The<scp>I</scp>n Situ Upgrading of Japanese Electronics Firms in<scp>M</scp>alaysian Industrial Clusters
Bibliographic record
Abstract
Abstract The ability of clusters generated by direct foreign investment (DFI) in emerging economies to generate sustained, value‐added growth is a matter of controversy. This article assesses this debate with reference to the role of Japanese electronics multinational corporations (MNCs) in the development of clusters inMalaysia. Conceptually, we present a typology ofDFI‐generated industrial clusters that represent increasing degrees of commitment to local value creation and upgrading. Empirically, we conducted a survey of 10 Japanese firms inMalaysia that examined whether or not their factories increased technological upgrading, increasingly embedded their operations through using local skilled labor and supply firms, and responded positively to national policies and cluster‐governance measures supporting the electronics industry. We found thatJapanese firms had clearly moved beyond simple assembly‐based to embedded clustering but had not progressed further to technology‐intensive behavior because of the poor technological environment inMalaysia, as well asJapaneseMNCs' strategies that depend on technology from headquarters. Nonetheless,JapaneseMNCswere sufficiently embedded inMalaysia to upgrade production to digital consumer products, and semiconductor assembly has flourished, warding off competition fromChina and low‐cost locations in the Association of Southeast Asian Nations. At the end of the study period,Malaysia remained an attractive location forJapanese electronicsMNCs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".