MNCs’ R&D talent management in China: aligning practices with strategies
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".