CONTEXTUALIZING ENTREPRENEURIAL INTENTIONS: ECONOMIC DYNAMISM AND CULTURE IN MULTILAYERED RELATIONSHIPS
Bibliographic record
Abstract
We examine how economic dynamism, along with the cultural orientation of individualism vs. collectivism shape the multilayered relationships between perceptual variables—self efficacy, attitude, social capital and perceived opportunities—and entrepreneurial intentions (EI). For the first time, we introduce economic dynamism as a national context variable for EI. We also join a group of entrepreneurship scholars to apply a multilayered approach to account for the multiple interactions among individual and contextual variables. We test our hypotheses using country-level aggregates of GEM data. For comparison purposes, we consider four nations differentiated along the dimensions of economic dynamism and the cultural trait of individualism vs. collectivism, namely, China, Italy, Japan and the United States. The results show that self-efficacy predicts EI across all four nations; the interactive effects between perceived opportunities and attitude and between social capital and attitude are contingent upon national contexts in terms of economic dynamism and individualism vs. collectivism. Although economic development long has been a popular contextual variable in the study of EI, there remains a lack of empirical support. One reason is economic development assumes a stable state, when in fact changes in an economy may be more critical in impacting entrepreneurial intentions. In the current study, we replace economic development with that of economic dynamism. Future research needs to refine the construct and develop a measure of it.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".