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Record W4220910146 · doi:10.1093/icc/dtac019

Industrial R&D and national innovation policy: an institutional reappraisal of the US national innovation system

2022· article· en· W4220910146 on OpenAlexaff
Ibrahim Shaikh, Krithika Randhawa

Bibliographic record

VenueIndustrial and Corporate Change · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCorporate governanceShareholder valuePoliticsEconomicsStakeholderMarket economyShareholderBusinessFinancePolitical scienceManagement

Abstract

fetched live from OpenAlex

Abstract Studies highlight how the once envied US national innovation system (NIS) is now showing signs of slowing down. In this article, we unpack this issue from an industrial R&D perspective. First, we highlight that open innovation (OI) practices (i.e., external sources and markets for technologies) have increased the rate of inventive activity in the current wave of industrial R&D, but financialization skews the firms’ focus on short-term profits and shareholder value maximization. When OI intersects with an institutional context that propagates such shareholder-centric governance of R&D, three social costs are incurred by the US NIS: (i) irrational relationship between risks and rewards, (ii) weak antitrust and intellectual property (IP) rights that result in a lack of business dynamism, and (iii) austerity and weak demand-side policies. We contend that these social costs tilt the R&D trajectory toward incremental R&D at the expense of the blue-sky science needed to retain US leadership in technological innovation. Second, we document three social benefits that public-sector R&D agencies generate for the US NIS: (i) undertaking a technology brokerage role, (ii) creating radical R&D markets, and (iii) embracing stakeholder governance. We emphasize how a hidden “entrepreneurial network state” subtly creates and shapes breakthrough R&D and markets for private sector firms but cannot recoup the rewards for society due to political rhetoric that favors incumbent market power. Third, we recommend both incremental and radical policies to drive institutional reforms that promote a stakeholder-centric form of R&D governance so that the future wave of industrial R&D creates value for society. Overall, we draw attention to the role politics plays in industrial R&D and the US NIS and how small adjustments in institutional dimensions and governance modes can impact the US R&D trajectory and competitiveness.

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)0.011
metaresearch head score (Gemma)0.013
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 score0.019
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.013
Scholarly communication0.0100.006
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.458
GPT teacher head0.300
Teacher spread0.157 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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