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Design Thinking in Executive Education: When the Parts are Greater than the Whole

2021· article· en· W3183696704 on OpenAlexaff
Stefan Meisiek, Angèle M. Beausoleil, Daved Barry, Anjana Dattani

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDesign thinkingSituatedRelevance (law)Situated cognitionPsychologyWork (physics)Process (computing)CognitionExecutive educationPedagogyKnowledge managementMathematics educationBusiness educationHigher educationEngineeringComputer sciencePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.264
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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