Knowledge sharing in Chinese construction project teams and its affecting factors
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore knowledge sharing in a Chinese context and to examine the impact of some key contextual factors that affect knowledge sharing within project teams in the Chinese construction sector. Design/methodology/approach Self-administered questionnaires were used in this study. Data were collected by surveying 222 managerial employees from different project teams in the construction sector in China. Regression analysis was then used to explore the relationship between different factors and the willingness to share knowledge. The potential influence of Chinese traditional cultures on this relationship was also explored. Findings This paper shows that within the Chinese context, explicit knowledge promotes knowledge sharing while tacit knowledge creates barriers to knowledge sharing in project teams. Moreover, trust is positively related to knowledge sharing but justice, leadership style, and empowerment do not influence whether employees will share knowledge among themselves in project teams. Originality/value While it is well known that knowledge management is critical to success, few studies have examined knowledge management in a Chinese context and little is known how the Chinese generate, codify, and transfer knowledge. This paper tries to bridge this gap by examining what affects knowledge sharing in project teams in China so as to help better understand knowledge management in this important emerging market and whether China can sustain its success in economic growth with effective knowledge management.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".