The Effects of Firm Relational Capital on Export Performance: The Moderating Role of Technological Turbulence
Bibliographic record
Abstract
Global value chains (GVCs) offer a range of opportunities to manufacturers interested in increasing their export market share by utilizing their business relationships with other firms. In recent studies, it is recognized that relational capital helps manufacturing firms to enhance their competitiveness in global markets. However, prior research does not provide a conclusive account of the impact of relational capital on their export performance in general, and particularly in the context of developing countries. Drawing on a learning-based perspective and contingency approach, this study fills these gaps by theorizing the link between relational capital and firm performance with a focus on developing-country firms that participate in GVCs. Specifically, we propose that the relational capital of these firms will have a stronger positive impact on their export performance when the market and technological turbulence are lower. The results confirm the key hypotheses by showing that developing-country firms' relational capital with buyers has a positive and significant impact on their export performance and that technological turbulence negatively moderates the relationship between relational capital with buyers and export performance. Overall, this research extends the literature on knowledge transfer, interfirm relational capital, and business performance in a developing country context.
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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 | 0.010 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".