The partnerships and logistics leadership in the SMEs: The impact of digital supply chain implementation
Bibliographic record
Abstract
Digital supply chains play an important role in improving the performance of small and medium enterprises (SMEs) in this digital era. There has been no research that analyzes the relationship between digital leadership, leadership, and partnerships. The aim of this research is to analyze the defect of digital supply chain implementation on logistics leadership and the impact of digital supply chain implementation on partnerships and logistics leadership partnerships. The method of this research is quantitative and data analysis uses structural equation modeling (SEM) partial least squares (PLS) using tools. SmartPLS 3.0 software data is used for processing the data. Research data is obtained by distributing online questionnaires to 589 SME owners in Indonesia determined using a simple random sampling method. The online questionnaire is designed using a Likert scale from 1 to 7 and distributed via social media. The stages of data analysis are validity testing, reliability testing and hypothetical testing. Based on the results of data analysis, it is concluded that digital supply chain implementation has a positive and significant effect on logistics leadership, digital supply chain implementation has a positive and significant effect on partnerships and logistics leadership had a positive and significant effect on partnerships. The novelty of this research is the creation of a correlation model for variable partnerships, logistics leadership and digital supply chain implementation. The managerial implication of this research is to encourage increased partnerships and logistics leadership and we conclude that SMES managers must implement digital supply chain implementation. The theoretical implication of this research is that a new correlation model of partnerships, logistics leadership and digital supply chain implementation in SMEs is created.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".