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Record W4389848459 · doi:10.1177/14673584231221967

At home and abroad: Comparing sustainable behaviour and willingness to pay across contexts

2023· article· en· W4389848459 on OpenAlexaff
Rachel Dodds, Mark Robert Holmes

Bibliographic record

VenueTourism and Hospitality Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsSustainabilityWillingness to payAltruism (biology)FrugalityTravel behaviorMarketingConsumer behaviourSustainable consumptionEnvironmentally friendlyConsumption (sociology)Construct (python library)Sociocultural evolutionSustainable livingBusinessEconomicsSocial psychologyPsychologyMicroeconomicsSociologyEcology

Abstract

fetched live from OpenAlex

Does a person’s everyday behavior at home influence their desire to travel sustainably and pay for it? Testing the Holmes, Dodds and Frochot (HDF) model, this research sought to understand the influence that daily behavior – measured by frugality, altruism, and pro-environmental behavior – has on both sustainable travel behavior and a traveler’s propensity to pay. This paper augments the HDF model in that it finds sustainable travel behavior to be not just a single construct, but rather influenced separately by sociocultural, environmental and local consumption behaviors. Second, this study also examines how these differences in sustainable travel influence the traveler’s propensity to pay. The findings of this study explain that day-to-day behavior at home does explain a traveler’s propensity to pay for sustainability efforts when traveling. Those who are more altruistic are more likely to be more environmentally friendly and more likely to look for local experiences when traveling. Those who are more environmentally minded at home are also more likely to seek out cultural, environmentally friendly and local experiences when traveling. In contrast, those who are more frugal are less likely to be environmentally friendly when traveling.

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)0.002
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 score0.023
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.025
GPT teacher head0.371
Teacher spread0.346 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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