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Record W2059919859 · doi:10.1057/palgrave.emr.1500042

New ventures' inward licensing: examining the effects of industry and strategy characteristics

2005· article· en· W2059919859 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

VenueEuropean Management Review · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsYork University
Fundersnot available
KeywordsObsolescenceBusinessNew VenturesIndustrial organizationLicenseFlexibility (engineering)Competitive advantageComplementary assetsMarketingEntrepreneurshipEconomicsFinanceManagement

Abstract

fetched live from OpenAlex

Abstract New ventures compete by creating innovative products. Liabilities of newness and inexperience, limited resources, rapid technological obsolescence and constantly changing market conditions often encourage new ventures to license other companies' technologies to complement and augment their internally developed innovations. Building on the knowledge‐based view of the firm, we propose that the intensity of new ventures' use of inward licensing reflects the demands of their industries and competitive strategies. The results of an empirical study of 361 US new ventures show that industry characteristics and competitive strategy influence their inward licensing as a means of lowering costs and maintaining strategic flexibility while building their capabilities.

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: Other · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.686

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 science0.0000.001
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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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