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Record W2253316367 · doi:10.15388/omee.2015.6.1.14226

Symbiotic Vs Commensal Networking: the Case of Textile SMEs in China and Russia

2015· article· en· W2253316367 on OpenAlexaff
Andrey Mikhailitchenko, Anna Sadovnikova

Bibliographic record

VenueOrganizations and Markets in Emerging Economies · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTypologyBusinessBusiness networkingSample (material)ChinaCriticismField (mathematics)MarketingIndustrial organizationTextileEmerging marketsKnowledge managementBusiness modelSociologyComputer sciencePolitical scienceElectronic businessGeography

Abstract

fetched live from OpenAlex

The purpose of this research is to contribute to the literature addressing the characteristics of small and medium enterprises (SMEs) based on the sample drawn from two emerging economies – China and Russia. The study investigates the intensity and typology of networking activities that SMEs are involved in. The research contributes to the field by empirically investigating, testing, and putting into a unified framework the measurement tools required for identifying symbiotic and commensal types of SMEs’ networking interactions. It also provides an insight into attitudinal, managerial, cultural, and environmental factors that condition these two types of networking and influence SMEs’ willingness to globalize their operations and thus make their networks international. The overriding framework of the study can be stated as developing, validating and testing the symbiotic networking concept relatively to the international business studies. In this way, the study contributes to overcoming the criticism that network theory is not predictive by nature and is not testable.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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