The role of digital industrial strategy and human creativity in transformational technopreneurship development
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between industrial digital strategy in the area of transformational technopreneurship development and analyze the relationship between human creativity in the area of transformational technopreneurship development. This study uses a quantitative approach with an explanatory research design, which aims to examine the influence of integrity, organizational commitment, and motivation on sustainable employee performance with job satisfaction as a mediating variable. The population consists of all employees of SME organizations, totaling 765 employees. The sampling technique applied is simple random sampling. The research instrument is a questionnaire using a 5-point Likert scale. The research variables are: Digital Work Environment (X1), Job Satisfaction (X2), Organizational Culture (X3) and Employee Work Productivity (Y). Data were analyzed using Partial Least Square – Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The analysis consists of two stages: Outer Model (Measurement Model): Convergent validity, discriminant validity, and reliability testing. Inner Model (Structural Model): Testing path coefficients, R² values, and direct and indirect influences between variables. The results of the study show that the Industrial digital strategy variable has a positive relationship with the transformational development area of technopreneurship, human creativity has a positive relationship with the transformational development area of technopreneurship.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".