Consumers’ Perceptions of Green Marketing in the Hotel Industry
Bibliographic record
Abstract
This paper attempts to explore the evolution of green marketing in the hotel industry. This study is guided by three research questions: (1) to explore the level of understanding of local and international tourists on green marketing; (2) to gather tourists’ points of view if marketers have failed or they have never really tried to adopt the concept; and (3) to examine the level of awareness of tourists relating to green practices embraced by hotels. Purposive sampling using the Maximum Variation Sampling (MVS) technique is used to capture a wide range of perspectives in selecting the respondents. The discussion reveals that the concept has evolved hastily in developed countries while it is an emerging trend in developing countries like Malaysia. While some hotels have already begun to respond to environmental concerns in the country, the lack of promotions and advertisements have become part of the causes of knowledge non-appearance. This paper further concludes that green marketing concept should not just be adopted as a marketing tactic, but has to be considered with much greater dynamism, as it has ecological and social elements within it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".