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Determinants of integrated product development diffusion

2006· article· en· W2146167818 on OpenAlexaff
Todd Boyle, Vinod Kumar, Uma Kumar

Bibliographic record

VenueR and D Management · 2006
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsCarleton UniversitySt. Francis Xavier University
Fundersnot available
KeywordsNew product developmentProcess managementProduct (mathematics)BusinessOrder (exchange)Knowledge managementDiffusionKey (lock)Concurrent engineeringProduct lifecycleOperations managementMarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

Integrated product development (IPD) is an approach for developing new products focused on the early and active involvement of design, manufacturing, marketing and other key new product development (NPD) stakeholders in order to achieve cross‐functional integration and concurrent execution of various NPD activities. The benefits of IPD are well known in both the academic literature and popular press, including significant reductions in NPD cycle time and costs. However, in spite of these benefits, for the majority of manufacturing organizations, IPD is not used on 100% of NPD projects. This research develops a model of the organizational contextual factors influencing the diffusion of IPD in organizations. Results of surveying 269 NPD managers indicate that the complexity of certain IPD practices and support for IPD directly influence IPD diffusion, while an innovative organizational climate and the complexity of the organization's NPD activities indirectly influence IPD diffusion through IPD support.

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)0.003
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.004
GPT teacher head0.197
Teacher spread0.192 · 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 designObservational
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

Citations31
Published2006
Admission routes1
Has abstractyes

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