MétaCan
Menu
Back to cohort
Record W2132534212 · doi:10.1109/iemc.2005.1559223

An exploratory study of new product development at small university spin-offs

2005· article· en· W2132534212 on OpenAlexaffabout
Steven Muegge, Milind Kumar Sharma, Uma Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsCarleton University
Fundersnot available
KeywordsSophisticationExtant taxonNew VenturesExploratory researchNew product developmentBusinessProduct (mathematics)Work (physics)Spin offsHigh techMarketingKnowledge managementIndustrial organizationEntrepreneurshipFinanceEngineeringComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

A large and growing body of management research examines new product development (NPD) in technology- intensive firms. Much of that work has been directed towards the organizational environment of medium and large organizations. Likewise, there is a large and growing body of research examining the formation, financing, growth, and success factors of new technology ventures (NTVs). However, there is a relative paucity of research into NPD at small NTVs. This paper describes the findings of an exploratory multiple case study of three NPD projects at small university spin-off (USO) ventures from a Canadian University. Emergent results are compared and contrasted with extant research. We find that NPD was inextricably coupled with new venture financing. NPD milestones had both technical and financial aspects. We observed significant heterogeneity with respect to NPD practices, technological sophistication of the product offering, and sophistication of the NPD environment. We propose that these observed differences are related to founder backgrounds.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0080.003
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.228
Teacher spread0.185 · 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 designQualitative
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

Citations1
Published2005
Admission routes2
Has abstractyes

Explore more

Same topicPrivate Equity and Venture CapitalFrench-language works237,207