The effect of green supply chain management practices on performances of herb manufacturers in Thailand
Bibliographic record
Abstract
This research aimed to 1) investigate the levels of green supply chain management practices (GSCMP), environmental performance (ENP), operational performance (OPP), and organizational performance (ORP) in the context of Thai herb producers; and 2) investigate the interactions between GSCMP, ENP, OPP, and ORP. Quantitative research methodologies were applied in the research. The sample for the quantitative study consisted of 340 Thai herb producers selected by stratified sampling by region. The instruments employed for research were questionnaires. Statistics such as frequency, percentage, mean, standard deviation, confirmatory factor analysis, and structural equation modeling were utilized for quantitative data analysis. Results indicated that GSCMP, ENP, OPP, and ORP levels were high. In addition, GSCMP had direct positive effects on ENP and OPP, as well as indirect positive effects on OPP and ORP, respectively, mediated via ENP and OPP. In addition, ENP had a favorable direct impact on OPP and a positive indirect impact on ORP, with OPP serving as a mediator. Herb producers might use these insights as a roadmap to enhance their organizational performance. In addition, government agencies may utilize the study's findings to establish a strategy for assisting entrepreneurs. In addition, academics and interested parties might bring the research findings to examine and perform more research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".