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Record W3085971886 · doi:10.3390/jrfm13090209

Funding Access and Innovation in Small Businesses

2020· article· en· W3085971886 on OpenAlexvenueno aff
Ronen Harel, Dafna Schwartz, Dan Kaufmann

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProduct innovationMarketingProduct (mathematics)Virtuous circle and vicious circleNew product developmentProcess (computing)Industrial organizationCompetitive advantageSmall businessEconomics

Abstract

fetched live from OpenAlex

The study examined the extent to which lack of access to external funding constitutes a barrier to innovation for small businesses operating in traditional industries. The findings indicate that, these businesses do not view lack of access to funding as a barrier to innovation for any of the four types of innovation: product, process, marketing, or organizational. However, for most of the innovations they promoted, the levels of innovation were relatively low, and which naturally entails relatively low risk to businesses. The findings also indicate that, there is a relationship between product and marketing levels of innovation and lack of access to external funding. The study’s contribution lies in its focus on small businesses operating in traditional industries—businesses which though, essential to economic growth, have garnered less separate attention in the innovation sphere. The study points to a vicious circle in which these businesses do not promote innovation at high levels that would advance their own competitive advantage and require external funding. Because this funding is not within their reach, they continue promoting low-level innovation, and so on and so forth. The study may practically contribute by assisting policymakers as they draw plans dedicated to supporting innovation in small businesses.

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.038
GPT teacher head0.243
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2020
Admission routes1
Has abstractyes

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