Industrial R&D and national innovation policy: an institutional reappraisal of the US national innovation system
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".