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Record W2527436578 · doi:10.1080/09537325.2016.1236190

On designers’ use of biomimicry tools during the new product development process: an empirical investigation

2016· article· en· W2527436578 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

VenueTechnology Analysis and Strategic Management · 2016
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBiomimeticsProcess (computing)New product developmentProduct (mathematics)Process managementEmpirical researchComputer scienceBusinessMarketingArtificial intelligenceEpistemology

Abstract

fetched live from OpenAlex

As technological problems and societal challenges become increasingly complex, designers are urged to recombine knowledge from different sources in order to innovate. In this article we question how nature may be the key source of inspiration and whether it can impact the new product development (NPD) process. We shed new light on whether designers and researchers are: first, familiar with biomimicry tools; second, aware of their characteristics; third, in favour of using biomimicry tools in the NPD process; and fourth, able to assess the impact of biomimicry tools on the NPD performance. By analysing survey data, counterintuitive results emerged concerning both the awareness of the biomimetic tools and their impact on the NPD innovation outcomes.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.058
GPT teacher head0.285
Teacher spread0.227 · 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