What matters for knowledge sharing in collectivistic cultures? Empirical evidence from China
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".