Linking Developing Country Firms’ Relational Capital to Their Export Performance in Global Value Chains: The Moderating Role of Technological Turbulence
Bibliographic record
Abstract
Global value chains (GVCs) involve globally dispersed activities among interdependent firms. They provide an avenue for developing country firms to improve their export performance. A dominant view is that they can accomplish this outcome with close or trusted relationships with more established GVC partners. However, other factors determine how much such relational capital translates into superior export performance. Drawing on an interfirm learning perspective, we explain why the export effects of developing country firms’ relational capital with GVC buyers and suppliers could depend on technological turbulence. We hypothesize a positive relationship between these firms’ export performance and their relational capital with GVC buyers and suppliers. But we expect technological turbulence to weaken this relationship. Based on a sample of 95 Bangladeshi firms in the ready-made garment industry, this quantitative analysis reports evidence that partially supports our predictions. Specifically, we find a positive relationship between these firms’ relational capital with GVC buyers and their export performance. In addition, the higher the technological turbulence, the weaker this relationship. Overall, this research adds to the theory and practice of interfirm learning in GVCs from the perspective of developing country firms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".