Sustainable Supply Chain Performance Model of Thai’s Pharmaceutical Business
Bibliographic record
Abstract
With the growing environmental awareness, the role of green initiatives and organizational determinants becomes very important for all sectors. It becomes very crucial for organizations to minimize their contribution to environmental degradation process. The foremost objective of the given study is to determine the impact of green motivation and top management support on sustainable supply chain performance with the help of work culture, green innovation and teamwork as mediating variables. In the following study, the survey questionnaire technique is used to collect the data. Almost all pharmaceutical employees respond to the survey questionnaires. Five-point Likert measure scales are used under the questionnaire technique and the final sample size was 438. Additionally, under the analysis, the significant SEM technique is used which has demonstrated that all hypothesis is accepted. The tables and figures indicated that green practices and top management support regarding innovation practices help in enhancing the performance of the companies. Similarly, the results provide that positive work culture, green innovation, and teamwork all have a positive mediating role in enhancing the relationship of top management support, green motivation with supply chain performance. Finally, it is examined that the given study is important and beneficial for the pharmaceutical sector and its related firms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".