Innovation Capacity and Entrepreneurial Intention: The Moderating Effects of Entrepreneurial Atmosphere
Bibliographic record
Abstract
There is natural relationship between innovative ability and entrepreneurial intentions (EI). Surprisingly, very little attention has been given to this important issue in previous literatures. Combining innovator’s DNA model with social cognitive theory, this paper collects 1263 samples to test the complex link between innovative capacity (operationalized as IC) and EI of potential entrepreneur. In addition, the effects of perceived entrepreneurial atmosphere (EA) on EI are examined. The results show that innovation capacity significantly affects EI, and perceived entrepreneurial desirability (ED) and feasibility (EF) mediates this relationship significantly. EA has direct effect on EI and indirectly changes the effect of IC through moderating the relationship between EF and EI. For robust test, we substitute dependent variable EI with entrepreneurial behavior and repeat the process above. The core results remain unchanged. The difference here is that actual entrepreneurial practice is not affected by attractiveness of start-up, innovator’s confidence to succeed in it is more important. This research confirms the necessary link between innovative capacity and EI, and substantially improves theexplaining efficacy of classical EI models based on personality traits. Our findings indicate that policymakers need to pay more attention to training and improving potential entrepreneurs’ innovative ability and create social atmosphere more suitable for innovators entering the process of starting a new venture. Through increasing attractiveness and the simplicity to be an entrepreneur, government can motivate innovators to be more willing to start a business and also help them succeed at ease.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".