, supply chain quality integration, and innovation systems in improving business performance in the manufacturing industry. The study surveyed manufacturing companies located in East Java, as many as 258 companies. Data was collected using questionnaires designed with a five-point Likert scale and distributed through Google Forms and company visits. 222 questionnaires were distributed through Google Forms, and 36 were distributed during company visits. The smartPLS software version 4.0 was used for descriptive analysis and hypothesis testing. The results showed that supply chain integration positively impacts information sharing, quality integration, and innovation systems. Information sharing significantly supports the implementation of quality integration and innovation systems. However, quality integration does not affect the innovation system. Likewise, innovation systems have no impact on improving business performance. Many manufacturing companies in East Java had not done innovation systems appropriately after the COVID-19 disruption as the company still focused on current processes and products to maintain company sustainability. Furthermore, information sharing, and supply chain quality integration significantly improve business performance. The results of this study could contribute to managers building close partnerships with external parties to maintain the quality of processes and products. Business owners must also consider using the latest technology for process and product innovation. These findings enrich the current supply chain management theory, particularly with quality integration and innovation systems.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".