The relationship between entrepreneurial intention and behavior: A meta-analytic review
Bibliographic record
Abstract
A vibrant literature studying antecedents of entrepreneurial intentions is largely motivated by an often implicit assumption that they will be followed by subsequent entrepreneurial behaviors or actions. A much smaller number of studies actually test this assumption. Their results suggest that while the entrepreneurial intention–behavior relationship is usually present, its strength turns out highly contextual. This meta-analysis intends to integrate and summarize the available research base on the entrepreneurial intention–behavior relationship, assessing the moderating impacts of environmental, demographic and methodological factors. Data from 75 studies (150,703 individuals) were included in the analysis. Our results indicate that the focal relationship is robust across environmental contexts, populations, and methodologies except for the measures used for entrepreneurial behavior, the use of a database compared to collecting new data, and the duration of time between intention and behavior. Additionally, entrepreneurial intentions were found to account for only 17% of the variance in entrepreneurial behaviors as opposed to the commonly expected and cited 37%. Our findings suggest theoretical and methodological considerations for future work aimed at exploring and overcoming the non-trivial intention–behavior gap and we encourage the discovery of cognitive and behavioral factors reinforcing the intention–action translation at different levels of analysis and over time.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".