The mediating role of sustainable supply chain management on entrepreneurship strategy, social capital and SMEs’ financial and non-financial performance
Bibliographic record
Abstract
Research related to entrepreneurial strategy variables, financial and non-financial performance, social capital and supply chain management in SMEs has not been widely carried out in Indonesia. The purpose of this study is to analyze the relationship between entrepreneurial strategy on financial and non-financial performance, social capital on financial and non-financial performance and sustainable supply chain management on financial and non-financial performance in SMES. The research method is quantitative with the online survey method. The data collection method is by distributing online questionnaires to 690 SMEs owners in Indonesia determined by simple random sampling. The questionnaire is designed using a Likert scale of 7. Data processing used structural equation modeling with SmartPLS 3.0 software tools. The results of data processing show that entrepreneurship strategy (ES) had a positive and significant effect on sustainable supply chain management (SSCM), social capital (SC) had a positive and significant effect on sustainable supply chain management (SSCM), entrepreneurship strategy (ES) had a positive and significant effect on SMEs financial and non-financial performance (SFNFP), social capital (SC) had a positive and significant effect on financial and non-financial performance (SFNFP) and sustainable supply chain management (SSCM) had a positive and significant effect on financial and non-financial SMEs financial performance ( SNFFP).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".