Enhancing Recycling Participation: Behavior Factors Influencing Residents’ Adoption of Recycling Vending Machines
Bibliographic record
Abstract
Recycling is a crucial waste management option because of the increasing amount of waste generated and the limited space in landfills. However, traditional recycling processes, which require individuals to deliver large quantities of waste to recycling centers, can discourage participation. To address this issue, this study expanded upon the technology acceptance model (TAM) by incorporating perceived risk and social influence to examine residents' intentions to adopt recycling vending machines. This study used partial least squares structural equation modeling based on the data collected from 525 individuals in Jiangsu Province, China. This study's findings indicate that TAM components, such as attitudes, perceived usefulness, and perceived ease of use, positively influence residents' intentions and behaviors to adopt recycling vending machines. Additionally, perceived usefulness and ease of use significantly affected attitudes toward recycling vending machines. This study also found that social influence had a significant positive impact on perceived usefulness and ease of use, while perceived risk negatively influenced these factors. Furthermore, attitude played a crucial mediating role, with additional factors impacting intentions and behaviors through attitude. Overall, this research can help stakeholders such as waste management companies to understand residents' concerns and improve the implementation of recycling vending machines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".