The moderating effect of strategic momentum on the relationship between big data analytics capabilities and lean supply chain practices
Bibliographic record
Abstract
The present study aimed to explore the moderating role of strategic momentum in the relationship between big data analytics capabilities and lean supply chain practices in eight textile companies in Jordan. A quantitative research methodology incorporating a cross-sectional design was adopted to gather questionnaire-based responses to investigate the hypotheses put forth. The sample for the study consisted of 116 respondents, who were selected from a diverse group of senior executives from various fields, including IT, logistics, marketing, production, and strategic planning. These individuals possessed both knowledge and skills in data and business analytics disciplines. The data were analyzed utilizing SPSS version 28 and the PROCESS v3.5 macro developed by Andrew F. Hayes. The results revealed that the strategic momentum positively moderates the relationship between big data analytics capabilities and lean supply chain practices. These findings indicate that high levels of strategic momentum allow an organization to increase resources and focus on investing in and developing its big data capabilities, thereby supporting the implementation of lean supply chain practices.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".