Framing the microfoundations of design thinking as a dynamic capability for innovation: Reconciling theory and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Design thinking (DT) is gaining ground among academics and practitioners as a means to improve the innovativeness of organizations. However, with few exceptions, DT studies are most entrenched in practice rather than theory‐driven research. This weak tie between theory and managerial practice calls for delving into the dynamics of DT for innovation to build stronger foundations for future studies. Therefore, this study provides a theory‐based framing of DT for innovation and a critical review of the DT literature to reconcile theory and practice. To this end, we propose framing and advancing DT as a dynamic capability for innovation rooted in lower‐level aspects, namely microfoundations. Based on our theoretical framework, we conduct a systematic literature review that unveils the dynamics of DT and the context‐specific capabilities to innovate. The contributions of the paper are twofold. First, we provide a theory‐based framing of DT and combining it with existing theories in innovation and management (i.e., dynamic capabilities and microfoundations). Second, we review the extant literature on DT for innovation to reconcile previous studies with these theoretical lenses to, hence, guide future research. Based on this interpretation, we then define a number of avenues for future research, thus reconciling practical evidence with theories that can further explain how DT relates to firm innovativeness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it