Big data and sustainable supply chain management of hypermarkets in Jordan: An experimental study using structural equation modeling approach
Bibliographic record
Abstract
The objective of the study is to identify the impact of big data on sustainable supply chain management. The current research was conducted on hypermarkets in Jordan. Many of these hypermarket brands are widely scattered in Jordan, for instance, Carrefour, Kareem, Safeway and more. Accordingly, the target population in the current research was hypermarkets managers in Jordan as they are responsible for formulating such strategies in the companies they work for. A convenience sample was selected from the target population that included 770 managers based on the sample size formula. The study hypotheses were tested by covariance based structural equation modeling (CB-SEM). The study results showed the impact of each big data dimension on sustainable supply chain management. Based on this result, the researchers recommend the hypermarkets in Jordan to use modern and diverse methods for accurate collection of reliable data and save it in organized ways, and to employ advanced programs to analyze it and extract information of high value.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".