A vicarious learning perspective on the relationship between home-peer performance and export intensity among SMEs
Bibliographic record
Abstract
Purpose Home-peer firms (i.e. firms from the same industry and country) noticeably influence the internationalization behavior of small-to-medium-sized enterprises (SMEs). Drawing from vicarious learning literature, the authors theorize how home-peer firms' success in export markets affects SMEs' export intensity into those markets. Design/methodology/approach The authors test the hypotheses on a sample of 32,108 Canadian SME exporters. A Tobit model was used to examine the effect of home-peer performance and its interactions with firm age, export experience, and geographic and institutional distance on export entry intensity. Findings The authors find that SMEs are more likely to enter export markets with higher intensity if home-peer firms perform well in those markets. This home-peer influence is stronger when the SME lacks export experience, when the home-peer information is more recent, and when environmental uncertainty is high. Originality/value The study is among the first to show empirically that the performance of home-peers positively influences the export intensity of SMEs in international markets, suggesting that SMEs use this measure to inform their internationalization strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".