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Record W2907119105 · doi:10.1186/s41469-018-0040-7

Implementing design thinking in organizations: an exploratory study

2018· article· en· W2907119105 on OpenAlexaff
David Dunne

Bibliographic record

VenueJournal of Organization Design · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDesign thinkingExploratory researchKnowledge managementLegitimacyIndustrial and organizational psychologyProcess (computing)SensemakingCompromiseWork (physics)Organizational architectureProcess managementResistance (ecology)Qualitative researchBusinessPublic relationsEngineeringPsychologyComputer scienceSociologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract Design thinking has been adopted by organizations in all sectors of the economy. In this qualitative study, I explore organizations’ goals in adopting design thinking, the challenges such programs encounter, and the approaches they have taken to deal with these challenges. I find that unclear goals, the need to build legitimacy, cultural resistance, and leadership turnover can compromise the work of design programs. Possible antidotes include technological and collaborative platforms, and extending design thinking into the implementation process.

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 imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0130.014
Scholarly communication0.0100.006
Open science0.0020.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.299
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations93
Published2018
Admission routes1
Has abstractyes

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