Consequences of Cultural Leadership Styles for Social Entrepreneurship: A Theoretical Framework
Bibliographic record
Abstract
The purpose of this conceptual article is to understand how the interplay of national-level institutions of culturally endorsed leadership styles, government effectiveness, and societal trust affects individual likelihood to become social entrepreneurs. We present an institutional framework comprising cultural leadership styles (normative institutions), government effectiveness (regulatory institutions), and societal trust (cognitive institutions) to predict individual likelihood of social entrepreneurship. Using the insight of culture–entrepreneurship fit and drawing on institutional configuration perspective we posit that culturally endorsed implicit leadership theories (CLTs) of charismatic and participatory leadership positively impact the likelihood of individuals becoming social entrepreneurs. Further, we posit that this impact is particularly pronounced when a country’s regulatory quality manifested by government effectiveness is supportive of social entrepreneurship and when there exist high levels of societal trust. Research on CLTs and their impact on entrepreneurial behavior is limited. We contribute to comparative entrepreneurship research by introducing a cultural antecedent of social entrepreneurship in CLTs and through a deeper understanding of their interplay with national-level institutions to draw the boundary conditions of our framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".