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Record W4292120423 · doi:10.1080/14778238.2022.2105758

The impacts of knowledge management enablers and knowledge management processes on university performance in Vietnam

2022· article· en· W4292120423 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKnowledge Management Research & Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsCegep de Thetford
Fundersnot available
KeywordsVietnameseKnowledge managementBusinessSustainabilityKnowledge creationVariance (accounting)MarketingComputer science

Abstract

fetched live from OpenAlex

Despite the abundance of literature on university performance (UP), few studies have examined the impacts of knowledge management (KM) enablers and KM processes on UP. This study introduces a model explaining the variance in UP, including KM enablers, which include organisational enablers (OEs) and personal enablers (PEs) as indirect determinants, and KM processes as direct determinants. Data from 296 Vietnamese university lecturers and managerial staff indicated that KM processes mediate the link between KM and UP. However, OEs and PEs had unequal impacts on KM processes. Of the three OE components, organisational rewards and culture had a direct impact on KM, while organisational leadership had an indirect impact through the two other processes. Of the two PE components, knowledge self-efficacy had a significant influence on KM. The findings indicate ways for Vietnamese universities to enhance performance and thus develop sustainably.

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.

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)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0020.006
Research integrity0.0000.001
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 head0.058
GPT teacher head0.373
Teacher spread0.315 · 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