The role of supply chain dynamics: Assessing the impact of logistics and distribution efficiency on the scaling up of MSMEs from Indonesian perspective
Bibliographic record
Abstract
This research endeavors to dissect the intricate dynamics surrounding the growth of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, specifically examining the influence of logistics and distribution efficiency, with a nuanced emphasis on the mediating role played by supply chain dynamics. Employing a quantitative methodology, the study administered questionnaires to MSME owners or managers, employing linear regression and mediation analysis techniques for data interpretation. The findings of this study illuminate a positive influence of both logistics and distribution efficiency on supply chain dynamics, subsequently fostering the expansion of MSMEs. A noteworthy revelation is the discerning mediating effect of supply chain dynamics, acting as a pivotal link between the efficiency of logistics and distribution processes and the substantial growth of MSMEs. This underscores the paramount importance of supply chain dynamics as a fundamental catalyst for the sustainable development of MSMEs. The novelty of this research lies in its innovative mediation approach, shedding light on the internal mechanisms that intricately connect logistics and distribution efficiency to MSME expansion. By delving into these relationships, the study offers a fresh perspective on how MSMEs can strategically bolster their growth trajectories. The practical implications are substantial, suggesting that a concentrated effort on optimizing supply chain management could serve as a strategic linchpin for supporting the expansion and sustainability of MSMEs in Indonesia. This study contributes valuable insights to the ongoing discourse on MSME development, advocating for a holistic strategy that encompasses logistics, distribution efficiency, and supply chain dynamics to fortify the foundations of sustainable growth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".