Local Network Relationships and the Internationalization of Small Knowledge-Intensive Firms
Bibliographic record
Abstract
This paper discusses the role of network relationships in the internationalization of small knowledge-intensive firms (SKIFs) by highlighting their local, spatially concentrated network relationships, which can serve as a significant local resource. Little is known in this regard with respect to a developing economy context. Primarily on the basis of a study of four case-firms in the Bangalore software industry and available secondary data, two issues are dealt with: (a) how local network relationships – such as those within clusters or industrial districts – are developed and (b) the impact that these relationships have on the internationalization of SKIFs, specifically in respect to enhancing international competitiveness. Three effects of local network relationships on the internationalization of SKIFs, viz., reputation-related, quality-related and networking benefits, are noted. However, it also emerged from follow-up interviews with local academic experts that these benefits may be passively rather than actively accrued, suggesting that some valuable local resources may be overlooked or wasted.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".