The Role of Cognitive Style in Influencing Entrepreneurial Self-Efficacy and Subsequent Entrepreneurial Intention
Bibliographic record
Abstract
Cognitive style has been largely acknowledged to highly contribute to explaining variances in individuals’ behavior. However, very few researchers studied the role of cognitive style in influencing entrepreneurial self-efficacy along the entrepreneurial intention process. Therefore, the purpose of this research is to study how differences in preferences towards linear, non-linear and balanced thinking style would affect individuals’ self-perceptions towards entrepreneurial self-efficacy and subsequent intentions to create a new business. This study’s findings reported that non-linear thinking style is negatively correlated to entrepreneurial self-efficacy which subsequently affects entrepreneurial intentions negatively. While linear thinking style was positively correlated to entrepreneurial self-efficacy which in return affects entrepreneurial intentions positively. Moreover, thinking style balance was found to be positively correlated to entrepreneurial self-efficacy that exceeds the magnitude of the Linear-Entrepreneurial Self-Efficacy relationship which subsequently affects intentions positively. Furthermore, the relationship between entrepreneurial self-efficacy and entrepreneurial intentions had higher significance and was stronger in effect for individuals with balanced thinking style than for those with linear and non-linear thinking style.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".