Design Thinking for Innovation in Sustainable Built Environments and the Integration of an Inclusive Foresight and Design Thinking Framework
Bibliographic record
Abstract
The search for new methods and tools to successfully address sustainability challenges is gaining momentum, due to the growing awareness of sustainability issues.Over the past two decades, design thinking (DT) has become a phenomenon in a wide range of contexts, and has recently drawn research attention as an innovative approach for handling complex socioecological problems.This review paper analyzes DT processes covered in sustainable built environment (SBE) articles that focus specifically on DT and innovation, with a view to suggesting/developing an affective new model for sustainability research.The research design was developed following Denyer and Tranfield's method.The author reviewed documents using the evidence from all open access English language articles related to this issue between 2000 and 2022 identified using a Scopus database search in order to clearly identify and analyze the challenges and opportunities for innovation growth in SBE using a DT and innovation framework, 50 articles were selected based on the PRISMA statement and plotted on a modified Ansoff Matrix.This systematic literature review indicates that research regarding DT for innovation in SBE is challenged by the matter of how to identify new contexts and new solutions for future-oriented sustainability.It is also proposed that a wider range of stakeholders are required to help optimize the solutions being generated.The results reveal research gaps in integrating foresight and DT into sustainability research.A model of inclusive foresight design thinking (FDT) is proposed to guide future research to support the practical application and enhance the viability of DT in sustainability.
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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 | 0.039 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".