The relationship of inspirational leadership and green supply chain management to reinforce the performance of SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this research is to analyze the relationship between inspirational leadership and performance and the relationship between green supply chain management and performance improvement. This research method is quantitative. Research data was obtained by distributing online questionnaires via social media, the questionnaire was designed using statement items with a Likert scale of 1 to 7. The respondents for this research were 678 SMEs owners in Indonesia who were determined using the simple random sampling method. The data analysis technique for this research uses partial least squares (PLS) structural equation modeling (SEM) analysis with SmartPLS 3.0 software data processing tools. The stages of research analysis are validity testing, reliability testing and hypothesis testing (significance). The results of the research show that inspirational leadership has a positive and significant effect on performance. With the existence of inspirational leadership on HR performance it will have a positive effect if employees get trust and good examples from leaders, and employees get inspiration and motivation so they can create new innovations that make the company more advanced. Green supply chain management has a positive and significant effect on improving performance. The concept in green supply chain management provides the possibility for organizations to improve process efficiency, waste recycling management, the ability to attract new suppliers and consumers, organizations can save costs, reduce delivery times through collaboration with suppliers and consumers which can ultimately improve operational performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it