Supply chain management and logistic presentation: Mediation effect of competitive advantage
Bibliographic record
Abstract
Supply chain management (SCM) practices have become strategic resources and capabilities for enhancing both competitive advantage logistic performance (ORGPER). However, it is not clear how SC Practices influence logistic performance in the agribusiness context. However, the mechanism of SCMPs effects is not yet understood since extant literature has produced mixed results. Hence, this study sought to test the impact of mediation of the competitive advantage of relations between SCMP and Reperform the point of view of Kenya's dairy supply chain. The study examined four estimates that were tested using partial minimum square structural equation modeling (PLS-SAME) techniques to work out the purpose of the study. Across-departmental survey design has been used to collect preliminary data from109 dairy cooperatives in thirteen major dairy producing counties in Kenya. The results reveal that SCM practice has a positive and significant effect on CA(P=0.730) and ORGPER (P=0.237). In addition, THERE is a positive, statistically significant effect on THECAORGPER (P=0.522). Further results show that CA mediates the relationship between SCMP and ORGPER. Consequently, the study concludes that SCMPs first generate CA, which in turn enhances ORGPER in a logistic sense. Theoretically, the study provides insights on the resource-based view theory as well as a conceptual framework for its validation. Similarly, the study informs managers and policymakers in knowing specific SCMPs to focus on to enhance CA and ORGPER of the dairy cooperatives in Kenya.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".