Time to come back: the effects of export market re-entry and time-out period on innovation
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how exporters’ time-out periods and re-entry to various export areas impact their knowledge stock and capacity to learn from foreign markets. Design/methodology/approach This paper introduces the concept of innovation divergent export areas (IDEXAs), which refers to a group of countries with relatively similar average levels of innovation capabilities (intra-area homogeneity), and different from other areas (inter-area heterogeneity), as measured by their R&D expenditures over gross domestic product (GDP). This paper tests the hypotheses on a longitudinal sample of Spanish manufacturing companies that exported to different IDEXAs from 1990 until 2016. Findings The findings suggest a positive effect of IDEXA re-entry on new product and process introductions and a negative impact of a time-out period of four or more years for those export areas with higher innovation levels. Practical implications Re-internationalization offers exporters the opportunity to reuse the knowledge gained in prior exporting episodes to increase their chances of success. Hence, it is important that managers make sense of the potentially damaging exit experience, to avoid repeating the same mistakes and perform better the next time around. Originality/value This study investigates for the first time the effects of re-entry to specific export areas on exporters’ capacity to increase their innovation output. Hence, it contributes to the international business literature by examining the performance consequences of companies’ re-internationalization, a key and under-researched topic. Furthermore, most studies focus on full withdrawal from foreign markets and ignore the more common microscopic decisions concerning withdrawing from one or more export areas.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.016 | 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".