The impact of cloud computing on supply chain performance the mediating role of knowledge sharing in utilities and energy sectors
Bibliographic record
Abstract
The purpose of this research was to analyze the increased performance improvement in the supply chain related to the energy and utilities sector of Jordan through cloud computing, also mediated by knowledge sharing. 150 respondents were analyzed. Research suggests that there was a strong positive relationship between cloud computing and supply chain performance. In addition, cloud computing had a strong and positive correlation with the practice of knowledge sharing. The result indicates that companies with a culture of knowledge sharing among employees were more likely to incorporate the use of cloud computing. The study also indicates that cloud computing adoption had enhanced supply chain performance in the utility and energy sector within Jordan. Therefore, the study also finds that there was a strong and positive relationship between knowledge sharing and well overall performance of supply chain activities. This points to the role that knowledge-sharing practices play in improving the performance of the supply chain within Jordanian utilities and energy sectors. Furthermore, the findings of the mediation study provide strong evidence in support of our hypothesis that knowledge sharing plays a major role as a mediator relating to the relationship between cloud computing adoption and supply chain performance. The observed mediation effect suggests that the positive impact of cloud computing implementation on supply chain performance can be attributed to some extent to its facilitating knowledge exchange practices. However, this research improves the understanding of relationships among cloud computing adoption implementations and information structure flow as well as supply chain performance within Utilities These results emphasize the importance of cloud technologies as information drivers that would lead to enhanced performance in terms of supply chain operations. These are insights that can be used by organizations and businesses aiming at improving their competitiveness and efficiency levels in these sectors.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".