Where Next?: Exploring Opportunity Areas and Tool Functions for Sustainable Product Design
Bibliographic record
Abstract
Abstract Shifts in policy and consumers’ awareness have raised the importance of sustainability in product design, inspiring the development of tools that support more sustainable design. However, such tools are not adopted as quickly as expected. To understand what tools designers consider useful, we explored how much control designers perceive over existing design strategies, and how much impact they think these strategies have. We used a survey (n = 42) and follow-up interviews (n = 12) to ask hardware product design professionals what areas they see opportunities in, and what functions they look for in tools. The findings reveal that designers perceive impact and control differently in different opportunity areas, so to increase the likelihood of adoption, tools should incorporate features that reflect those differences. Designers report the least control over aspects related to manufacturing, and also rate these as having low impact on sustainability. In contrast, designers attribute high control and impact to aspects related to their design practice and their organizations’ business model, which are tightly linked. To address these issues, designers pointed towards tools that improve information transparency, support decision-making, predict results, share knowledge, and discover user needs. Regardless of how much control designers have, they care about tools and strategies that are highly impactful.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".