The role of strategic entrepreneurship and social capital on sustainable supply chain management and organizational performance
Bibliographic record
Abstract
This study aims to examine the effect of entrepreneurial strategy on organizational performance, the effect of social capital on organizational performance with sustainable supply chain management as a mediating variable. Sustainable supply chain management has an important influence on organizational measurement in various industries. It is used in considering environmental impacts and supplier responsibilities. The population in the current study were 390 logistics managers of companies in the manufacturing industry in Indonesia. The sample in this study was taken by purposive sampling, namely the sampling method based on certain criteria and considerations. In this study, hypothesis testing used the Partial Least Square (PLS) analysis technique with the SmartPLS 3.0 application. The data collection technique used is an online survey. The survey was carried out by distributing online questionnaires designed using the 1 to 7 Likert method to managers in the logistics section of the manufacturing industry. The stages of data analysis were to test convergent validity, discriminant validity test, goodness fit model test to meet the R-square value and test the hypothesis. The results show that entrepreneurial strategy and social capital had an influence on organizational performance and sustainable supply chain management. In addition, sustainable supply chain management mediates between organizational performance and entrepreneurial strategy, while social capital sustainable supply chain management does not mediate the organizational performance variables.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".