Barriers to Product Repair: Exploring Motivations and Capabilities Among Operators
Bibliographic record
Abstract
Abstract This study explores barriers to product repair from the perspective of common ‘do-it-yourself’ users and commercial operators. Through a systematic literature review, we identify and compare repair barriers faced by different operators. Our findings highlight that operators predominantly struggle with limited capability (e.g., lack of skills, tools, spare parts) and motivation (e.g., economic motivation and environmental motivation) to repair. In comparison, commercial operators show greater capability than common users but struggle with the economic motivation of repair due to the necessity of financially compensating their labor. In contrast, common users exhibit a lower capability but benefit from additional motivating factors, such as reduced labor costs and product attachment. A subsequent case study illustrates how these barriers manifest within the repair procedure of an exemplary product. Further, we show how the varying capability and motivation among operators constrain the range of viable repair strategies. Navigating this interplay between repair operator and repair viability holds the potential to assess and enable targeted repair strategies for a diverse range of operators, thereby promoting product repairability and circularity.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".