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Benchmarking Best NPD Practices—II

2004· article· en· W4254938193 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
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBenchmarkingBest practicePortfolioProject portfolio managementBusinessNew product developmentProductivityProcess managementProduct (mathematics)Quality (philosophy)Knowledge managementMarketingOperations managementProject managementComputer scienceManagementEconomicsFinance

Abstract

fetched live from OpenAlex

OVERVIEW:Translation of strategy into new product initiatives is the focus of this second of three articles reporting the results of the most recent American Productivity and Quality Center study on performance and best practices in new product development. Having a new product strategy for your business is clearly linked to positive performance, and the article outlines what the elements or components of a best-in-class innovation strategy are, and their relative impacts. Strategy ultimately must be reflected in spending decisions; best-performing businesses undertake a higher proportion of more innovative NPD projects, while the worst performers have a timid NPD project portfolio. There is also strong evidence that a formal portfolio management approach improves NPD performance overall. Finally, the issue of resources at the NPD team level is probed. Best performers were found to have far more resources available for NPD project teams, especially in non-technical areas, and to provide much sharper focus in the allocation of these resources.

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 categoriesInsufficient 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: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.086
GPT teacher head0.326
Teacher spread0.240 · 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