Framing the microfoundations of design thinking as a dynamic capability for innovation: Reconciling theory and practice
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.
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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".