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Record W2121311572 · doi:10.1109/nafips.2011.5752033

Modeling innovation in international business with respect to the cultural distance using interval type-2 fuzzy sets

2011· article· en· W2121311572 on OpenAlexaff
Masoomeh Moharrer, Hooman Tahayori, Alireza Sadeghian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHofstede's cultural dimensions theoryInternational businessDimension (graph theory)Fuzzy setQuality (philosophy)Fuzzy logicInterval (graph theory)Set (abstract data type)Knowledge managementComputer scienceCultural diversityMathematicsMarketingBusinessSociologyEconomicsArtificial intelligenceSocial scienceManagementEpistemologyPure mathematics

Abstract

fetched live from OpenAlex

Cultural distance is one of the important variables in international business which differentiates the domestic and local business from international business. However, calculating the cultural distance is a critical issue for determining its effects on various aspects of international business and in particular on innovation. Moreover, it is difficult to precisely measure the quality of innovation in international business. In this paper, we will demonstrate a novel method for calculating interval type-2 fuzzy set of the quality of innovation in international business collaborations for each country. The sets are defined with respect to each individual Hofstede cultural dimension and also with respect to the cultural distances of the countries that are calculated using the known methods in management studies which are based on aggregating Hofstede cultural dimensions.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.274
Teacher spread0.207 · 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 designSimulation or modeling
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
Published2011
Admission routes1
Has abstractyes

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