Organisational resilience of small and medium enterprises (SMEs) and their firm performance in internationalisation
Bibliographic record
Abstract
The main research question is what the effects of organisational resilience of small and medium enterprises (SMEs) in conditions of uncertainty and disruption are on their current firm performance in internationalisation. This research aims to contribute to the existing literature by addressing this question, using international market orientation as a mediator, and home country institutional support and entry mode selection as moderators. A quantitative method was adopted, with data collection via surveys. The research first analysed 91 effective responses from SMEs in the UK and Canada from a pilot study to explore potential relationships. Based on these results, minor adjustments were made and further surveys were conducted. This research employed exploratory factor analysis, confirmatory factor analysis, and structural equation modelling, using software including SPSS, PROCESS, and Analysis of Moment Structures (AMOS). A total of 241 effective responses from SMEs based in the UK, Canada, and the US were used in the final analysis. The findings show that SMEs’ organisational resilience in conditions of uncertainty and disruption has a significant and positive impact on current firm performance in internationalisation. Full mediation effects were found, and potential moderation effects were observed in certain situations. This research contributes to understanding the practical application and strategic implications of organisational resilience and the mechanisms that support SMEs in internationalisation. It also enhances the measurement rigour of organisational resilience. Furthermore, the research expands theoretical applications by examining the combined relevance of the research-based view, dynamic capability view, and institutions, through the use of international market orientation as a mediator, and home country institutional support and entry mode selection as moderators. Finally, the research contributes to understanding the relationships among entry modes, risk management, and 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".