An investigation on green attitudes and demographics: Understanding the intention of international tourists in Malaysia to pay a premium for green hotels
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".