Doing Design Thinking: Conceptual Review, Synthesis and Research Agenda
Bibliographic record
Abstract
Design thinking is attracting 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 what design thinking is and what it can do. While some authors regard it as a new and effective way to foster creativity and innovation, others consider it a management fad built on a misunderstood notion of design practices. This paper draws upon the concept of “umbrella constructs” (Hirsch and Levin, 1999) - those whose inherent indeterminacy can undermine their development and, ultimately, lead to what Hirsch and Levin term “construct collapse.” Accordingly, we delve into current conceptualizations of design thinking in order to identify emerging issues, consider divergent interpretations, and integrate conflicting views. We begin by presenting a systematic review of the literature. This exercise enables us to categorize the constituent components of design thinking and develop a process model that brings together process- and individual-level attributes as well as main tools. Next, we problematize studies on design thinking by evaluating assumptions that are made by its advocates. We conclude by proposing an agenda for future studies, which we believe will promote sufficient consistency in defining design thinking and will foster further exploration and development.
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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 | 0.068 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".