MétaCan
Menu
Back to cohort
Record W2150457675 · doi:10.1108/jkm-06-2014-0252

What matters for knowledge sharing in collectivistic cultures? Empirical evidence from China

2014· article· en· W2150457675 on OpenAlexaff
Zhenzhong Ma, Yufang Huang, Jie Wu, Weiwei Dong, Liyun Qi

Bibliographic record

VenueJournal of Knowledge Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsKnowledge managementCollectivismKnowledge sharingContext (archaeology)Knowledge value chainBusinessEmpirical researchIncentiveEmpirical evidencePersonal knowledge managementPsychologyMarketingOrganizational learningComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this study is to identify key factors that facilitate knowledge sharing in collectivistic cultures and further help better understand knowledge management in the international context. Design/methodology/approach – Using a survey method, this study collected data from over 200 managerial employees in knowledge management-based project teams from China. Regression analysis was then conducted to analyze the impact of individual differences and environmental factors on the willingness to share knowledge among team members to identify key factors for successful knowledge retention in the constantly changing organizational environment in a collectivistic context. Findings – The results show that incentives are very important in individual’s decision to share knowledge in project teams even in a collectivistic culture like China and both intrinsically and extrinsically motivated individuals tend to share more knowledge with their team members. Individuals with high altruism are also found more likely to share knowledge with others. Moreover, a trusting environment and explicit knowledge will facilitate knowledge sharing for better retention. Research limitations/implications – More studies should be conducted in other collectivistic cultures to explore cultural barriers in knowledge management in the international context and comparative studies using samples from different cultural backgrounds are also encouraged to help extend the theories on knowledge management. Originality/value – While it is well-known that knowledge sharing is essential for organization to maintain competitive advantage, relatively few studies have examined knowledge sharing in collectivistic cultures, and even fewer have done so in China. This study adds values to the literature by identifying key factors for knowledge sharing in China, and thus helps refine the knowledge management theories and provides insights for multinationals on knowledge management in the Chinese market.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0000.001
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 head0.080
GPT teacher head0.381
Teacher spread0.301 · 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

Citations100
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Knowledge ManagementSame topicKnowledge Management and SharingFrench-language works237,207