Dynamic capabilities as a moderator: enhancing the international performance of SMEs with international entrepreneurial orientation
Bibliographic record
Abstract
Abstract This paper explores the impact of International Entrepreneurial Orientation (IEO) on the international performance of Small and Medium-sized Enterprises (SMEs), with a focus on the post-COVID-19 era. IEO, treated as a subdimension of entrepreneurial orientation, is crucial for SMEs in global markets, especially given the challenges posed by the pandemic. The study examines dynamic capabilities as a moderating factor in the IEO-international performance relationship, based on the resource-based view (RBV). A survey involving 120 internationalized SMEs from industrial and service sectors was conducted, and data were analyzed using Structural Equation Modeling (SEM) through Partial Least Squares (PLS). The findings indicate that seizing and reconfiguring capabilities significantly enhance the IEO-international performance link while sensing capabilities do not show a notable impact. This research contributes to the literature by affirming the role of dynamic capabilities in strengthening SMEs’ international performance through IEO, highlighting the differential impact of various dynamic capabilities, and offering insights into the specific roles of these capabilities as moderators in the IEO-international performance relationship. The study underscores the importance of strategic entrepreneurial orientation and dynamic capabilities for SMEs in the global market.
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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".