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Record W2885050103 · doi:10.1111/jpim.12466

Doing Design Thinking: Conceptual Review, Synthesis, and Research Agenda

2018· article· en· W2885050103 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Product Innovation Management · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsWilfrid Laurier University
FundersUniversity of CambridgeSamsungBritish Academy
KeywordsConstruct (python library)CLARITYDesign thinkingIdentification (biology)Multidisciplinary approachPolysemyManagement scienceComputer sciencePrincipal (computer security)Card sortingEngineering ethicsData scienceKnowledge managementSociologyArtificial intelligenceManagementSocial scienceEngineering

Abstract

fetched live from OpenAlex

Design thinking has attracted considerable interest from practitioners and academics alike, as it offers a novel approach to innovation and problem‐solving. However, there appear to be substantial differences between promoters and critics about its essential attributes, applicability, and outcomes. To shed light on current knowledge and conceptualizations of design thinking we undertook a multiphase study. First, a systematic review of the design thinking literature enabled us to identify 10 principal attributes and 8 tools and methods. To validate and refine our findings, we then employed a card sorting exercise with professional designers. Finally, we undertook a cluster analysis to reveal structural patterns within the design thinking literature. Our research makes three principal contributions to design and innovation management theory and practice. First, in rigorously deriving 10 attributes and 8 essential tools and methods that support them from a broad and multidisciplinary assortment of articles, we bring much needed clarity and validity to a construct plagued by polysemy and thus threatened by “construct collapse.” Second, aided by the identification of perspectives of scholars writing about design thinking, we provide detailed recommendations for relevant topics warranting further study in order to advance theoretical understanding of design thinking and test its applications. Third, we identify the enduring, yet essential, questions that remain unresolved across the extant design thinking literature and that may impede its practical implementation. We also provide suggestions for the theoretic frames, which may help address them, and thus advance the ability of scholars and managers alike to benefit from design thinking’s apparent advantages.

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.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.760
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.133
GPT teacher head0.379
Teacher spread0.246 · 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