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Record W1498655746 · doi:10.5437/08956308x5702145

Managing the Front End of Innovation—Part I: Results From a Three-Year Study

2015· article· en· W1498655746 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 · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFront (military)Knowledge managementFront and back endsSenior managementBusinessOrganizational cultureVariance (accounting)MarketingProcess managementManagementOperations managementComputer scienceEngineeringEconomicsAccountingMechanical engineering

Abstract

fetched live from OpenAlex

OVERVIEW:An IRI Research-on-Research project looked at effective practices in the front end of innovation through a study of practices in 197 large US-based companies over a three-year period. The research team used a holistic framework that evaluated front-end activities through the lens of the New Concept Development (NCD) model. Analysis of the data revealed that organizational attributes—senior management commitment, vision, strategy, resources, and culture—were of most importance to front-end performance, explaining 53 percent of the variance in performance among participating companies. All of the organizational attributes had correlations ranging from 15 percent for senior management commitment to 24 percent for vision, which suggests that all of the organizational attributes are important to a company's front-end performance.

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)0.006
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.847
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.111
GPT teacher head0.331
Teacher spread0.220 · 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