Antecedents of green supply chain collaborative innovation in tourism SMEs: Moderating the effects of socio-demographic factors
Bibliographic record
Abstract
The importance of environmentally friendly issues related to manufacturing industry activities in Makassar City is currently a special concern. Green supply chain management integrates supply chain management with environmental management, so it is important to reduce environmental impacts. This study aims to determine the antecedents of green supply chain collaborative innovation on the performance of Small and Medium Enterprises in the Tourism Sector. Data was collected by distributing questionnaires. The unit of research analysis is Small and Medium Enterprises in the Tourism Sector in West Java. Respondents who were used as samples were 311 respondents. The analytical method used in testing the hypothesis is Partial Least Square (PLS). The results show: green supply chain management has a positive and significant effect on the performance of Small and Medium Enterprises in the Tourism Sector in West Java, green supply chain management mediates the effect of green innovation and transformational leadership on the performance of Small and Medium Enterprises in the Tourism Sector.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".