Dominant determinant characteristics of innovative behavior of new entrepreneur candidates
Bibliographic record
Abstract
In the global competition era and in Covid 19 pandemic period, the innovative competence of emerging entrepreneurs relies on the positive characters they should have. The present study was aimed to formulate a reinforcement model of innovative behavior through the identification of dominant determinant factors. The study employed survey methods and involved variables of innovative behavior (Y), creativity (X1), technology literacy (X2), and risk-taking behavior (X3). The respondents were 86 final year students of Faculty of Business Economics (emerging entrepreneur candidates) as the samples, and the data were further analyzed by multiple regression. The creativity (X1), technology literacy (X2), and risk-taking behavior (X3) contribute simultaneously at 45.70 percent to the development of innovative behavior (Y); b) the applicable prediction model of innovative behavior is Y=1.171+0.622X1+0.170X2 -0.080X3; meanwhile, the creativity yielded the most significant sensitivity in developing the innovative behavior variable compared to the technology literacy and risk-taking behavior variables; c) it is worth noting that risk-taking behavior is not among the contributing factors; in fact, this variable constrains an individual to be innovative (if it is too high); e) the variable of creativity is in line with innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".