Sustainable Packaging Trends in the Beverage Industry: A Study on Production, Supply Chain, and Consumer Behavior
Bibliographic record
Abstract
This study investigates the factors that influence sustainable packaging trends in the beverage industry, focused on sustainable manufacturing, global supply chain coordination, and consumer behavior in emerging regions.While previous studies examined environmental innovation and consumer preferences separately, this study integrates both by analyzing firm practices, willingness to pay (WTP), and the moderating role of consumer perception.A quantitative approach was used, with 385 responses collected through stratified random selection from four stakeholder groups: customers, students, retail workers, and sustainability specialists.SPSS and AMOS were used to conduct data analysis, which included EFA, CFA, SEM, and moderation.Sustainable manufacturing has a positive influence on global supply chain (β=0.622) and a considerable impact on packaging outcomes, both directly (β=0.362) and through the global supply chain (indirect effect =0.243), which in turn has a direct impact (β=0.391) on sustainable packaging trends in the beverage industry.Accordingly, WTP increases packaging adoption (β=0.251), which is further amplified by positive consumer perception (β=0.557).The study provides new insights into how circular economy activities and perceptual alignment might promote sustainable packaging reform and offers actionable recommendations for beverage companies and governments to align operational practices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".