Relationship of External Knowledge Management and Performance of Chinese Manufacturing Firms: The Mediating Role of Talent Management
Bibliographic record
Abstract
For the competitive market, both talent management and knowledge management of employees are key primary resources in organizations. While it is well known that in today's emerging economy, intangible resources like knowledge and human capital seem as the soul of survival; few studies have examined the effect of external knowledge management and talent management strategies in Chinese manufacturing firms. This study tries to bridge this gap by examining the importance of external knowledge management and talent management, Moreover, how this consequence can affect in particular industry for the economic growth of China? Total 249 responses were collected through structured questionnaire from manufacturing organizations located in Shanghai and Suzhou, China. PLS-SEM techniques via Smart-PLS (3.2.4) software has been used to test and validate proposed model and the relationships among the hypothesized constructs. The findings of this study show that external knowledge management (E-KM) and talent management both contributes positively to the performance of manufacturing firms. Moreover, talent management as mechanism demonstrated strong mediation effects between E-KM and performance. In researchers' point of view and results revealed the evidence by linking E-KM with TM-OP and TM as a mechanism between E-KM and OP. Such insights may helpful for managers to target sustainable current and future growth of the organizations and also, to overcome the shortage of talented and qualified worker’s issues in fast-growing emerging economies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".