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To Manage Innovation, Learn the Architecture

2008· article· en· W72497717 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 · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
Fundersnot available
KeywordsArchitectureProcess (computing)Product (mathematics)Argument (complex analysis)Modular designKey (lock)Product innovationKnowledge managementInnovation managementSelection (genetic algorithm)Process managementNew product developmentComputer scienceBusinessElement (criminal law)MarketingArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

OVERVIEW:Innovation is often perceived as an unmanageable process. At best, sophisticated selection procedures can impose discipline and guidance so as to contain costly errors. The research reported here, conducted with 923 chief technical officers and senior R&D managers, yields a more nuanced view. Innovation becomes manageable when managers move away from prescriptions that view the process as uniform and recognize that different rules and practices apply in different contexts. The main argument presented here is that product architecture has become a key element of innovation strategy. Innovation focuses not only on stand-alone items but increasingly on systemic as well as modular products and services. Product architecture interacts with market dynamics, which leads to distinct “games of innovation,” seven of which have been identified empirically. These games are not predetermined but leave ample room for creative actions.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.046
GPT teacher head0.283
Teacher spread0.237 · 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