Export duration and product innovations: do born globals learn by exporting differently?
Bibliographic record
Abstract
Purpose This paper aims to study the learning-by-exporting effect among small-to-medium-sized enterprises (SMEs). Specifically, the authors propose a dynamic perspective and suggest that learning-by-exporting is duration-dependent and contingent upon the born global internationalization strategy. In earlier phases of export activities, exporting has had a strong positive effect on SMEs’ innovations, which, however, diminishes over time. This inverted U-shape effect is even more distinct for born global firms. Design/methodology/approach The authors used longitudinal data with 1,689 Canadian SMEs to test their hypotheses. A two-stage instrumental approach is used to take into account the endogeneity of the born global international strategy on new product innovations. Findings Born globals learn faster at the early stages of exporting but also restrain their innovations more strongly than gradual internationalizers in the longer run, leveling out the initial learning advantages of newness. Thus, this study suggests that born globals have a significantly different learning trajectory than gradual internationalizers. Practical implications To maximize the benefits of exporting on innovation, managers should focus on learning during the initial years of exporting. However, once this period has passed, it is advisable for managers to invest in research and development as well as other innovation activities to complement the learning effect of exporting. Born global firms experience more rapid learning at the initial stage of exporting, but such learning effects wear off quicker later than gradually internationalized firms. For SME managers, this study helps draw their attention to the learning benefits of exporting in the initial years of export participation. Originality/value This study corroborates recent studies arguing for a “learning-by-exporting” effect. Providing longitudinal firm-level evidence, the authors also forward a dynamic perspective and show that learning by exporting is duration dependent and contingent upon the market entry strategy pursued by SMEs.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".