Behavioral Intention and Willingness to Pay Premium for Green Hotel Concept: The Role of Trust and Green Hotel Attributes
Bibliographic record
Abstract
This study aims to assess the behavioral intention and willingness to pay a premium for green hotel concept through the role of green hotel attributes and trust.Data was collected through online questionnaires on 157 respondents in Indonesia who have stayed at hotel in the last three years.Findings show that contribution to environment conservation, located in a cleannatural environment, and use recycled materials are perceived most important attributes that should be implemented by hotel.On the contrary, the use of low-flow showerheads and sink are perceived less priority for green hotel concept.The results show a positive influence between green hotel attributes on intention to stay with significant mediation of trust toward green hotels.However, trust toward green hotels does not have a positive effect on willingness to pay a premium for green hotels.This study contributes to the literature of green hotel by providing an insightful customer point of view about green hotel, both from prospective and existing customer, so that hotel industry able to better adopt green practices as a part of green marketing strategy and sustainable development.Future research conducted for green hotel brand or characteristic will eventually provide deep understanding.Thus, hotel will comprehend better on how their customer perceived willingness to pay premium and trust from its attributes.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".