The Effects of Export Market Re-Entry and Time-Out Period on Innovation
Bibliographic record
Abstract
While flourishing research has analyzed exporters’ ex-post improvements on innovation via the learning-by-exporting (LBE) effect, we know little about how exporters’ time-out periods and re-entry to foreign markets impact their knowledge stock and LBE processes. To analyze these issues, this paper draws on organizational learning and dynamic capabilities perspectives to assume that as international markets provide exporters with access to innovation knowledge and capabilities, re-entering those markets will enable firms to benefit again from these innovation inflows. Furthermore, re-entrants may be able to leverage their market-specific export heritage from prior entries. Still, export heritage may be subject to decay, so we also hypothesize that a long time-out period will have a negative effect on innovation outcomes at the time of re-entry. Finally, we argue that in markets with higher innovation levels exporters may experience greater depreciation of their export heritage, causing a long time-out period to have a larger negative impact on learning outcomes that in those markets with lower innovation levels. This paper tests these assumptions by drawing on a sample of 1151 Spanish manufacturing firms from 1990 until 2016. The results offer relevant implications for research, managers, and policymakers.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".