Decision-Making Behavior and Risk Perception of Chinese Female Wildlife Tourists
Bibliographic record
Abstract
Prior to the global pandemic, wildlife tourism was increasing rapidly globally but was in the early stages of development in China, where it faces great challenges and opportunities. Women comprise a substantial proportion of the market but their decision-making behavior and their perceptions of risk in wildlife tourism have not yet been explored. This paper explores relationships between risk perception and decision-making in tourism. A survey of female tourists was undertaken at non-captive and semi-captive wildlife sites in western China, as well as through internet website posting, resulting in 415 completed questionnaires. Quantitative methods were used to examine four sequential stages of decision-making in wildlife tourism: destination selection, trip itinerary, travel mode and security assurance, and entertainment consumption. Three dimensions of risk perception in wildlife tourism were identified: physical safety, personal comfort, and quality of experience. Decision-making behavior and risk perceptions are related. Perceived risks greatly impact tourists’ travel mode and security assurance decisions. The higher the perceived risk, the greater the likelihood of female tourists participating in decisions on destination selection, travel methods and other entertainment activities undertaken on their wildlife tourism trips. Concerns regarding personal comfort positively influence destination selection, the trip itinerary, and recreation and consumption decisions. Assurance of acquiring a quality experience influences entertainment consumption decisions. The study contributes to the understanding of risk, decision-making behavior and gender research, and confirms the practical importance of safety considerations at wildlife destinations.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".