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Record W2517601801 · doi:10.5539/ass.v12n9p15

The Role of Cultural Factors on Intra-Firm Technology Transfer Performance and Corporate Sustainability: A Conceptual Study

2016· article· en· W2517601801 on OpenAlexvenueno aff
Syed Ali Fazal, Sazali Abdul Wahab, Nowshin Zarin, Abu Sofian Yaacob, Nur Fadiah Mohd Zawawi

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
FundersWorld Bank Group
KeywordsMultinational corporationBusinessSubsidiaryIndustrial organizationProfitability indexSustainabilityTechnology transferCompetitive advantageMarketingCompetition (biology)International tradeEcology

Abstract

fetched live from OpenAlex

<p>Technological innovations have emerged as crucially significant factor for sustaining market competition and achieving sustainable competitive advantage in the 21st century. The Multinational Corporations (MNCs) as celebrities of innovation play significant role in diffusing technological knowledge throughout firms both nationally and internationally. Although numerous studies exist on technology transfer the majority of existing literature addresses the issues related to inter-firm transfer of technology only while the area related to intra-firm transfer of technology has been largely underexposed; study of which is believed to be ideal for fruitful exploration of profitability in technology transfer projects. By exploring the existing relevant literature, the current study would attempt to posit a new model in regards to the effect of host-country cultural environment on the performance of technology transferred by the MNCs to their subsidiaries in Malaysia and its subsequent impact on the corporate sustainability of the firm. In the present study the relative influence of two cultural environment factors, namely national cultural distance and organizational cultural distance have been addressed and the study is expected to contribute both theoretically in the body of knowledge and also in terms of practical implication for policy makers of the host-country and the involved MNCs and hence enriching the existing intra-firm technology transfer literature simultaneously.</p>

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 score0.494
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.014
GPT teacher head0.232
Teacher spread0.218 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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