Impact of Waste Picker and Recycling Applications as Dimension of Perceived Behavioural Control on Recycling Intention
Bibliographic record
Abstract
Separating household waste is a crucial step in the recycling process that relies on personal motivation. Enhancing recycling intentions is particularly important in underdeveloped nations where the role of waste pickers significantly influences the separation efforts. A relevant area for study is the availability of recycling applications which can support and simplify waste separation efforts. The presence of waste pickers and the availability of recycling applications should be considered critical factors when assessing Perceived Behavioural Control (PBC), along with factors such as infrastructure availability and recycling costs. These factors are integrated into the extended Theory of Planned Behavior (TPB) model which includes key components such as attitude toward recycling, subjective and moral norms, PBC, as well as economic incentives. Therefore, this study aimed to show that PBC functioned as a second-order variable influencing the intention to recycle. Data were collected from 122 respondents in the Greater Jakarta area of Indonesia and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results showed that moral norms and attitudes toward recycling were the primary motivators for households to engage in waste management and recycling. However, PBC tended to discourage recycling intentions due to variables such as waste pickers, recycling applications, costs, and infrastructure availability. These results suggested that policymakers should reconsider the role of waste pickers in fostering citizen’s ability to develop recycling habits. Further publications were recommended to explore additional factors such as public perceptions of government enforcement and the effectiveness of industry and appeals to better understand recycling and waste management practices in Indonesia.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".