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Record W3127531363 · doi:10.1371/journal.pone.0245953

Tourist willingness to pay for local green hotel certification

2021· article· en· W3127531363 on OpenAlexaff
Katherine Nelson, Stefan Partelow, Moritz Stäbler, Sonya Graci, Marie Fujitani

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsToronto Metropolitan University
FundersLeibniz-GemeinschaftWaitt Foundation
KeywordsWillingness to payBusinessCertificationSustainabilityMarketingPrice premiumTourismSustainable tourismPaymentEcosystem servicesEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

This study aims to understand tourists' willingness to pay a price premium for a local green hotel certification, and is one of only a few in the literature for small-island tourism destinations in emerging economies with their unique and pressing sustainability challenges. In a survey of 535 tourists visiting Gili Trawangan, Indonesia, facing numerous waste management and coral reef conservation issues, the willingness to pay extra for sustainable hotel services was elicited. There were five discrete pricing levels across the surveys that ranged from $0.75 USD to $7.50 USD extra per night. We examined the relationship of the respondents' payment choice to their socio-demographic attributes and attitudes regarding environmental issues such as climate change. The main findings and practical implications of the study are: (1) to demonstrate the broad willingness to pay for sustainable hotel services. Findings indicate at all price levels (between $0.75 USD and $7.50 USD), more than 50% of tourists are willing to pay. (2) To estimate a lower bound mean willingness to pay per night for a local green hotel certificate of $1.55USD and 1.34€ EUR, and (3) To identify individual attributes that influence willingness to pay. Findings indicate environmental knowledge and preferences play a role. These results can be used generally to incorporate evidence-based practices into the development of a green hotel marketing strategy, and to help define the target market for small-scale green hotel certification. Additionally, we propose a finance strategy for funding local and sustainable initiatives that support the hotel industry and the island's infrastructure through the premiums collected from the 'Gili Green Award' certificate.

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 score0.104
Threshold uncertainty score0.527

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 head0.038
GPT teacher head0.217
Teacher spread0.179 · 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

Citations95
Published2021
Admission routes1
Has abstractyes

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