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Record W1981420793 · doi:10.1177/1063293x10389799

The Performance of Technical Information Transfer in New Product Development

2010· article· en· W1981420793 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

VenueConcurrent Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsInformation transferComputer scienceNew product developmentProcess (computing)Product (mathematics)Communications systemSet (abstract data type)Information systemTechnical communicationSystems engineeringProcess managementEngineeringTelecommunicationsBusiness

Abstract

fetched live from OpenAlex

The performance of new product development (NPD) is greatly affected by communication strategy and the information technology tools used to support the strategy. A number of users of NPD processes claim that an adequate communication strategy decreases their product cycle time and cost. However, the problem with evaluating the impact of information transfer is that no one has ever specifically measured how performant communication strategies are, or how effective information transfer tools are. For this purpose, a model was developed to evaluate communication strategies, and a set of communication measurement tools was identified to gauge the efficiency of information transfer. This study examines communication within NPD processes at three companies and measures how well information was transmitted, stored, and retrieved. The results indicate that a shorter, more efficient communication process reduces time and effort, and that a shift toward formal methods and passive information tools (computer-based systems) results in more effective and easier coordination as well as better integration.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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