Building manufacturing operational performance through management commitment, information technology adoption, supply chain transparency and practices
Bibliographic record
Abstract
Transparency in the supply chain is crucial for enhancing integration, efficiency, and responsiveness, but it also carries the risk of strategic information leakage. Management commitment and the effective use of digital technology are essential to creating safe and transparent environments. This study aims to investigate the impact of management commitment on the adoption of information technology, supply chain transparency, and practices that enhance operational performance. Data collection was conducted at 221 manufacturing companies that have adopted information technology as their primary system. Testing the research instrument using SmartPLS version 4 data analysis showed a good goodness-of-fit model. The study's results indicate that management commitment has a significant influence on the adoption of information technology and supply chain practices. However, its impact on supply chain transparency is less pronounced. The resulting operational performance is not directly determined by supply chain transparency, but rather by the adoption of information technology and the implementation of effective supply chain practices. The adoption of information technology has an impact on supply chain practices through supply chain transparency. The results of this study contribute practically to company management by enabling the update of information technology and the implementation of supply chain practices and transparency, thereby facilitating continuous evaluation and control of the company system. The results of this study contribute to enriching the theory of digitalization in the manufacturing industry's supply chain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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".