The Effects of Consideration of Future and Immediate Consequences on Willingness to Pay for Eco-Friendly Plant Attributes
Bibliographic record
Abstract
We investigated how differences in the consideration of future consequences (CFC) influence consumers' willingness to pay for edible and ornamental plants using data from plant auction experiments conducted in the U.S. and Canada. Specifically, the study focused on individuals' preferences for plant attributes related to production method, container type, and product origin. Individuals assigning higher importance to future consequences of their current decisions were willing to pay a price premium for plants grown using sustainable (16.7 cents) and energy-saving (16.5 cents) production methods, non-conventional containers such as compostable (18.2 cents) and plantable (14.3 cents), and locally produced plants (15.3 cents), and they expected a discount (37.8 cents) to purchase imported plants (i.e., produced outside the U.S.). In contrast, individuals assigning higher importance to immediate outcomes of their decisions were not willing to pay a price premium for the above mentioned attributes, with the exception of water-saving and compostable ones. The results contribute to our understanding of the effects of temporal considerations on choice decision making by horticultural consumers, and provide horticultural marketers with an opportunity to effectively position products that provide long- or short-term benefits.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".