Green Entrepreneurship: A New Paradigm for Millennials in Indonesia
Bibliographic record
Abstract
The number of young entrepreneurs in Indonesia is very low when compared to global data. Meanwhile, environmental issues in Indonesia are in a state of emergency. We are interested in conducting research on millennials as a productive generation and, according to several studies, a generation with high environmental awareness. The purpose of this study is to assess their desire to become environmentally conscious entrepreneurs. The variables used in this study are Green Awareness and Green Knowledge, which will be reflected in their Green Entrepreneurial Behavior via the mediation of Green Entrepreneurial Intention. This study differs from previous studies in that it investigates the millennial generation, not only their desire to become environmentally friendly entrepreneurs, but also their future behavior once they become entrepreneurs. Data was gathered through the use of an online questionnaire, specifically a Google form. Purposive sampling was used, yielding 217 responses from millennials living in and around Jakarta, a metropolitan city known for producing the most young entrepreneurs. The data was processed using PLS-SEM (Partial Least Square Structural Equation Modelling) with SmartPLS 3.2.8. According to the study's findings, improving Green Awareness and Green Knowledge could lead to an increase in environmentally conscious entrepreneurs. The government, educational institutions, and the companies can work together to carry out environmental awareness campaigns and provide environmental knowledge so that future entrepreneurs can become environmentally oriented entrepreneurs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".