Electronic waste recycling intention, behavior and environmental benefits: Evidence from Middle East
Bibliographic record
Abstract
Significant quantities of electronic waste (e-waste) generated each year have become a global problem, and individuals, organizations, and governments wrestle to find ways of dealing with e-waste. The frequent introduction of new models of electronic gadgets in the market has resulted in a disproportionately large accumulation of obsolete products, escalating the problem of managing e-waste and calling for effective disposal and recycling methods. This study aims to investigate the antecedents to the e-waste recycling intention (EWRI) of individuals. Integrating the theory of planned behavior (TPB) and behavioral reasoning theory (BRT), study developed a conceptual model and tested in the context of a Middle Eastern country, Bahrain. Data was collected from 603 households and analyzed. Hierarchical regression, and PROCESS macros were used to test the hypothesized relationships. The results indicate: (i) attitude, perceived behavioral control, subjective norms, habits, and convenience are positively allied with EWRI, which, in turn, leads to e-waste recycling behavior (EWRB); (ii) EWRB is a precursor to environmental benefits of recycling, and (iii) environmental concern (first moderator) and environmental awareness (second moderator) strengthens the relationship between EWRI and behavior. The findings contribute to the advancement of the theory of sustainability and provide recommendations for administrators and policymakers regarding e-waste recycling.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".