Exploring nexus among big data analytic capability and organizational performance through mediation of supply chain agility
Bibliographic record
Abstract
The organization needs to improve on a steady basis. For this purpose, organizations must gauge their performance regularly. To achieve this purpose, the agility of the supply chain may play a key role. Therefore, this study was designed to explore the relationship between big data analytics, organizational flexibility, supply chain agility, and organizational performance. This study assessed the mediation effect of supply chain agility as well. The research design of the cross-sectional and research approach was quantitative. The data of this study was gathered from the retail sector employees. In total, 516 questionnaires were distributed using simple random sampling. The usable response rate of the study was 54.90%. The gathered data was examined through smart PLS 3.3.2. The findings of the study revealed that Supply chain agility plays a crucial role in improving the performance of the organization. The study also confirmed the mediating effect of supply chain agility. The findings of the study are helpful for the policymakers of the retail sector.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".