Design Thinking in Executive Education: When the Parts are Greater than the Whole
Bibliographic record
Abstract
Research on design thinking education at business schools has mostly been concerned with practical issues of implementation, the efficacy of teaching design processes, and participant cognition. Rarely have studies looked at how design thinking techniques and skills gained might translate from the business classroom into the workplace. To close this gap, we turned to situated learning theory and studied how senior managers experience design thinking education and attempt to relate it to their communities of practice at work. We compared custom and open-enrollment executive education courses and found that any uptake depended more on the idiosyncratic workplace situation than on the willingness of senior managers to employ design thinking. As a consequence, few managers were able to employ the whole process, and most managers rather transferred parts and perspectives of what they had learned. Our research has relevance for the larger debate of the efficacy of design thinking for management and offers an explanation for the discrepancy between how design thinking is taught and how it is practiced.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".