Predicting supply chain management of e-waste recycling behavior using an extended theory of planned behavior model
Bibliographic record
Abstract
This study aims to examine the supply chain management perspective of subjective norms, attitude, perceived behavioral control, and intention on e-waste recycling behavior, and the mediating role of intention in the relationship between the factors of extended theory of planned behavior and e-waste recycling behavior. The research sample was students at some universities in Jakarta, Indonesia with data collected through an online questionnaire. The results of the analysis show that subjective norms, attitudes, perceived behavioral control, and intentions have significant effects on e-waste recycling behavior. In addition, intention can mediate the relationship between attitude and perceived behavioral control on e-waste recycling behavior. The implication highlighted the importance of the role of subjective norms, attitude, perceived behavioral control, and intention in influencing one's e-waste recycling behavior. Therefore, public awareness can pay attention to the factors of extended theory of planned behavior in helping to increase the awareness of e-waste recycling. From a theoretical point of view, the findings show that the Theory of Planned Behavior is a useful theoretical framework for understanding the behavior of e-waste recycling. Practically speaking, significant findings about the role of subjective norms, attitudes, perceived behavioral controls, and intentions in influencing e-waste recycling behavior can inform policies and programs aimed at promoting more responsible e-waste management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".