Knowledge sharing, perceived risk and environmental information on energy saving behaviors of hotel guests
Bibliographic record
Abstract
Energy efficiency is one of the critical aspects, because the hotel sector has significant energy consumption and has an impact on environmental information. Energy efficiency helps reduce operational costs. By reducing excessive energy consumption, hotels can set a good example of responsible and sustainable business practices. It is important to understand the factors that affect energy efficiency. Some of the relevant factors are knowledge sharing, risk perception, and environmental information. This study aims to analyze the effect of knowledge sharing, risk perception, and environmental information factors on energy efficiency in the hospitality industry. The research method used in this study is a quantitative method with a survey approach. The number of samples used in this study were 176 hotel managers in Indonesia. Questionnaires were distributed to respondents using a Likert scale of 1 to 7. Data were analyzed using the Partial Least Square (PLS) method using SmartPLS software. The research results conclude that knowledge sharing, perceived risk, and environmental information have a significant influence on energy efficiency in the hospitality industry. Well-informed consumers tend to be more aware of the impact of their decisions and actions on environmental information, so they are more likely to choose hotels that adopt sustainable and energy efficient practices. In addition, the perception of risk also affects energy efficiency in the hospitality industry. Consumers who perceive risks associated with low sustainability or negative environmental information impacts are less likely to choose the hotel. Environmental information factors also play an important role in energy efficiency. Hotels that have an environment that supports and facilitates sustainable practices have the potential to achieve higher energy efficiency.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".