Experience, resources and export market performance
Bibliographic record
Abstract
Purpose Based on integrating learning, resource-based and social network theories, the purpose of this paper is to shed fresh light on the association between export experience and export performance by seeking to better understand the links between them, and assessing the boundary conditions, moderators, mediators, and non-linear relationships in greater depth. Design/methodology/approach This paper mobilizes a quantitative research design using a survey of Brazil-based exporters. The authors test the hypotheses proposed in this study by employing moderated mediation regression models. Findings The authors find support for aJ-shape relationship between export experience and export market performance. In particular, the authors find that innovation and international marketing resources mediate the effect of export experience on export market performance, and the authors unveil that this mediation effect is contingent on the strength of international business network ties. Originality/value This study advances the export marketing literature by explaining how export experience drives export success in two ways: first, by clarifying the ambiguity in extant theoretical explanations and previous empirical findings regarding the shape of the relationship between export experience and export performance. Second, this study reconciles the disagreement as to whether superior export performance results from exporters’ existing resources or from their learning by exporting. Thus, the paper is valuable for scholars and export managers or policymakers alike by providing recommendations on how less experienced firms can overcome the initial period of weak export performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".