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Record W3084417416 · doi:10.22161/ijaers.79.5

Matrix of Strategic Entrepreneurship Process in Small and Medium Enterprises of the Brazilian and Canadian Aeronautical Industry

2020· article· en· W3084417416 on OpenAlexaboutno aff
Marcela Barbosa de Moraes, Eveline Galvan, Erivaldo Alves Ribeiro, Eudes da Silva Vieira, Zilma Cardoso Barros Soares, Leonardo Santos da Cruz

Bibliographic record

VenueInternational Journal of Advanced Engineering Research and Science · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipBusinessMatrix (chemical analysis)Process (computing)Industrial organizationBusiness administrationEconomic geographyGeographyMaterials scienceComputer scienceComposite materialFinance

Abstract

fetched live from OpenAlex

The central proposal of this paper is to study the process of strategic entrepreneurship in small and medium enterprises in the Brazilian and Canadian aeronautical industry.The research was based on the qualitative approach with multiple case studies, following the recommendations of Eisenhardt (1989).The study addressed the reality of four small and medium technology-based companies in the aeronautical industry, being two Brazilian and two Canadian.Data were collected with in-depth semi-structured interviews, lasting approximately 2h and 40min, with owner-managers and analyzed with Atlas-ti software.The analysis took place in depth in each case and then in a comparative way between the cases in search of similarities and differences that led to the formation of valid results for the whole sample studied.Finally and based on the analysis of the results, it was concluded that the companies analyzed, through the process of strategic entrepreneurship, obtained a competitive advantage, since they incorporated to the entrepreneurial activity, strategic partnerships and the development of innovation as activities to be developed in a continuous, identifying the innovative elements of production chains in which companies viewed greater potential for profit and ultimately worked together with customers to develop and improve processes and products.This allowed the Brazilian and Canadian SMEs to identify and explore new opportunities in the face of the greater circulation of tacit and explicit knowledge in the productive chain and the execution of R&D together.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.047
GPT teacher head0.318
Teacher spread0.271 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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