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To Manage Innovation, Learn the Architecture

2008· article· en· W72497717 on OpenAlexaff
Roger Miller, Xavier Olleros

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0070.009
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.007

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2008
Admission routes1
Has abstractyes

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