How civic engagement sparks entrepreneurial intention: the mediating role of well-being
Bibliographic record
Abstract
The underlying mechanism by which civic engagement promotes entrepreneurial intention remains insufficiently understood. This paper aims to develop a theoretical model proposing that eudaimonic well-being mediates the connection between civic engagement and entrepreneurial intention. The models are tested on a representative sample extracted from the Gallup World Poll, which consists of 104,342 individuals across 35 countries worldwide. The primary methodological approach is the multilevel mixed-effects logistic regression model, with structural equation modeling used as a supplementary analysis. The findings reveal that the sense of connection and commitment towards the greater community is strongly associated with the intention to start a business. More importantly, the study highlights the role of eudaimonic well-being, which is one of the key psychological benefits of civic engagement, in mediating the relationship between civic engagement and entrepreneurial intention. The hedonic aspect of well-being plays a limited role in this relationship. Finally, this paper advances understanding in entrepreneurship research by (1) uncovering the formation of entrepreneurial intent from the psychological benefits of civic engagement, (2) disentangling hedonic and eudaimonic well-being, and (3) demonstrating well-being as an important resource of venturing ambitions. Finally, this study aligns with recent calls for further exploration of non-economic motivators of 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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".