Enhancing dine-out decisions: The role of precautionary measures and digital marketing in mitigating perceived risk at small eateries in Bali's tourist hubs ,
Bibliographic record
Abstract
Perceived risk is important in home-based restaurant businesses in tourist areas, especially in influencing tourists' views of risk when deciding to eat at a home-based restaurant. This perception of physical and psychological risks can influence tourists' intentions to dine out. This research aims to measure the magnitude of the influence of perceived risk on tourists' intention to dine out, as well as the role of precautionary measures and digital marketing strategies as moderating variables. Data was collected from 143 respondents who were consumers of home restaurants in several tourist areas in Bali, Indonesia. Data analysis used SmartPLS version 4.0 to test the relationship between research variables. The results show that perceived physical risk does not significantly impact tourists' intention to dine out. In contrast, perceived psychological risk has a strong negative influence on the perceived psychological risk the more likely tourists will dine on site. Health prevention measures implemented by restaurants (precautionary measures) and digital marketing strategies directly affect tourists' intention to dine out. Interesting digital content on social media related to food and beverage products has also been proven to increase this intention. However, precautionary measures and digital marketing as moderating variables do not significantly strengthen the relationship between perceived risk and intention to dine out. This research provides insight for home restaurant business owners to pay attention to consumer risk perceptions and utilize digital marketing strategies effectively. Creating interactive and informative content on social media is very important to increase tourist intent. This research also enriches the literature on consumer behavior and service science in tourism.
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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 | 0.006 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".