MNCs’ R&D talent management in China: aligning practices with strategies
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to propose practical recommendations in accordance with the strategic roles played by research and development (R&D) in multinational companies (MNCs). Design/methodology/approach This study applies a qualitative method to investigate the talent management (TM) practices implemented in MNCs’ R&D units. Findings The findings identify four R&D strategies and four sectors of TM practices. Furthermore, there exists an alignment between R&D strategies and TM practices. Research limitations/implications This paper has several limitations. This qualitative research is exploratory, and larger samples or quantitative methods are needed to ensure the wider applicability of the findings. When possible, longitudinal studies yield superior results in revealing the evolving strategic roles of R&D subsidiaries and their TM practices. The authors used China as the research context, and similar studies in other emerging countries with active R&D activities are required to further validate or complement the findings in this study. Practical implications This study has some practical implications for companies with regard to aligning their TM practices with R&D strategies. Originality/value R&D units play an increasingly significant role in MNCs and TM is a key issue. However, there is a lack of TM research focusing on R&D employees by taking strategies into account.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it