The Mediating Effect of Decision Quality on Knowledge Management and Firm Performance for Chinese Entrepreneurs: An Empirical Study
Bibliographic record
Abstract
While it is well-known knowledge management is crucial for an organization’s competitive advantage, relatively little research has explored the process whereby knowledge management affects firm performance in a collectivistic culture such as China. This study is to explore the mechanism through which knowledge management helps improve firm performance and then to examine the mediating role of decision quality in the Chinese context. Using a self-administered questionnaire to collect data from Chinese entrepreneurs and with structural equation modeling, this study shows that knowledge accumulation, internal sharing, and external knowledge sharing all have a positive impact on firm performance, and decision quality partially mediates the impact of knowledge management on firm performance. This study adds value to the knowledge management literature by introducing decision quality as a mediating variable to examine the impact of knowledge sharing on firm performance in China. The findings of this study can help enrich the literature on knowledge management and firm performance and highlight the important impact of decision quality on knowledge management and firm performance. Management practitioners can also benefit from the findings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".