Sustainable product disposal: Consumer redistributing behaviors versus hoarding and throwing away
Bibliographic record
Abstract
Abstract Business strategies involving sustainable product disposal have focused mostly on technical aspects but neglected to adequately incorporate the nature of consumers' behavior. The current study addresses this void. We study consumer product disposal behavior and subsequently offer insights to businesses on how to incorporate consumer input into their strategic decision making in the light of opportunities to mitigate environmental impacts. Consumers' redistributing of unwanted but still useful products to others by reselling, passing along, or donating, rather than hoarding or throwing away, contributes to product lifetime extension and waste management. We study factors influencing product redistribution and explore profile of consumers who engage in various disposal behaviors. Findings from two online surveys, on mobile phones and sunglasses, reveal that specific waste attitudes, that is, waste minimization and waste aversion, rather than general environmental concern, are key determinants of product redistribution choice. Product cost is positively related to reselling and giving behaviors. Furthermore, product quality and product self‐image congruency significantly reduce the odds of throwing away. The method of product redistribution is also influenced by consumers' demographic characteristics including age, education level, and income. This paper advances extant literature on product disposal from the perspective of the consumer and provides input into development of business strategies that incorporate consumers' sustainable disposal behaviors. We also offer input to policy makers on how to curb or delay waste and pollution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".