The impact of national culture on entrepreneurial performance and management practices among Vietnamese entrepreneurs
Bibliographic record
Abstract
This study examines how national culture influences management practices and entrepreneurial performance among Vietnamese entrepreneurs in native and diaspora settings, highlighting the impact of cultural dimensions such as power distance, indulgence, and masculinity. Using a sample of 375 Vietnamese entrepreneurs, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the data. It addresses three research questions: how national culture shapes management practices (RQ1), how these practices impact entrepreneurial performance (RQ2), and whether they mediate the cultural influence on performance (RQ3). The findings highlight the critical role of cultural adaptation in business strategy. Results reveal that cultural alignment significantly boosts entrepreneurial performance. The study emphasizes the critical interaction between cultural dimensions and management strategies, particularly highlighting the role of indulgence—a more recent addition to Hofstede’s cultural dimensions. The findings provide valuable insights for entrepreneurs and policymakers. Entrepreneurs can achieve greater success by integrating cultural nuances into management strategies. Policymakers can enhance entrepreneurial activities by supporting culturally informed education and training programs. This study contributes new insights into the influence of indulgence and other cultural dimensions on entrepreneurship. It extends existing research by considering interactions between different cultural dimensions and their combined effect on management practices and entrepreneurial 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".