Chinese expatriates working with African partners: power struggles and knowledge hiding
Bibliographic record
Abstract
Purpose The study explores African partners' experiences regarding Chinese expatriates' knowledge control practices in 29 Sino-African joint ventures in 12 countries. It provides insights into power dynamics and knowledge transfer (KT) from African partners' perspective. Design/methodology/approach The qualitative paper mobilized semi-structured interviews with Africans who worked with Chinese expatriates across Africa. The study focused on understanding the experiences of African partners when collaborating with their Chinese expatriate colleagues on assignments in joint ventures (JVs) in Africa. Findings Chinese expatriates employed five tactics, as described by African partners, to control knowledge based on power, behaviors and knowledge type. Particularly, through the lens of unofficial power, this study explains knowledge hiding tactics between knowledge-holding Chinese expatriates and host country knowledge-seeking locals. A new dimension of authority-based knowledge hiding is discovered. Originality/value The paper brings new insights into the analysis of power (official and unofficial) boundaries regarding knowledge control mechanisms in joint venture collaborations between employees from China and Africa. Unofficial power appeared as a major leverage for expatriates in monopolizing their strategic knowledge. The study recommends mobilizing African diaspora and repatriates from China to improve KT for Africa.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".