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Record W2956223653 · doi:10.5267/j.msl.2019.6.027

Knowledge management in business and education: Evidence from Vietnam companies and universities

2019· article· en· W2956223653 on OpenAlexvenueno aff
Vu Ngoc Xuan

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaConfirmatory factor analysisDescriptive statisticsBusinessExploratory factor analysisKnowledge managementCompetition (biology)Structural equation modelingIdentification (biology)Field (mathematics)MarketingComputer scienceStatistics

Abstract

fetched live from OpenAlex

Knowledge Management (KM) is the process of system identification, acquisition and transfer of information and knowledge that people can use to create for perfect competition. KM is considered as an intellectual property of opinions in favour of social orientation and it is created in organizations through social relationships. The definition of KM highlights that it could be considered as closely related to the theory and practice, and is a highly interdisciplinary field of multi-disciplinary. The purpose of this paper is associated with the knowledge management in general and uses survey in Vietnam companies and universities to analysis the role of knowledge management for business development. The authors collected data from 300 companies and universities in Hanoi, Danang, Ho ChiMinh and Can Tho cities. The descriptive statistics, Cronbach Alpha test, Exploratory Factor Analysis, Confirmatory Factor Analysis and Structural Equation Modelling methods were used in this paper. The results indicate that KM had a positive impact on the efficiency of the business organizations. The study also shows that there was a big difference on the effect of KM on organizational effectiveness in terms of SMEs and large enterprises.

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)0.001
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 score0.383
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.022
GPT teacher head0.288
Teacher spread0.265 · 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
Published2019
Admission routes1
Has abstractyes

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