Rethinking operational decisions making: Strategic drivers from management commitment, supply chain transparency and integration
Bibliographic record
Abstract
This study aims to examine the influence of commitment management on supply chain integration, supply chain transparency, and operational decision-making in manufacturing companies in Indonesia. Amidst the increasing complexity of supply chains and the demands for rapid, data-driven decision-making, companies need to build integrated and transparent systems, supported by strong commitment from top management. This study used a quantitative approach with a survey method of 128 respondents from manufacturing companies in Java, and the data were analyzed using Partial Least Square (PLS) techniques. The results showed that commitment management significantly influenced supply chain integration, supply chain transparency, and operational decision-making. Supply chain integration was also shown to influence supply chain transparency, but not significantly on operational decision-making. Meanwhile, supply chain transparency significantly influenced operational decision-making. A mediation test showed that the indirect influence of commitment management on operational decision-making through supply chain integration and transparency was not significant. This indicates that operational decision-making still relies heavily on the direct involvement of top management. This study provides a theoretical contribution in enriching the understanding of the role of management commitment in supply chain-based operational decision-making systems. Practically, the results of this study recommend strengthening the role of middle managers, decision-making training, and the implementation of integrated information systems to improve the effectiveness of operational decisions in real time.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".