An investigation on green attitudes and demographics: Understanding the intention of international tourists in Malaysia to pay a premium for green hotels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lodging industry is one of the most crucial segments that consume a large amount of non-renewable resources. The extant literature shows that a large number of hotels are conducting green performances to offset the shift in customers’ buying behaviour from conventional hotels towards green hotels. Thus, an empirical investigation on hotel customers’ demographic as well as eco-friendly attitudes and intentions can help hotel operators better predict green buying behaviour of their potential/current customers. In this regard, the author conducts a series of multiple regression analyses in order to find any relationships between green attitudes and the intention to pay a premium for green hotels in Malaysia. A total of usable responses were used for data analysis. In general, findings reveal that except for seriousness of environmental problems (SEP), all other green attitudes, applied in this study, have a significant impact on the intention to pay a premium for green hotels. In addition, results of ANOVA indicate a variety of differences in intention to pay a premium for green hotels across different demographic characteristics. Finally, findings of this study not only affirm the Theory of Reasoned Action (TRA) by Ajzen (1975), but also provide managerial implications for hoteliers, marketers, and tourism ministries for better sustainability, segmentation, positioning, and resource allocation.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it