A structural analysis of barriers to emerging economies’ participation in the compound semiconductor manufacturing supply chain
Bibliographic record
Abstract
Purpose Compound semiconductors are required to achieve advanced technologies. However, their supply chain remains limited to developed economies. This study aims to investigate the barriers preventing emerging economies, such as Bangladesh, from integrating into this supply chain. Design/methodology/approach Drawing on the Resource-based View (RBV), the study conceptualizes barriers as constraints and applies a two-stage approach: expert interviews identify key barriers, followed by Interpretive Structural Modeling (ISM) to analyze their interrelationships. Findings The study identifies core barriers such as the lack of cleanroom and fabrication facilities, weak infrastructure, limited financial access and insufficient research and development capabilities. Barriers related to institutional coordination, global integration and regulatory standards are found to be secondary outcomes of deeper and broader systemic deficiencies. Research limitations/implications The study extends RBV to the national level and offers a model that future research can apply to examine barriers to value chain participation in emerging economies. Practical implications The study provides insights for policymakers, development agencies and industrial strategists seeking to design better-targeted tasks for participation in value chains. Originality/value This study contributes to the developing application of the RBV by extending its analytical scope beyond the firm level to examine barriers at the broader national levels.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".