The role of internal motivations in consumer upcycling intention and purchase intention of upcycled products
Bibliographic record
Abstract
Purpose This research aims to investigate the relationship between internal motivations and consumer upcycling intention, and how these motivations relate to purchase intention of upcycled products. Design/methodology/approach This research is based on an online survey with a sample of 470 US consumers. Structural equation modeling with Mplus was applied to test the proposed relationships. Findings Perceived competence is the strongest internal motivation related to consumer upcycling intention, followed by waste prevention and frugality. Consumers who have motivations of waste prevention, social connectedness and emotional attachment for consumer upcycling have higher intention to purchase upcycled products. Research limitations/implications The generalizability of the findings might be limited due to the US-based survey sample. Future research could validate and extend these findings in different cultural contexts. Practical implications The findings enable policymakers and business practitioners in the circular economy to develop effective strategies to promote consumer upcycling as well as the purchase of upcycled products. Originality/value First, this research addresses the dearth of literature studying upcycling and the broader circular economy from the demand side (i.e. the consumer). Second, by identifying perceived competence as the strongest internal motivation for consumer upcycling, this research offers a new perspective on how to promote consumer upcycling. Third, by demonstrating that certain internal motivations for consumer upcycling can explain purchase intention of upcycled products, this research validates for the first time the connection between consumer upcycling and upcycling businesses empirically.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".