Assessing the effect of business intelligence on supply chain agility. A perspective from the Jordanian manufacturing sector
Bibliographic record
Abstract
This study aimed to examine the effect of integrating Business Intelligence (BI) into the supply chain on supply chain agility, within the Jordanian manufacturing sector. Based on the resource-based view of the firm, this study developed and examined a research model, to achieve its goal. The impact of three dimensions of BI including managerial, technical, and cultural competencies was examined. Using an electronic questionnaire, data was gathered from 462 administrative personnel and employees. Structural equation modeling techniques were employed to analyze the data. Results revealed that the three dimensions of BI have statistically significant positive direct effects on supply chain agility. In addition to that, the results revealed that BI cultural competence has statistically significant positive direct effects on BI technical and managerial competencies. This study contributes to the literature on the role of BI in promoting supply chain agility, from the perspective of a developing country. The findings of this study are expected to help organizations' administrations in making better decisions regarding employing BI to achieve an agile supply chain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".