MétaCan
Menu
Back to cohort
Record W2947576355 · doi:10.5430/ijba.v10n4p1

Building Competitive Advantage for Vietnamese Firms: The Roles of Knowledge Sharing and Innovation

2019· article· en· W2947576355 on OpenAlexvenueno aff
Thân Thanh Sơn, Cung Huu Nguyen, Thang Quang Tran, Phong Ba Le

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageVietnameseBusinessKnowledge managementIndustrial organizationKnowledge sharingStructural equation modelingMarketingComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the influence of knowledge sharing (KS) and two specific types of innovation on competitive advantage in Vietnamese firms. Based on using structural equation modeling (SEM) and survey data collected from 225 participants, the findings reveal that KS directly and indirectly affects firm’s competitive advantage through the mediating role of innovation speed and innovation quality. The findings stress the important role of building a positive climate to stimulate employees for sharing knowledge aimed at improving firm’s innovation capability, and sustaining competitive advantage. Future research needs to explore the relationship between three components of knowledge management namely knowledge acquisition, KS, and knowledge application, innovation, and specific aspects of competitive advantage (such as low cost advantage, differentiation advantage, and time advantage) to provide deeper the mechanism of how specifics aspects of knowledge management connected with firm’s certain types of competitive advantage through innovation.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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 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

Citations33
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Business AdministrationSame topicKnowledge Management and SharingFrench-language works237,207