Unpacking knowledge management practices in China: do institution, national and organizational culture matter?
Bibliographic record
Abstract
Purpose This study aims to empirically examine knowledge management practices in China with the purpose to provide a holistic view regarding the current status of knowledge management at both national and organizational levels. Design/methodology/approach Using a survey method, this study collected primary data from organizations across several regions in China. The data were analyzed to detect possible relationships among institutional force, organizational culture and knowledge management process in Chinese organizations. More specifically, to what extent are these relationships moderated by national culture? Findings While knowledge management practices in China were partly influenced by institutional forces, most of the predicted connections between organizational culture and knowledge management were supported. In addition, the dynamic nature of national culture is predominant, that pervasively influencing knowledge management processes and thus contextualization determines how knowledge is being managed in China. Indeed, the ideologies of relationships and trust are key vehicles for knowledge management in the Chinese organizations. Practical implications This study comprehensively reviews existing literature to form an integrative framework, which is under explored in a Chinese context. Such initiative helps scholars and practitioners to gain a full understanding of knowledge management, in general, in the Chinese business environment in particular. Originality/value This paper provides a detailed and empirical insight into the knowledge management practices in Chinese organizations and suggests that knowledge management in a distinctive and yet diverse cultural context should be considered with caution.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".