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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".