Revealing the research potential for the field of cross-cultural entrepreneurship: lessons from an integrative literature review
Bibliographic record
Abstract
Culture plays an important role for the study of entrepreneurship. However, whereas cross-cultural research in management (CCM) has strongly evolved in the last three decades and identified different paradigms, paradigmatically diversified research is still lacking in cross-cultural entrepreneurship. To fill this gap, this study suggests an integrative literature review with two objectives: 1) provide an overview of cross-cultural entrepreneurship research with an attention to national culture, different paradigms, and research themes, and 2) point towards possibilities to enrich such research. Through an integrative literature review, 147 studies of cross-cultural entrepreneurship research were identified and regrouped according to two main paradigms in CCM research: positivism and interpretivism. The analysis of all papers led to the emergence of five research themes according to which the papers were regrouped. Based on this matrix of paradigms and research themes, all texts were categorized into 10 areas. Findings show the dominance of cross-cultural entrepreneurship studies based on the positivist paradigm of culture, whereas research rooted in the interpretive paradigm is rather unexplored and offers great potential for future research. Based on these findings, we argue that particularly rich qualitative research designs offer interesting opportunities for developing the field of cross-cultural entrepreneurship.
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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 | 0.044 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".