Exploring the relationship between sustainable supply chain and sustainable development goals on the financial performance of SMEs
Bibliographic record
Abstract
This research aims to analyze the relationship between sustainable supply chains on financial performance, Sustainable Development Goals (SDGs) financial performance and Sustainable supply chains on Sustainable Development Goals (SDGs). This research uses a quantitative survey method, research data was obtained by distributing online questionnaires through media social to 390 respondents belonging to SMEs, and respondents were determined using the simple random sampling method. Data analysis used Structural Equation Modeling (SEM) Partial Least Square (PLS) with data processing tools using SmartPLS 3.0 software. The questionnaire was designed using a Likert scale of 1 to 7. The independent variable in this research is the sustainable supply chain, the mediating variable is Sustainable Development Goals (SDGs) and the dependent variable is financial performance. The stages of data analysis are validity and reliability testing, significance or hypothesis testing, and mediation influence testing. The results of data analysis show that the Sustainable supply chain has a positive and significant relationship to financial performance, Sustainable Development Goals (SDGs) have a positive and significant relationship to financial performance and the Sustainable supply chain has a positive and significant relationship to Sustainable Development Goals (SDGs). To improve the financial performance of SMEs, they must implement a sustainable supply chain in their supply chain, namely from supplier to customer. To improve the financial performance of SMEs, they must implement Sustainable Development Goals (SDGs) in their management system. Implementing sustainability in the supply chain is important to increase operational efficiency and reduce negative impacts on the environment and society.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".