Effect of Green Packaging, Green Supply Chain, and Green Advertising on Competitive Advantage to Business Performance of Thailand's Pharmaceutical Firms
Bibliographic record
Abstract
Green marketing and its determinants develop a system that protects the environment from harmful production, consumption of brands and goods, and also protects society and its members. However, this research paper aimed to evaluate the impact of green determinants which include green packaging, supply chain, and advertising on the sustainable performance of the pharmaceutical sector of Thailand. The mediating role of competitive advantage has also been evaluated in this study to improve the sustainability performance of the sector. A study model that was conceptual in nature produced to evaluates the relationships from that model. The target population of this research was 540 managers of different pharmaceutical firms in Thailand, out of which 259 were female and 281were male. Several techniques and tests were also being used to check as well as to calculate results such as confirmatory factor analysis, KMO, and SEM. The testing of relationships gives results that indicate that the impact of green packaging has been significant in the sustainable performance of the sector. Furthermore, the findings also indicate that competitive advantage positively mediates the relationship between green practices and the sustainable performance of the sector. The significant findings of this research paper will positively contribute to the existing literature.
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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 | 0.007 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".