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Record W4309271038 · doi:10.1504/ijmpt.2023.10052217

Determinants of innovation co-operation for manufacturing SMEs: evidence from a systematic review of the literature (1992-2015)

2022· review· en· W4309271038 on OpenAlexaff
Nabil Amara, Amélie Cloutier

Bibliographic record

VenueInternational Journal of Materials and Product Technology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsSystematic reviewBusinessManufacturing engineeringMaterials scienceEngineeringMEDLINEPolitical science

Abstract

fetched live from OpenAlex

This paper summarises a comprehensive and systematic review of 29 quantitative studies from peer-reviewed journals published in ranked publications between 1992 to 2015 on the determinants of innovation cooperation for manufacturing SMEs. Applying a documented methodology, it crystallises acquired knowledge by identifying, synthesising and discussing 220 unique determinants stemming from a vast and heterogeneous body of literature. The article introduces an analytical framework integrating different perspectives to approach this concept presenting a holistic and integrated view of the topic. It provides a typology that sorts the determinants into six categories: 1) environmental characteristics; 2) industrial characteristics; 3) organisational characteristics; 4) individual characteristics; 5) partnership characteristics; 6) project characteristics. This systematic review also identifies current gaps in the literature. The provided research perspectives will allow researchers and policymakers to better foster innovation and guide researchers addressing this phenomenon in the future. It clearly lays a foundation for future research on the topic, organising and building upon the literature that has been published so far.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.223
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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