Participatory Design for Sustainable Product Innovation of Banana Fiber in Thailand
Bibliographic record
Abstract
This research examines the effectiveness of participatory design approaches in facilitating sustainable product innovation through a qualitative case study on the development of banana fiber crafts in Thailand.The study employed a mixed-methods research design, combining participant observation, semi-structured interviews, and documentary analysis to investigate the integration of diverse knowledge domains through collaborative processes.The study followed a three-phase approach: initial knowledge gathering through expert consultations and field surveys, collaborative design development through structured workshops, and iterative prototype refinement with stakeholder feedback.The study was conducted at Ban Hua Khwai community in Songkhla Province, Thailand, where traditional craft knowledge was integrated with contemporary design approaches through structured participatory processes.Data collection spanned six months, involving 15 design students, 3 researchers, 2 entrepreneurs, and 12 community producers, who were purposively sampled to represent key stakeholder perspectives.The analysis employed thematic coding using NVivo software, focusing on identifying patterns of knowledge integration and creative collaboration.Results demonstrated the successful development of innovative product categories in two main segments: decorative household items and lifestyle products.The study introduced and validated the Creative Knowledge Integration (CKI) framework, providing a structured approach for synthesizing diverse knowledge domains in participatory design processes.Analysis using Elkington's Triple Bottom Line framework revealed significant positive impacts across economic (through successful product commercialization), social (through meaningful engagement of elderly community producers), and environmental dimensions (through sustainable material use and production methods).This research contributes to both the theoretical understanding of collective creativity in design and the practical application of participatory methods in sustainable product innovation.The findings provide valuable insights for designers, researchers, and communities seeking to develop sustainable products through collaborative approaches.
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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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".