Consumer Segmentation of Green Financial Products Based on Sociodemographic Characteristics
Bibliographic record
Abstract
Many green financial products currently have a low financial return level; even so, these products are spreading dynamically. In our study, we explored Hungarian green financial investment preferences and separated consumers of green financial products into homogeneous groups, which were characterized on the basis of sociodemographic characteristics. In the case of investments with a similar risk, using the sample we examined we proved that there is a homogenous group (C2) in Hungary which prefers green aspects to higher financial returns in the course of its investment decisions. We separated a group (C3) which can be considered influenceable, and we concluded that, with the application of appropriate marketing activities, this group could be a potential target consumers for national banks and traders of green financial products in the future. Young females are the main target consumers for green financial products in Hungary, and they are the largest majority of the C2 group, for whom financial rationality takes a backseat to green aspects. Based on the results of our study, national banks and traders of financial products can create a more accurate and effective marketing strategy for their products on the Hungarian market.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".