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Record W2787251298 · doi:10.24266/0738-2898.32.2.64

The Effects of Consideration of Future and Immediate Consequences on Willingness to Pay for Eco-Friendly Plant Attributes

2014· article· en· W2787251298 on OpenAlexaboutno aff
Hayk Khachatryan, Chengyan Yue, Ben Campbell, Bridget K. Behe, Charlie Hall

Bibliographic record

VenueJournal of Environmental Horticulture · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsWillingness to payProduction (economics)Product (mathematics)Price premiumBusinessWillingness to acceptPosition (finance)MarketingEconomicsNatural resource economicsMicroeconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.189
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2014
Admission routes1
Has abstractyes

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