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Benchmarking Best NPD Practices—III

2004· article· en· W4242162812 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

VenueResearch-Technology Management · 2004
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBenchmarkingBest practiceCommercializationNew product developmentProduct (mathematics)Process (computing)BusinessQuality (philosophy)Process managementProductivityMarketingComputer scienceManagementEconomics

Abstract

fetched live from OpenAlex

OVERVIEW:The new product process by which firms drive new product projects from inception through to commercialization, and the methods, practices and tactics embedded within that process, are the focus of this final of three articles reporting the results of the recent American Productivity and Quality Center study on performance and best practices in new product development. Many of the decisive activities that were identified turn out to be poorly executed, while a handful of tasks emerge as pivotal to NPD performance. Most firms now employ a systematic, formal, new product process, but the nature of the process and the way it is implemented are the true keys to success. For instance, delivering a differentiated, superior product is one practice that strongly separates the Best and Worst Performers. Market information, up-front homework, stable product definition, and voice-of-customer research are found to be relatively weak practices in businesses' new product efforts, but all strongly discriminate between the Best and Worst Performers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.052
GPT teacher head0.367
Teacher spread0.315 · 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