Gardening Consumer Segments Vary in Ecopractices
Bibliographic record
Abstract
Savvy marketers rely on the principles of customer segmentation and product targeting to more efficiently allocate scarce resources and effectively reach groups of consumers with similar likes, preferences, or demands. Our objective was to identify and profile consumer segments with regard to their gardening purchases to determine whether there were differences in their ecofriendly attitudes and behaviors such as recycling. Our underlying hypothesis was that different types of gardeners may exhibit more environmentally friendly behavior, predisposing them to be more receptive to product innovations specifically designed to be ecofriendly. Researchers collected plant purchases, recycling attitudes and behaviors, and preferences for ecofriendly containers from 763 consumers in Indiana, Michigan, Minnesota, and Texas. A cluster analysis based on plant purchases yielded three consumer segments: low use, woody plant buyers, and herbaceous plant buyers. There were some differences with regard to recycling behaviors among consumers in the three groups, including recycling aluminum drinking cans, newspapers, magazines, use of energy-saving bulbs, and composting yard waste. Generally, herbaceous plant buyers were most ecofriendly followed by woody plant buyers and low use. Given these differences, there appears to be some merit in the future to segment consumers by plant purchases versus others to target specific types of ecofriendly products to them.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".