What propels the transition? Understanding push-pull-mooring influences on switching from improper E-waste handling to formal recycling
Bibliographic record
Abstract
This study investigates the factors influencing consumers’ intentions to switch from improper handling to e-waste recycling. Utilizing the theoretical framework of Push-Pull-Mooring (PPM) theory, this research adopts a mixed-methods design. Initially, a qualitative study identifies the push, pull, and mooring factors affecting consumers’ switching intentions. Subsequently, the second phase of the research develops and quantitatively tests a framework based on these findings using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that perceived environmental risk and climate change-related health risk perception are push factors in this context. Additionally, government initiatives are identified as pull factors, while perceived convenience is a mooring factor. This research significantly enriches the literature on e-waste recycling and offers practical insights for enhancing e-waste recycling initiatives in developing countries. The study’s comprehensive approach provides a robust basis for understanding and promoting better e-waste management practices in similar contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".