Exploring factors of e-waste recycling intention: The case of generation Y
Bibliographic record
Abstract
The seriousness of the e-waste crisis stems from the fact that consumers do not participate much in ensuring the proper disposal of electronic materials. In this context, millennials are the largest segment of consumers of electronic products who are not yet motivated to get sustainably rid of them. However, to inspire consumers to recycle e-waste, it is necessary to investigate consumers' behavioral intentions towards e-waste thoroughly. This study integrates the theory of planned behavior, social influence theory, and personality traits to examine how consumers gauge their choice to recycle e-waste. Data were collected from randomly surveying 300 Lithuanians through a structured questionnaire. Using the PLS-SEM approach, results show that attitude, subjective norms, and perceived behavioral control significantly influence consumers' e-waste recycling intention. Regarding personality traits, only openness to experience significantly affects consumers' e-waste recycling intention. In contrast, other traits such as agreeableness, conscientiousness, extraversion, and neuroticism have a non-significant influence on consumers' e-waste recycling intention. In addition, normative and informational social influence affects consumers' e-waste recycling intention. The current study advances our understanding of e-waste recycling behavior by examining how TPB, personality factors, and social influence theory influence intentions. It provides valuable insights for policymakers and marketers on understanding and encouraging the e-waste behavior of Lithuanian Y-generation consumers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".