Doing Design Thinking: Conceptual Review, Synthesis, and Research Agenda
Bibliographic record
Abstract
Design thinking has attracted considerable interest from practitioners and academics alike, as it offers a novel approach to innovation and problem‐solving. However, there appear to be substantial differences between promoters and critics about its essential attributes, applicability, and outcomes. To shed light on current knowledge and conceptualizations of design thinking we undertook a multiphase study. First, a systematic review of the design thinking literature enabled us to identify 10 principal attributes and 8 tools and methods. To validate and refine our findings, we then employed a card sorting exercise with professional designers. Finally, we undertook a cluster analysis to reveal structural patterns within the design thinking literature. Our research makes three principal contributions to design and innovation management theory and practice. First, in rigorously deriving 10 attributes and 8 essential tools and methods that support them from a broad and multidisciplinary assortment of articles, we bring much needed clarity and validity to a construct plagued by polysemy and thus threatened by “construct collapse.” Second, aided by the identification of perspectives of scholars writing about design thinking, we provide detailed recommendations for relevant topics warranting further study in order to advance theoretical understanding of design thinking and test its applications. Third, we identify the enduring, yet essential, questions that remain unresolved across the extant design thinking literature and that may impede its practical implementation. We also provide suggestions for the theoretic frames, which may help address them, and thus advance the ability of scholars and managers alike to benefit from design thinking’s apparent advantages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.162 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".