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<scp>Competing on Standards? Entrepreneurship, Intellectual Property, and Platform Technologies</scp>

2009· article· en· W1988297025 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

VenueJournal of Economics & Management Strategy · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntellectual propertyEconomic rentIncentiveBusinessLawsuitEntrepreneurshipIndustrial organizationMarketingFinanceMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

Entrepreneurs often rely on intellectual property (IP) to earn a return on their innovations, and also compatibility standards, which allow them to supply specialized components for a shared technology platform. This paper compares the IP strategies of small entrepreneurs and large incumbents that disclose patents at 13 voluntary standard setting organizations (SSOs). These patents have a relatively high litigation rate. For small private firms, the probability of filing a lawsuit increases after disclosure to the SSO. For large public firms, the filing rate is unchanged. Although forward citations increase after disclosure for all firms, the size of this effect is the same for entrepreneurs and incumbents. These results suggest that standards increase the difference between large and small firms’ incentives to litigate, rather than the relative value of their patents. We conclude that because specialized technology providers cannot seek rents in complementary markets, they defend IP more aggressively once it has been incorporated into an open platform.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.076
GPT teacher head0.228
Teacher spread0.152 · 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