The influence of information technology integration on firm performance through supply chain quality and supply chain resilience
Bibliographic record
Abstract
Products produced by manufacturing companies are always required to meet the requirements set by customers. Manufacturing companies need to maintain product quality by involving internal company components and external partners by using integrated information technology to become supply chain quality integration. All components in the supply chain flow must be committed to maintaining product quality in their respective roles. The research aims to ensure that technology information integration can impact firm performance through supply chain quality integration and resilience. Data collection on manufacturing companies in Java using purposive sampling found 162 companies that had received ISO certification. Respondents were determined to be employees at the middle management level and had worked for a minimum of 3 years. Data processing used partial least squares version 4 to answer all research hypotheses. The data processing results showed that information technology integration influenced supply chain quality integration by 0.588 and supply chain resilience by 0.523 and had no significant effect on increasing firm performance. Supply chain quality integration influences supply chain resilience by 0.288 and increases firm performance by 0.496. Lastly, Supply chain resilience has a positive and significant effect on increasing firm performance by 0.169. The research results provide a practical contribution to the company's top management in maintaining the role and function of technology information integration and the ISO system in maintaining supply chain quality integration. Theoretical contribution to enrich the theory of total quality management and supply chain management.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".