Empirical evidence how social capital effects the internationalisation process of SME in Zhejiang
Bibliographic record
Abstract
SMEs (small and medium-sized enterprises) are taking the similar way towards internationalisation as large MNCs (multinational corporations). The two participants have similar problems in the internationalisation process, but the conditions for SMEs are completely different due to their limited resources. Social capital is influencing the process and can be a compensation for the disadvantages SMEs are confronted with. This study shows how social capital affects the internationalisation speed and performance of SMEs in China. Using the existing research as a framework, this paper proposes hypotheses concerning various aspects of social capital in terms of networks or ties to key institutions and discusses their effects on the SME’s internationalisation. The hypotheses are tested on a representative sample of 99 SMEs located in Zhejiang (China) with the help of a regression analysis. The findings indicate that some aspects of social capital contribute to a superior performance and a faster internationalisation speed. The results of this study can help managers and founders choose proper business strategies or representatives of political institutions setting policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".