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Record W3040479210 · doi:10.1002/smj.3217

Organizational change and the dynamics of innovation: Formal R&D structure and intrafirm inventor networks

2020· article· en· W3040479210 on OpenAlexafffund
Nicholas Argyres, Luis A. Rios, Brian S. Silverman

Bibliographic record

VenueStrategic Management Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDecentralizationOrganizational structureCorporationBusinessIndustrial organizationSocial connectednessEconomicsEconomic geographyMicroeconomicsManagementMarket economyPsychologySocial psychologyFinance

Abstract

fetched live from OpenAlex

Abstract Research Abstract Prior research has argued and shown that firms with more centralized R&D produce broader innovations, but the organizational mechanisms underlying this relationship are underexplored. This gap limits our understanding of whether and how formal R&D structure can be used as a lever to influence research outcomes. To address this question, we study the relationship between formal R&D structure, internal inventor networks, and innovative behavior and outcomes. We find that centralization of R&D budget authority increases the connectedness of internal inventor networks, which in turn increases the breadth of both innovation impact and technological search. Surprisingly, decentralization does not have the opposite effect. Our results suggest that changes in formal structure influence innovation outcomes through changes in inventor networks, with a lag reflecting organizational inertia. Managerial Abstract Diversified corporations can organize their R&D functions to be more or less centralized. Prior research has shown that this organizational choice is associated with different types of innovative outcomes. But what happens when a corporation changes its level of R&D centralization? This paper suggests that centralization of R&D gradually leads to new patterns of collaboration among inventors, which in turn will be associated with innovations that draw on and influence a wider range of technologies. However, future work is needed to understand why decentralization does not appear to have the opposite effect.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

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

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 head0.042
GPT teacher head0.224
Teacher spread0.182 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations104
Published2020
Admission routes2
Has abstractyes

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