MétaCan
Menu
Back to cohort
Record W3123465385 · doi:10.5539/ass.v11n4p223

Factors Affecting Knowledge Transfer in Public Organization Employees

2015· article· en· W3123465385 on OpenAlexvenueno aff
Md Zahidul Islam, Ikramul Hasan, Mohammad Habibur Rahman

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationPublic sectorContext (archaeology)BusinessKnowledge transferKnowledge managementPrivate sectorPublic relationsPsychologyMarketingSocial psychologyPolitical scienceEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

Managing knowledge is considered an essential resource for both public and private sector organizations. Effective transfer of knowledge (KT) among the employees could give a better platform in public entities to serve its clients in a more innovative and efficient way. In the context of Southeast Asia, studies on KT in public offices in Brunei compared to that in Singapore and Malaysia is relatively low. This study has made an attempt to investigation the relationship between cultural elements (trust, communication between employees, rewards and learning & development) and Knowledge Transfer with organizational socialization as a moderating variable. A structured questionnaire survey was conducted to collect responses from a range of public sector employees. In results the findings reveal that there is a significant relationship between learning & development and KT, but the hypotheses related to the other three variables: trust, communication and reward remain insignificant. On the other hand with the moderating effect trust shows significant influence over KT in building relationship with help of socialization.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.580
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.092
GPT teacher head0.335
Teacher spread0.243 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicKnowledge Management and SharingFrench-language works237,207