Host-Site Dynamics and their Impact on China’s Investment in Malaysia: A Fuzzy-Set Qualitative Comparative Analysis
Bibliographic record
Abstract
This study examines host-site political and socioeconomic conditions under which China-funded projects in Malaysia shape local acceptance and avoid contestation. Employing a fuzzy-set qualitative comparative analysis (fsQCA) of 35 China-funded projects and 60 in-depth interviews, this study identifies multiple interrelated causal pathways to locally accepted investment. Two conditions consistently emerge as necessary to minimize contestation: 1) strong political alignment between federal and state governments, and 2) the deep integration of the local workforce. Notably, the absence of bumiputra equity or employment also emerges as a necessary condition, challenging prevailing assumptions on the role of Malaysia's affirmative action policies. This study finds that, rather than relying on mandated inclusion, firms that engage with bumiputra communities through externally oriented corporate social responsibility (CSR) initiatives are more likely to achieve sustained social acceptance. Furthermore, a combination of substantive technology spillovers and proactive community-based engagement significantly enhances local receptivity. Together, these findings offer valuable insights for policymakers, international investors, and scholars concerned with the governance of FDI in politically plural and institutionally complex settings. The study highlights the critical need for adaptive localization strategies that align commercial goals with the social and political fabric of host countries, both in Malaysia and across the broader Asia-Pacific region.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".