Green purchase intention: The power of success in green marketing promotion ,
Bibliographic record
Abstract
The development of green marketing has received the attention of various levels of business around the world. The promotion of green marketing is now shaping all business sectors' practices, including the property industry. Yogyakarta is one of the cities whose industry has been developing so fast. This study explored green purchase intention to succeed in green marketing promotion. It examined how the relationship between green purchase intention with the variables supporting it and measuring its value. This research was completed with two methods: identifying the relationship between the green purchase intention variables (endogenous) with variables and their indicators (exogenous) that affect the value of green purchase intention. The identification using structural equation modelling. While the measurement of green purchase intention values was carried out using a dynamic system simulation. The data was collected through the survey method on 400 sample sizes. The results obtained, a significant relationship between 4 exogenous variables and the endogenous variable and also obtained some prediction value per unit time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".